ResNet及其变体结构梳理与总结

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2021-12-14 21:00

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【导读】2020年,在各大CV顶会上又出现了许多基于ResNet改进的工作,比如:Res2Net,ResNeSt,IResNet,SCNet等等。为了更好的了解ResNet整个体系脉络的发展,我们特此对ResNet系列重新梳理,并制作了一个ResNet专题,希望能帮助大家对ResNet体系有一个更深的理解。本篇文章我们将主要讲解ResNet、preResNet、ResNext以及它们的代码实现。

ResNet


  • 论文链接:https://arxiv.org/abs/1512.03385

  • 代码地址:https://github.com/KaimingHe/deep-residual-networks

  • pytorch版:https://github.com/Cadene/pretrained-models.pytorch



Motivation和创新点

深度学习的发展从LeNet到AlexNet,再到VGGNet和GoogLeNet,网络的深度在不断加深,经验表明,网络深度有着至关重要的影响,层数深的网络可以提取出图片的低层、中层和高层特征。但当网络足够深时,仅仅在后面继续堆叠更多层会带来很多问题:第一个问题就是梯度爆炸 / 消失(vanishing / exploding gradients),backprop无法把有效地把梯度更新到前面的网络层,导致前面的层参数无法更新。第二个问题就是退化(degradation)问题,即当网络层数堆叠过多会导致优化困难、且训练误差和预测误差更大了,注意这里误差更大并不是由过拟合导致的。

ResNet旨在解决网络加深后训练难度增大的现象。其提出了residual模块,包含两个3×3卷积和一个shortcut connection。shortcut connection可以有效缓解反向传播时由于深度过深导致的梯度消失现象,这使得网络加深之后性能不会变差。短路连接是深度学习又一重要思想,除计算机视觉外,短路连接也被用到了机器翻译、语音识别/合成领域。此外,具有短路连接的ResNet可以看作是许多不同深度而共享参数的网络的集成,网络数目随层数指数增加。值得注意的是:在此之前已有研究者使用跨层连接对响应和梯度中心化(center)处理;inception结构本质也是跨层连接;highway网络也使用到了跨层连接


ResNet的关键点是:

  • 利用残差结构让网络能够更深、收敛速度更快、优化更容易,同时参数相对之前的模型更少、复杂度更低

  • ResNet大量使用了批量归一层,而不是Dropout。

  • 对于很深的网络(超过50层),ResNet使用了更高效的瓶颈(bottleneck)结构极大程度上降低了参数计算量。



ResNet的残差结构

为了解决退化问题,我们引入了一个新的深度残差学习block,在这里,对于一个堆积层结构(几层堆积而成)当输入为时,其学习到的特征记为,现在我们希望其可以学习到残差 ,这样其实原始的学习特征是 。之所以这样是因为残差学习相比原始特征直接学习更容易。当残差为0时,此时堆积层仅仅做了恒等映射,至少网络性能不会下降,实际上残差不会为0,这也会使得堆积层在输入特征基础上学习到新的特征,从而拥有更好的性能。


本质也就是不改变目标函数 ,将网络结构拆成两个分支,一个分支是残差映射,一个分支是恒等映射,于是网络仅需学习残差映射即可。对于上述残差单元,我们可以从数学的角度来分析一下,首先上述结构可表示为:

其中分别表示的是第个残差单元的输入和输出,注意每个残差单元一般包含多层结构。是残差函数,表示学习到的残差,而表示恒等映射,是ReLU激活函数。基于上式,我们求得从浅层到深层的学习特征为:

利用链式规则,可以求得反向过程的梯度:

式子的第一个因子 表示的损失函数到达的梯度,小括号中的1表明短路机制可以无损地传播梯度,而另外一项残差梯度则需要经过带有weights的层,梯度不是直接传递过来的。残差梯度不会那么巧全为-1,而且就算其比较小,有1的存在也不会导致梯度消失。所以残差学习会更容易。要注意上面的推导并不是严格的证明。

残差结构为什么有效?

  1. 自适应深度:网络退化问题就体现了多层网络难以拟合恒等映射这种情况,也就是说难以拟合,但使用了残差结构之后,拟合恒等映射变得很容易,直接把网络参数全学习到为0,只留下那个恒等映射的跨层连接即可。于是当网络不需要这么深时,中间的恒等映射就可以多一点,反之就可以少一点。(当然网络中出现某些层仅仅拟合恒等映射的可能性很小,但根据下面的第二点也有其用武之地;另外关于为什么多层网络难以拟合恒等映射,这涉及到信号与系统的知识见:https://www.zhihu.com/question/293243905/answer/484708047)

  2. 差分放大器:假设最优更接近恒等映射,那么网络更容易发现除恒等映射之外微小的波动

  3. 模型集成:整个ResNet类似于多个网络的集成,原因是删除ResNet的部分网络结点不影响整个网络的性能,但VGGNet会崩溃,具体可以看这篇NIPS论文:Residual Networks Behave Like Ensembles of Relatively Shallow Networks

  4. 缓解梯度消失:针对一个残差结构对输入求导就可以知道,由于跨层连接的存在,总梯度在的导数基础上还会加1

下面给出一个直观理解图:

如上图所示,左边来了一辆装满了“梯度”商品的货车,来领商品的客人一般都要排队一个个拿才可以,如果排队的人太多,后面的人就没有了。于是这时候派了一个人走了“快捷通道”,到货车上领了一部分“梯度”,直接送给后面的人,这样后面排队的客人就能拿到更多的“梯度”。


Bottleneck的好处-两种残差单元

我们来计算一下1*1卷积的计算量优势:首先看上图右边的bottleneck结构,对于256维的输入特征,参数数目:1x1x256x64+3x3x64x64+1x1x64x256=69632,如果同样的输入输出维度但不使用1x1卷积,而使用两个3x3卷积的话,参数数目为(3x3x256x256)x2=1179648。简单计算下就知道了,使用了1x1卷积的bottleneck将计算量简化为原有的5.9%,收益超高。


详细见:【基础积累】1x1卷积到底有哪些用处?


ResNet网络设计结构:


基于Pytorch的ResNet代码实现

def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes, grayscale):
        self.inplanes = 64
        if grayscale:
            in_dim = 1
        else:
            in_dim = 3
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(in_dim, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1, padding=2)
        #self.fc = nn.Linear(2048 * block.expansion, num_classes)
        self.fc = nn.Linear(2048, num_classes)

        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, (2. / n)**.5)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        #x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        logits = self.fc(x)
        probas = F.softmax(logits, dim=1)
        return logits, probas



def resnet101(num_classes, grayscale):
    """Constructs a ResNet-101 model."""
    model = ResNet(block=Bottleneck,
                   layers=[3, 4, 23, 3],
                   num_classes=NUM_CLASSES,
                   grayscale=grayscale)
    return model


PreResNet

  • 论文链接:https://arxiv.org/abs/1603.05027

  • 代码地址:https://github.com/KaimingHe/resnet-1k-layers.


论文主要思想和改进点
本文分析了残差模块背后的传播公式,重点是创建一个信息传播的“直接”路径——不仅在残差单元内,而且要通过整个网络。一系列实验表明在使用恒等映射作为跳跃连接和在BN后添加激活函数的方式,前向和后向传播信号可以直接从一个块传播到任何其他块。一系列的消融实验证明了这些恒等映射的重要性。这促使我们提出了一个新的残差单位,它使的训练更容易,并改进了泛化效果。我们把它叫做preResNet,preResNet主要是调整了residual模块中各层的顺序。preResNet比较了ReLU和BN的摆放位置不同,比较两者的效果。相比经典residual模块(a),(b)将BN共享会更加影响信息的短路传播,使网络更难训练、性能也更差;(c)直接将ReLU移到BN后会使该分支的输出始终非负,使网络表示能力下降;(d)将ReLU提前解决了(e)的非负问题,但ReLU无法享受BN的效果;(e)将ReLU和BN都提前解决了(d)的问题。preResNet的短路连接(e)能更加直接的传递信息,进而取得了比ResNet更好的性能。

基于Pytorch的ResNet代码实现
import torch.nn as nn


__all__ = ['preresnet20', 'preresnet32', 'preresnet44',
           'preresnet56', 'preresnet110', 'preresnet1202']


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.bn_1 = nn.BatchNorm2d(inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.conv_1 = conv3x3(inplanes, planes, stride)
        self.bn_2 = nn.BatchNorm2d(planes)
        self.conv_2 = conv3x3(planes, planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.bn_1(x)
        out = self.relu(out)
        out = self.conv_1(out)

        out = self.bn_2(out)
        out = self.relu(out)
        out = self.conv_2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.bn_1 = nn.BatchNorm2d(inplanes)
        self.conv_1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn_2 = nn.BatchNorm2d(planes)
        self.conv_2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                padding=1, bias=False)
        self.bn_3 = nn.BatchNorm2d(planes)
        self.conv_3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.bn_1(x)
        out = self.relu(out)
        out = self.conv_1(out)

        out = self.bn_2(out)
        out = self.relu(out)
        out = self.conv_2(out)

        out = self.bn_3(out)
        out = self.relu(out)
        out = self.conv_3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual

        return out


class PreResNet(nn.Module):

    def __init__(self, depth, num_classes=1000, block_name='BasicBlock'):
        super(PreResNet, self).__init__()
        # Model type specifies number of layers for CIFAR-10 model
        if block_name.lower() == 'basicblock':
            assert (
                depth - 2) % 6 == 0, "When use basicblock, depth should be 6n+2, e.g. 20, 32, 44, 56, 110, 1202"
            n = (depth - 2) // 6
            block = BasicBlock
        elif block_name.lower() == 'bottleneck':
            assert (
                depth - 2) % 9 == 0, "When use bottleneck, depth should be 9n+2 e.g. 20, 29, 47, 56, 110, 1199"
            n = (depth - 2) // 9
            block = Bottleneck
        else:
            raise ValueError('block_name shoule be Basicblock or Bottleneck')

        self.inplanes = 16
        self.conv_1 = nn.Conv2d(3, 16, kernel_size=3, padding=1,
                                bias=False)
        self.layer1 = self._make_layer(block, 16, n)
        self.layer2 = self._make_layer(block, 32, n, stride=2)
        self.layer3 = self._make_layer(block, 64, n, stride=2)
        self.bn = nn.BatchNorm2d(64 * block.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.avgpool = nn.AvgPool2d(8)
        self.fc = nn.Linear(64 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight.data)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False))

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv_1(x) # 32x32

        x = self.layer1(x) # 32x32
        x = self.layer2(x) # 16x16
        x = self.layer3(x) # 8x8
        x = self.bn(x)
        x = self.relu(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def preresnet20(num_classes):
    return PreResNet(depth=20, num_classes=num_classes)


def preresnet32(num_classes):
    return PreResNet(depth=32, num_classes=num_classes)


def preresnet44(num_classes):
    return PreResNet(depth=44, num_classes=num_classes)


def preresnet56(num_classes):
    return PreResNet(depth=56, num_classes=num_classes)


def preresnet110(num_classes):
    return PreResNet(depth=110, num_classes=num_classes)


def preresnet1202(num_classes):
    return PreResNet(depth=1202, num_classes=num_classes)


ResNetXt


  • 论文链接:https://arxiv.org/abs/1611.05431


论文思想和主要改进点
传统的方法通常是靠加深或加宽网络来提升性能,但计算开销也会随之增加。ResNeXt旨在不改变模型复杂度的情况下提升性能。受精简而高效的Inception模块启发,在这篇文章中,我们提出了一个简单的、高度模块化的图像分类网络架构。我们的网络是通过聚合不同的模块构建起来的,它借鉴了Inception的“分割-变换-聚合”策略,却用相同的拓扑结构来组建多分支结构这种多分支结构的策略衍生出了一个新的维度,我们称之为“基数”,它也是网络结构中除了深度和宽度之外,一个重要的影响因素。作为ResNet的一个高能进化版ResNeXt在宽度和深度之外,通过引入了“基数 (Cardinality) ”的概念。在网络不加深不加宽的情况下,增加基数便可以提高模型效果和提升准确率,还能减少超参数的数量。

ResNeXt的关键点是:

  • 沿用ResNet的短路连接,并且重复堆叠相同的模块组合。

  • ResNeXt将ResNet中非跳跃连接的那一分支变为多个分支。

  • 多分支分别处理。

  • 使用1×1卷积降低计算量。其综合了ResNet和Inception的优点。

  • ResNeXt与Inception最本质的差别,其实是Block内每个分支的拓扑结构,Inception为了提高表达能力/结合不同感受野,每个分支使用了不同的拓扑结构。而ResNeXt则使用了同一拓扑的分支,即ResNeXt的分支是同构的!

因为ResNeXt是同构的,因此继承了VGG/ResNet的精神衣钵:维持网络拓扑结构不变。主要体现在两点:

  • 特征图大小相同,则涉及的结构超参数相同

  • 每当空间分辨率/2(降采样),则卷积核的宽度*2


神经元连接

聚合变换

ResNeXt最终输出模块公式:


此外,ResNeXt巧妙地利用分组卷积进行实现。ResNeXt发现,增加分支数是比加深或加宽更有效地提升网络性能的方式。ResNeXt的命名旨在说明这是下一代(next)的ResNet。


如果一个ResNeXt Block中只有两层conv,前后都可等效成一个大的conv层

上图a的解读:

ResNeXt最核心的地方只存在于被最上最下两层卷积夹着的,中间的部分

  1. 因为第一个分开的conv其实都接受了一样的输入,各分支又有着相同的拓扑结构。类比乘法结合律,这其实就是把一个conv的输出拆开了分掉。相同输入,不同输出)

  2. 而最后一个conv又只对同一个输出负责,因此就可以并起来用一个conv处理。不同输入,相同输出

  3. 唯一一个输入和输出都不同的,就是中间的3*3conv了。它们的输入,参数,负责的输出都不同,无法合并,因此也相互独立。这才是模型的关键所在。最终模型可以被等效为下图所示的最终形态:




ResNeXt的网络结构设计:


基于Pytorch的ResNet代码实现

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


__all__ = ['resnext29_8x64d', 'resnext29_16x64d']


class Bottleneck(nn.Module):

    def __init__(
            self,
            in_channels,
            out_channels,
            stride,
            cardinality,
            base_width,
            expansion)
:


        super(Bottleneck, self).__init__()
        width_ratio = out_channels / (expansion * 64.)
        D = cardinality * int(base_width * width_ratio)

        self.relu = nn.ReLU(inplace=True)

        self.conv_reduce = nn.Conv2d(
            in_channels, D, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn_reduce = nn.BatchNorm2d(D)
        self.conv_conv = nn.Conv2d(
            D,
            D,
            kernel_size=3,
            stride=stride,
            padding=1,
            groups=cardinality,
            bias=False)
        self.bn = nn.BatchNorm2d(D)
        self.conv_expand = nn.Conv2d(
            D, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn_expand = nn.BatchNorm2d(out_channels)

        self.shortcut = nn.Sequential()
        if in_channels != out_channels:
            self.shortcut.add_module(
                'shortcut_conv',
                nn.Conv2d(
                    in_channels,
                    out_channels,
                    kernel_size=1,
                    stride=stride,
                    padding=0,
                    bias=False))
            self.shortcut.add_module(
                'shortcut_bn', nn.BatchNorm2d(out_channels))

    def forward(self, x):
        out = self.conv_reduce.forward(x)
        out = self.relu(self.bn_reduce.forward(out))
        out = self.conv_conv.forward(out)
        out = self.relu(self.bn.forward(out))
        out = self.conv_expand.forward(out)
        out = self.bn_expand.forward(out)
        residual = self.shortcut.forward(x)
        return self.relu(residual + out)


class ResNeXt(nn.Module):
    """
    ResNext optimized for the Cifar dataset, as specified in
    https://arxiv.org/pdf/1611.05431.pdf
    """


    def __init__(
            self,
            cardinality,
            depth,
            num_classes,
            base_width,
            expansion=4)
:

        """ Constructor
        Args:
            cardinality: number of convolution groups.
            depth: number of layers.
            num_classes: number of classes
            base_width: base number of channels in each group.
            expansion: factor to adjust the channel dimensionality
        """

        super(ResNeXt, self).__init__()
        self.cardinality = cardinality
        self.depth = depth
        self.block_depth = (self.depth - 2) // 9
        self.base_width = base_width
        self.expansion = expansion
        self.num_classes = num_classes
        self.output_size = 64
        self.stages = [64, 64 * self.expansion, 128 *
                       self.expansion, 256 * self.expansion]

        self.conv_1_3x3 = nn.Conv2d(3, 64, 3, 1, 1, bias=False)
        self.bn_1 = nn.BatchNorm2d(64)
        self.stage_1 = self.block('stage_1', self.stages[0], self.stages[1], 1)
        self.stage_2 = self.block('stage_2', self.stages[1], self.stages[2], 2)
        self.stage_3 = self.block('stage_3', self.stages[2], self.stages[3], 2)
        self.fc = nn.Linear(self.stages[3], num_classes)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight.data)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def block(self, name, in_channels, out_channels, pool_stride=2):
        block = nn.Sequential()
        for bottleneck in range(self.block_depth):
            name_ = '%s_bottleneck_%d' % (name, bottleneck)
            if bottleneck == 0:
                block.add_module(
                    name_,
                    Bottleneck(
                        in_channels,
                        out_channels,
                        pool_stride,
                        self.cardinality,
                        self.base_width,
                        self.expansion))
            else:
                block.add_module(
                    name_,
                    Bottleneck(
                        out_channels,
                        out_channels,
                        1,
                        self.cardinality,
                        self.base_width,
                        self.expansion))
        return block

    def forward(self, x):
        x = self.conv_1_3x3.forward(x)
        x = F.relu(self.bn_1.forward(x), inplace=True)
        x = self.stage_1.forward(x)
        x = self.stage_2.forward(x)
        x = self.stage_3.forward(x)
        x = F.avg_pool2d(x, 8, 1)
        x = x.view(-1, self.stages[3])
        return self.fc(x)


def resnext29_8x64d(num_classes):
    return ResNeXt(
        cardinality=8,
        depth=29,
        num_classes=num_classes,
        base_width=64)


def resnext29_16x64d(num_classes):
    return ResNeXt(
        cardinality=16,
        depth=29,
        num_classes=num_classes,
        base_width=64)

参考连接

  1. https://zhuanlan.zhihu.com/p/54289848

  2. https://zhuanlan.zhihu.com/p/28124810

  3. https://zhuanlan.zhihu.com/p/31727402

  4. https://zhuanlan.zhihu.com/p/56961832

  5. https://zhuanlan.zhihu.com/p/54072011

  6. https://github.com/BIGBALLON/CIFAR-ZOO

  7. https://zhuanlan.zhihu.com/p/78019001


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