【转载】ResNet网络详解及Pytorch代码实现（超详细帮助你掌握ResNet原理及实现）

目录

1. ResNet解决了什么问题

2. ResNet原理及结构

2.1 BasicBlock结构

2.2 BottleNeck结构

2.3 ResNet结构

3. ResNet代码详解(Pytorch)

3.1 BasicBlock代码块

3.2 BottleNeck代码块

3.3 ResNet代码

博客中的ResNet内容来自何凯明大神在CVPR2016发表的文章《Deep Residual Learning for Image Recognition》（原文链接），ResNet代码部分来自Pytorch官方实现的ResNet源码，大家感兴趣的可以点击对应链接。
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1. ResNet解决了什么问题

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Resnet网络是为了解决深度网络中的退化问题，即网络层数越深时，在数据集上表现的性能却越差，如下图所示是论文中给出的深度网络退化现象。

从图中我们可以看到，作者在CIFAR-10数据集上测试了20层和56层的深度网络，结果就是56层的训练误差和测试误差反而比层数少的20层网络更大，这就是ResNet网络要解决的深度网络退化问题。
而采用ResNet网络之后，可以解决这种退化问题，如下图所示。

从图中作者在ImageNet数据集上的训练结果可以看出，在没有采用ResNet结构之前，如左图所示，34层网络plain-34的性能误差要大于18层网络plain-18的性能误差。而采用ResNet网络结构的34层网络结构ResNet-34性能误差小于18层网络ResNet。因此，采用ResNet网络结构的网络层数越深，则性能越佳。

2. ResNet原理及结构

接下来介绍ResNet网络原理及结构。
假设我们想要网络块学习到的映射为H(x)，而直接学习H(x)是很难学习到的。若我们学习另一个残差函数F(x) = H(x) - x可以很容易学习，因为此时网络块的训练目标是将F(x)逼近于0，而不是某一特定映射。因此，最后的映射H(x)就是将F(x)和x相加，H(x) = F(x) + x，如图所示。

因此，这个网络块的输出y为

由于相加必须保证x与F()是同维度的，因此可以写成通式如下式，Ws用于匹配维度。

文中提到两种维度匹配的方式（A）用zero-padding增加维度 (B）用1x1卷积增加维度。

下面给出论文中两种基础块结构，BasicBlock结构用于ResNet34及以下的网络，BotteNeck结构用于ResNet50及以上的网络。理解了这两个基础块，ResNet就是这些基础块的叠加了。

2.1 BasicBlock结构

BasicBlock结构图如图所示，

网络结构如图，两个3x3的卷积层，通道数都是64，然后就是注意那根跳线，也就是Shortcut Connections，将输入x加到输出。

2.2 BottleNeck结构

BasicBlock结构图如图所示，

网络结构如图，先是一个1x1的卷积层，然后一个3x3的卷积层，然后又是一个1x1的卷积层。注意的是这里的通道数是变化的，1x1卷积层的作用就是用于改变特征图的通数，使得可以和恒等映射x相叠加，另外这里的1x1卷积层改变维度的很重要的一点是可以降低网络参数量，这也是为什么更深层的网络采用BottleNeck而不是BasicBlock的原因。

2.3 ResNet结构

了解了上述BasicBlock基础块和BotteNeck结构后，ResNet结构就直接叠加搭建了。5种不同层数的ResNet结构图如图所示，

图中的每一层其实就是我们上面提到的BasicBlock或者BotteNeck结构。这里给出ResNet-34结构图如图所示，图中的虚线连接线是表示通道数不同，需要调整通道。

3. ResNet代码详解(Pytorch)
   这部分将给出Pytorch官方给出的ResNet源码，先分别给出BasicBlock和BottleNeck的代码块

3.1 BasicBlock代码块

#定义BasicBlock
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsaple=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups !=1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")

        #下面定义BasicBlock中的各个层
        self.conv1 = con3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True) #inplace为True表示进行原地操作，一般默认为False，表示新建一个变量存储操作
        self.conv2 = con3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.dowansample = downsaple
        self.stride = stride

    #定义前向传播函数将前面定义的各层连接起来
    def forward(self, x):
        identity = x #这是由于残差块需要保留原始输入

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

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

        if self.dowansample is not None: #这是为了保证原始输入与卷积后的输出层叠加时维度相同
            identity = self.dowansample(x)

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

        return out

3.2 BottleNeck代码块

#下面定义Bottleneck层（Resnet50以上用到的基础块）
class Bottleneck(nn.Module):
    expansion = 4 #Bottleneck层输出通道都是输入的4倍

    def __init__(self, inplanes, planes, stride=1, downnsaple=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        #定义Bottleneck中各层
        self.conv1 = con1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = con3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = con1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplanes=True)
        self.downsaple = downnsaple
        self.stride = stride

    #定义Bottleneck的前向传播
    def forward(self, x):
        identity = 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)
        out = self.relu(out)

        if self.downsaple is not None:
            identity = self.downsaple(x)

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

        return out

3.3 ResNet代码

这里给出的代码没有完全列出官方源码，需要完整源码的同学见前面代码链接。

import torch
import  torch.nn as nn
from .utils import  load_state_dict_from_url #这里是为了加载预训练模型需要的

#提供官方预训练模型的下载地址
model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}

#封装下3x3卷积层（卷积层的bias置为False是因为卷积层后面要加BN层，因此这里的bias不需要）
#Conv2d函数的具体参数说明可参见Pytorch官方手册https://pytorch-cn.readthedocs.io/zh/latest/package_references/torch-nn/#_1
def con3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)

#封装下1x1卷积层
def con1x1(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kenerl_size=1, stride=stride, bias=False)

#定义BasicBlock
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsaple=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups !=1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")

        #下面定义BasicBlock中的各个层
        self.conv1 = con3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True) #inplace为True表示进行原地操作，一般默认为False，表示新建一个变量存储操作
        self.conv2 = con3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.dowansample = downsaple
        self.stride = stride

    #定义前向传播函数将前面定义的各层连接起来
    def forward(self, x):
        identity = x #这是由于残差块需要保留原始输入

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

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

        if self.dowansample is not None: #这是为了保证原始输入与卷积后的输出层叠加时维度相同
            identity = self.dowansample(x)

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

        return out

#下面定义Bottleneck层（Resnet50以上用到的基础块）
class Bottleneck(nn.Module):
    expansion = 4 #Bottleneck层输出通道都是输入的4倍

    def __init__(self, inplanes, planes, stride=1, downnsaple=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        #定义Bottleneck中各层
        self.conv1 = con1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = con3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = con1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplanes=True)
        self.downsaple = downnsaple
        self.stride = stride

    #定义Bottleneck的前向传播
    def forward(self, x):
        identity = 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)
        out = self.relu(out)

        if self.downsaple is not None:
            identity = self.downsaple(x)

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

        return out

#下面进入正题，定义ResNet类
class ResNet(nn.Module):
    def __init__(self, block, layer, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(self.inplanes)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layer[0])
        self.layer2 = self._make_layer(block, 128, layer[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layer[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layer[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Linear(512 * block.expanion, num_classes)

        #定义初始化方式
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_nomal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)
    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsaple = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expanion:
            downsaple = nn.Sequential(
                con1x1(self.inplanes, planes * block.expanion, stride),
                norm_layer(planes * block.expanion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsaple, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expanion
        for _ in range(1, block):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilate=self.dilation,
                                norm_layer=norm_layer))

        return  nn.Sequential(*layers)

    def _forward_impl(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 = torch.flatten(x, 1)
        x = self.fc(x)

        return x

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

    def _resnet(arch, block, layers, pretrained, progress, **kwargs):
        model = ResNet(block, layers, **kwargs)
        if pretrained:
            state_dict = load_state_dict_from_url(model_urls[arch],
                                                  progress=progress)
            model.load_state_dict(state_dict)
        return model

    def resnet34(pretrained=False, progress=True, **kwargs):
        return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
                       **kwargs)

    def resnet101(pretrained=False, progress=True, **kwargs):
        return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
                       **kwargs)

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版权声明：本文为优快云博主「武晨」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。
原文链接：https://blog.youkuaiyun.com/wuzhuoxi7116/article/details/106309943/