目录
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》，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。
1. ResNet解决了什么问题
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)