1
ResNet是2015年就提出的网络结构，中文名字叫作深度残差网络，主要作用是图像分类。现在在图像分割、目标检测等领域都有很广泛的运用.
2
随着硬件的不断升级，我们可以使得原来很浅的网络不断的加深，但是这种做法随之而来就出现了这样的一个问题深层训练的效果反而不如浅层网络，也就是网络出现了退化。这个问题很大程度上归结为网络层数过深，梯度下降优化loss变得困难。
作者为了解决上述问题，提出了这样一个结构：
3
如上图所示，作者添加了一个identity结构，也就是将输入x和卷积后的输出F(x)相加。如果我们要学习的目标函数是H(x)的话。
当需要拟合的函数H(x)很复杂的时候，H(x)和F(x)=H(x)-x近似等价，那么我们之前的学习目标H(x)就可以变成F(x)+x。为什么要这样的的做替换？因为F(x)的优化会比H(x)简单。为什么呢？这实际上是源自**残差表示法（Residual Representations）**的思想。
在图像识别中，VLAD是一种由残差向量关于字典的编码表示，而Fisher Vector可以被定义为VLAD的概率版本，它们都是图像检索和分类的强大的浅层表示。对于向量化，编码残差向量比编码原始向量更有效。
在低层次的视觉和计算机图形学中，为了解偏微分方程，一般使用的多网格法。就是将系统重新设计成多个尺度下的子问题，每个子问题负责一个较粗的和更细的尺度之间的残差解。多网格的另一种选择是分层基础的预处理，它依赖于在两个尺度之间表示残差向量的变量。研究表明，这些（转化成多个不同尺度的子问题，求残差解的）解决方案的收敛速度远远快于那些不知道残差的标准解决方案。
4 residual block
整个网络最重要的部分就是上面的identity，我们称这个结构整体是residual block。现在我们要考虑的问题就是上述结构中，卷积层如何存放？激活函数如何存放？。。。这些问题。为此，作者对于不同方式做了比较
实验结果发现，最后一种方式的residual block效果最好。
这应该是早期residual block的模样，而由于技术的不断发展，后来出现了batch normalization这样的技术，residual block也做了相对应的调整，变成了现在的模样。
对于resnet18和resnet34
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.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
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)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out对于resnet50、resnet101和resnet152
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 out5 网络结构
整体的resnet系列网络如下：
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)#7x7,64
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)#maxpool
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)#average pool
self.fc = nn.Linear(512 * block.expansion, 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, math.sqrt(2. / n))
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)
x = self.fc(x)
return x代码和网络结构都是对应的，很容易理解。
关于网络结构我这里只举两个例子resnet18以及resnet50
def resnet18(pretrained=False, **kwargs):
"""Constructs a ResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)#对应图中的2x2x2x2
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
return model
def resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)#对应图中的3x4x6x3
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
return model
