对Pytorch中ResNet源码的探讨

赶着放假,实验室人少了,不过还是得抓紧学习啊,毕竟对象找不到,那工作就是第二件大事啦

ResNet的重要性应该是不言而喻:随着网络深度的增加,网络开始出现退化现象,即深层网络的性能还不及浅层网络(注意:这既不是梯度消失/爆炸,也不是过拟合),鉴于此,文章设计了一种使用shortcut / skip connection 的残差结构使网络达到很深的层次,同时提升了性能。

复习就到此了,接下来一起探讨源码解析吧!

一、两个基本结构:BasicBlock & BottleNeck

由于最近在看Triplet Loss代码,所以代码都是Triplet里的,不过都一样啊!

首先,咱们要知道,resnet中的卷积核不是1x1就是3x3,所以咱们得首先定义这两个卷积核的操作吧:

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)  


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)

这就完事了!不过咱们要理清一个事,定义在class之外的def是fuction,相当于把这家伙给外包了;定义在class里面的def叫method.

关键点来了,你们的BasicBlock & BolleNeck是不是都是这样写的?

BasicBlock

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.relu = nn.ReLU(inplace=True)
    self.conv2 = conv3x3(planes, 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

看到代码,咱们来逐行解析。expansion=1——有两个意思,首先expansion是区分BasicBlock和BottleBlock的关键;其次expansion=1意味着输出维度与输入维度是一致的;def _ _init_ _()——该行代码中inplanes表示输入通道(input channel),pleans表示输出通道(output channel);self.conv1 = 3x3——这一步中,stride=2;self.conv2 = conv3x3——这一步中,stride默认等于1;

为什么要这样做呢?根据公式:  W=\frac{W-F+2P}{S}可知,卷积后的特征图的尺寸跟步长有很大关系,所以这么做无非是对图像进行维度变化。(padding默认为1)

self.downsample = downsample——默认情况downsample=None,表示不做下采样,但当输入维度x和output的通道数不一样,则需要下采样来匹配维度,这样才能相加啊。

BottleNeck

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

看到expansion=4了没?这印证了上诉expansion的第一点,此外,expansion=4还表示输出维度是输入维度的4倍(没忘吧?)

附上BasicBlock & BottleNeck 网络结构:

对Pytorch中ResNet源码的探讨_第1张图片

小结一下:

1.BasicBlock的expansion为1,即输入和输出的通道数是一致的。而Bottleneck的expansion为4,即输出通道数是输入通道数的4倍

2.不管是BasicBlock还是Bottleneck,最后都会做一个判断是否需要给x做downsample,因为必须让输入维度与输出维度一致,才能相加。

二、ResNet本体结构

在解析ResNet结构之前,我们先要对ResNet有个基本的了解。源码中的ResNet类可以根据输入参数的不同,变成resnet18,34,50,101等。而我们最常用的是ResNet-50,其BottleNeck结构个数为3,4,6,3(论文链接:https://arxiv.org/abs/1512.03385)

首先,Pytorch是怎么调用ResNet的呢?

class Model(nn.Module):
  def __init__(self, last_conv_stride=2):
    super(Model, self).__init__()
    self.base = resnet50(pretrained=True, last_conv_stride=last_conv_stride)

可以看到,resnet50(pretrained=True)可以看出,调用的是resnet50网络。进入到ResNet-50,

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)
  if pretrained:
    model.load_state_dict(remove_fc(model_zoo.load_url(model_urls['resnet50'])))
  return model

可以看到,ResNet-50调用了ResNet本体的参数,即这行代码:model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)

第一个参数BottleNeck没什么好说的,调用我们的“瓶颈结构”;第二个参数【3,4,6,3】,是BottleNeck结构个数,怎么理解呢,看resnet源代码:

class ResNet(nn.Module):

  def __init__(self, block, layers, last_conv_stride=2): 
    self.inplanes = 64  
    super(ResNet, self).__init__()
    self.conv1 = nn.Conv2d(3, 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=last_conv_stride) 
    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)

    return x

怎么理解这段代码呢?咱们要学会从后往前看。从ResNet的forward代码来看,它是先经过conv1()bn1relumaxpool()。如果你对ResNet网络结构清楚,可以知道,无论是resnet18,resnet34,resnet50还是resnet101等等的resnet一开始都必须经过这几层。这是静态的。而后面的layer1,layer2,layer3,layer4是动态的以区别是resnet18,resnet34,resnet50还是resnet101等等

接下来,我们开始逐行解析代码,为了方便,我们直接在代码上注释(请忽略我的chinglish)

class ResNet(nn.Module):

  def __init__(self, block, layers, last_conv_stride=2):  # block:i.e.BasicBlock or Bottleneck;  # layers:corresponds to [3,4,6,3] in resnet50

    self.inplanes = 64  # inplanes :'inplanes' represents the number of input channel
    super(ResNet, self).__init__()
    self.conv1 = nn.Conv2d(3, 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])  # 64:make_layer(planes=64);  # layers[0]:3 ge Bottlenecks
    self.layer2 = self._make_layer(block, 128, layers[1], stride=2)  # 128:make_layer(planes=128);  # layers[1]:4 ge Bottlenecks
    self.layer3 = self._make_layer(block, 256, layers[2], stride=2)  # 256:make_layer(planes=256);  # layers[2]:6 ge Bottlenecks
    self.layer4 = self._make_layer(
      block, 512, layers[3], stride=last_conv_stride)  # 512:make_layer(planes=512);  # layers[3]:3 ge Bottlenecks

    for m in self.modules():  # parameters initialization
      if isinstance(m, nn.Conv2d):  # isinstance():judge the type of paramenter 'm'
        n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels  # number of points in convolution kernel
        m.weight.data.normal_(0, math.sqrt(2. / n))  # means:0  std: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):  # Note: the 'planes' here is not the number of output channels,but the number of referance channel(ji zhun tong dao)
    downsample = None                                      # block:means choice Bottleneck or BasicBlock  # blocks:means [3,4,6,3]
    if stride != 1 or self.inplanes != planes * block.expansion:  # planes * expansion = the number of output channels
      downsample = nn.Sequential(
        nn.Conv2d(self.inplanes, planes * block.expansion,
                  kernel_size=1, stride=stride, bias=False),
        nn.BatchNorm2d(planes * block.expansion),
      )  # downsample:connects the different dimension between input and output dimension in Bottleneck

    layers = []
    layers.append(block(self.inplanes, planes, stride, downsample))  # stride=2,it will reduce the feature map size by a factor of two
    self.inplanes = planes * block.expansion
    for i in range(1, blocks):
      layers.append(block(self.inplanes, planes))  # in this step,the stride=1 by default,so that no reduction the feature map size

    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)

    return x

_make_layer方法中比较重要的两行代码是:

1、layers.append(block(self.inplanes, planes, stride, downsample)),该部分是将每个blocks的第一个residual结构保存在layers列表中。

2、 for i in range(1, blocks): layers.append(block(self.inplanes, planes)),该部分是将每个blocks的剩下residual 结构保存在layers列表中,这样就完成了一个blocks的构造。
 

参考博客:https://blog.csdn.net/u014453898/article/details/97115891https://blog.csdn.net/u014453898/article/details/97115891https://blog.csdn.net/u014453898/article/details/97115891

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