pytorch中修改预训练模型_修改se resnet50模型后预训练模型还要改吗

作者：花生_TL007 | 2024-03-10 08:32:01

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修改se resnet50模型后预训练模型还要改吗

原文链接：https://blog.csdn.net/whut_ldz/article/details/78845947

一、pytorch中的pre-train模型

卷积神经网络的训练是耗时的，很多场合不可能每次都从随机初始化参数开始训练网络。

pytorch中自带几种常用的深度学习网络预训练模型，如VGG、ResNet等。往往为了加快学习的进度，在训练的初期我们直接加载pre-train模型中预先训练好的参数，model的加载如下所示：


  
  
    
    
     
     
    
    
    
    
     
     
      
      import torchvision.models 
      
      as models
     
     
    
    

    
    
     
     
    
    
    
    
     
      
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      #resnet
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      model = models.ResNet(pretrained=
      
      True)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      model = models.resnet18(pretrained=
      
      True)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      model = models.resnet34(pretrained=
      
      True)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      model = models.resnet50(pretrained=
      
      True)
     
     
    
    

    
    
     
     
    
    
    
    
     
      
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      #vgg
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      model = models.VGG(pretrained=
      
      True)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      model = models.vgg11(pretrained=
      
      True)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      model = models.vgg16(pretrained=
      
      True)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      model = models.vgg16_bn(pretrained=
      
      True)
     
     
    
    
  
  
1

二、预训练模型的修改

1.参数修改

对于简单的参数修改，这里以resnet预训练模型举例，resnet源代码在Github点击打开链接。

resnet网络最后一层分类层fc是对1000种类型进行划分，对于自己的数据集，如果只有9类，修改的代码如下：


  
  
    
    
     
     
    
    
    
    
     
     
      
      # coding=UTF-8
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      import torchvision.models 
      
      as models
     
     
    
    

    
    
     
     
    
    
    
    
     
      
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      #调用模型
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      model = models.resnet50(pretrained=
      
      True)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      #提取fc层中固定的参数
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      fc_features = model.fc.in_features
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      #修改类别为9
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      model.fc = nn.Linear(fc_features, 
      
      9)
     
     
    
    
  
  
1

2.增减卷积层

前一种方法只适用于简单的参数修改，有的时候我们往往要修改网络中的层次结构，这时只能用参数覆盖的方法，即自己先定义一个类似的网络，再将预训练中的参数提取到自己的网络中来。这里以resnet预训练模型举例。


  
  
    
    
     
     
    
    
    
    
     
     
      
      # coding=UTF-8
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      import torchvision.models 
      
      as models
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      import torch
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      import torch.nn 
      
      as nn
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      import math
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      import torch.utils.model_zoo 
      
      as model_zoo
     
     
    
    

    
    
     
     
    
    
    
    
     
      
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      class CNN(nn.Module):
     
     
    
    

    
    
     
     
    
    
    
    
     
      
     
     
    
    

    
    
     
     
    
    
    
    
     
         
      
      def __init__(self, block, layers, num_classes=9):
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
              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=
      
      2)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
              self.avgpool = nn.AvgPool2d(
      
      7, stride=
      
      1)
     
     
    
    

    
    
     
     
    
    
    
    
     
             
      
      #新增一个反卷积层
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
              self.convtranspose1 = nn.ConvTranspose2d(
      
      2048, 
      
      2048, kernel_size=
      
      3, stride=
      
      1, padding=
      
      1, output_padding=
      
      0, groups=
      
      1, bias=
      
      False, dilation=
      
      1)
     
     
    
    

    
    
     
     
    
    
    
    
     
             
      
      #新增一个最大池化层
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
              self.maxpool2 = nn.MaxPool2d(kernel_size=
      
      3, stride=
      
      1, padding=
      
      1)
     
     
    
    

    
    
     
     
    
    
    
    
     
             
      
      #去掉原来的fc层，新增一个fclass层
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
              self.fclass = 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, 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)
     
     
    
    

    
    
     
     
    
    
    
    
     
             
      
      #新加层的forward
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
              x = x.view(x.size(
      
      0), 
      
      -1)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
              x = self.convtranspose1(x)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
              x = self.maxpool2(x)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
              x = x.view(x.size(
      
      0), 
      
      -1)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
              x = self.fclass(x)
     
     
    
    

    
    
     
     
    
    
    
    
     
      
     
     
    
    

    
    
     
     
    
    
    
    
     
             
      
      return x
     
     
    
    

    
    
     
     
    
    
    
    
     
      
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      #加载model
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      resnet50 = models.resnet50(pretrained=
      
      True)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      cnn = CNN(Bottleneck, [
      
      3, 
      
      4, 
      
      6, 
      
      3])
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      #读取参数
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      pretrained_dict = resnet50.state_dict()
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      model_dict = cnn.state_dict()
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      # 将pretrained_dict里不属于model_dict的键剔除掉
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      pretrained_dict =  {k: v 
      
      for k, v 
      
      in pretrained_dict.items() 
      
      if k 
      
      in model_dict}
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      # 更新现有的model_dict
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      model_dict.update(pretrained_dict)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      # 加载我们真正需要的state_dict
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      cnn.load_state_dict(model_dict)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      # print(resnet50)
     
     
    
    

    
    
     
     
    
    
    
    
     
     
      
      print(cnn)
     
     
    
    
  
  
1

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