pytorch中的pre-train函数模型引用及修改(增减网络层,修改某层参数等)

https://blog.csdn.net/whut_ldz/articl

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:

 

 
  1. import torchvision.models as models

  2.  
  3. #resnet

  4. model = models.ResNet(pretrained=True)

  5. model = models.resnet18(pretrained=True)

  6. model = models.resnet34(pretrained=True)

  7. model = models.resnet50(pretrained=True)

  8.  
  9. #vgg

  10. model = models.VGG(pretrained=True)

  11. model = models.vgg11(pretrained=True)

  12. model = models.vgg16(pretrained=True)

  13. model = models.vgg16_bn(pretrained=True)

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:

 
  1. # coding=UTF-8

  2. import torchvision.models as models

  3.  
  4. #调用模型

  5. model = models.resnet50(pretrained=True)

  6. #提取fc层中固定的参数

  7. fc_features = model.fc.in_features

  8. #修改类别为9

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


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。

 
  1. # coding=UTF-8

  2. import torchvision.models as models

  3. import torch

  4. import torch.nn as nn

  5. import math

  6. import torch.utils.model_zoo as model_zoo

  7.  
  8. class CNN(nn.Module):

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

  11. self.inplanes = 64

  12. super(ResNet, self).__init__()

  13. self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,

  14. bias=False)

  15. self.bn1 = nn.BatchNorm2d(64)

  16. self.relu = nn.ReLU(inplace=True)

  17. self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

  18. self.layer1 = self._make_layer(block, 64, layers[0])

  19. self.layer2 = self._make_layer(block, 128, layers[1], stride=2)

  20. self.layer3 = self._make_layer(block, 256, layers[2], stride=2)

  21. self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

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

  23. #新增一个反卷积层

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

  25. #新增一个最大池化层

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

  27. #去掉原来的fc层,新增一个fclass层

  28. self.fclass = nn.Linear(2048, num_classes)

  29.  
  30. for m in self.modules():

  31. if isinstance(m, nn.Conv2d):

  32. n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels

  33. m.weight.data.normal_(0, math.sqrt(2. / n))

  34. elif isinstance(m, nn.BatchNorm2d):

  35. m.weight.data.fill_(1)

  36. m.bias.data.zero_()

  37.  
  38. def _make_layer(self, block, planes, blocks, stride=1):

  39. downsample = None

  40. if stride != 1 or self.inplanes != planes * block.expansion:

  41. downsample = nn.Sequential(

  42. nn.Conv2d(self.inplanes, planes * block.expansion,

  43. kernel_size=1, stride=stride, bias=False),

  44. nn.BatchNorm2d(planes * block.expansion),

  45. )

  46.  
  47. layers = []

  48. layers.append(block(self.inplanes, planes, stride, downsample))

  49. self.inplanes = planes * block.expansion

  50. for i in range(1, blocks):

  51. layers.append(block(self.inplanes, planes))

  52.  
  53. return nn.Sequential(*layers)

  54.  
  55. def forward(self, x):

  56. x = self.conv1(x)

  57. x = self.bn1(x)

  58. x = self.relu(x)

  59. x = self.maxpool(x)

  60.  
  61. x = self.layer1(x)

  62. x = self.layer2(x)

  63. x = self.layer3(x)

  64. x = self.layer4(x)

  65.  
  66. x = self.avgpool(x)

  67. #新加层的forward

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

  69. x = self.convtranspose1(x)

  70. x = self.maxpool2(x)

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

  72. x = self.fclass(x)

  73.  
  74. return x

  75.  
  76. #加载model

  77. resnet50 = models.resnet50(pretrained=True)

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

  79. #读取参数

  80. pretrained_dict = resnet50.state_dict()

  81. model_dict = cnn.state_dict()

  82. # 将pretrained_dict里不属于model_dict的键剔除掉

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

  84. # 更新现有的model_dict

  85. model_dict.update(pretrained_dict)

  86. # 加载我们真正需要的state_dict

  87. cnn.load_state_dict(model_dict)

  88. # print(resnet50)

  89. print(cnn)

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