pytorch 修改预训练模型

妖狐艹你老母 2022-11-29 00:56 420阅读 0赞

**转载请注明作者和出处：** [http://blog.csdn.net/john\_bh/][http_blog.csdn.net_john_bh]

### 文章目录 ###

*   *  1. pytorch 预训练模型
     *  2. 修改全连接层类别数目
     *  3. 修改某一层卷积
     *  4. 修改某几层卷积
     *   *  4.1 去掉后两层(fc层和pooling层)
         *  4.2 增减多个卷积层

## 1. pytorch 预训练模型 ##

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)

## 2. 修改全连接层类别数目 ##

预训练模型以 resnet50 为例。

model = torchvision.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10，重定义最后一层
    model.fc = nn.Linear(fc_features ,10)
    print(model.fc)

或者直接传入类别个数：

self.resnet = torchvision.models.resnet50(pretrained=False,num_classes=10)

## 3. 修改某一层卷积 ##

预训练模型以 resnet50 为例。

model = torchvision.models.resnet50(pretrained=True)
    # 重定义第一层卷积的输入通道数
    model.conv1 = nn.Conv2d(4, 64, kernel_size=7, stride=2, padding=3, bias=False)

## 4. 修改某几层卷积 ##

### 4.1 去掉后两层(fc层和pooling层) ###

预训练模型以 resnet50 为例。  
nn.module的model它包含一个叫做children()的函数，这个函数可以用来提取出model每一层的网络结构，在此基础上进行修改即可，修改方法如下(去除后两层)：

resnet_50_s = torchvision.models.resnet50(pretrained=False)
    resnet_layer = nn.Sequential(*list(resnet_50_s.children())[:-2])
    self.resnet = resnet_layer

在去掉预训练resnet模型的后两层(fc层和pooling层)后，新添加一个上采样层、池化层和分类层，构建网络代码如下：

class Net_resnet50_upsample(nn.Module):
        def __init__(self):
            super(Net_resnet50_upsample, self).__init__()
            self.conv = nn.Conv2d(1, 3, kernel_size=1)
            resnet_50_s = torchvision.models.resnet50(pretrained=False)
            resnet_layer = nn.Sequential(*list(resnet_50_s.children())[:-2])
            self.resnet = resnet_layer
            # print(self.resnet)
            
            self.up7to14=nn.UpsamplingNearest2d(scale_factor=2)
            self.avgpool=nn.AvgPool2d(7,stride=2)
            self.fc = nn.Sequential(
                nn.Linear(2048 * 4 * 4, 1024),
                nn.ReLU(inplace=True),
                nn.Linear(1024, 128),
                nn.ReLU(inplace=True),
                nn.Linear(128, 10))
    
        def forward(self, x):
            x = self.conv(x)
            x = self.resnet(x)
            x=self.up7to14(x)
            x=self.avgpool(x)
            x = x.view(x.size(0), -1)
            x = self.fc(x)
            return x

### 4.2 增减多个卷积层 ###

有的时候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
    
    #Bottleneck是一个class 里面定义了使用1*1的卷积核进行降维跟升维的一个残差块，可以在github resnet pytorch上查看
    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
    
    #不做修改的层不能乱取名字，否则预训练的权重参数无法传入
    class CNN(nn.Module):
        def __init__(self, block, layers, num_classes=9):
            self.inplanes = 64
            super(CNN, 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.AdaptiveAvgPool2d(output_size=(1,1))
            # 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=False)
    print(resnet50)
    cnn = CNN(Bottleneck, [3, 4, 6, 3]) #3 4 6 3 分别表示layer1 2 3 4 中Bottleneck模块的数量。res101则为3 4 23 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)

结果对比：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pvaG5fYmg_size_16_color_FFFFFF_t_70_pic_center]

[http_blog.csdn.net_john_bh]: http://blog.csdn.net/john_bh/
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2pvaG5fYmg_size_16_color_FFFFFF_t_70_pic_center]: /images/20221124/ab0071a57ab04519b2deec94ca4bcf45.png