pytorch 修改预训练模型 妖狐艹你老母 2022-11-29 00:56 335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
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