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）

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#resnetmodel = models.ResNet(pretrained=True)model = models.resnet18(pretrained=True)model = models.resnet34(pretrained=True)model = models.resnet50(pretrained=True)#vggmodel = models.VGG(pretrained=True)model = models.vgg11(pretrained=True)model = models.vgg16(pretrained=True)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：

# coding=UTF-8import 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9model.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。

# coding=UTF-8import torchvision.models as modelsimport torchimport torch.nn as nnimport mathimport torch.utils.model_zoo as model_zooclass 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modelresnet50 = 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_dictmodel_dict.update(pretrained_dict)# 加载我们真正需要的state_dictcnn.load_state_dict(model_dict)# print(resnet50)print(cnn)

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