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）

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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）

2017年12月19日 18:49:37 whut_ldz 阅读数：7421 标签：深度学习神经网络 pytorch 预训练修改更多

个人分类： python，pytorch，深度学习

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)

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)

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)