pytorch加载VGG16及进行fine-tuning训练

加载VGG16模型并打印查看

from torchvision import models
net=models.vgg16()
print(net)

1.1结果说明

1.2查看某一部分

**

加载模型进行预训练，改变classifier层，固定feature层参数

**
2.1模型搭建

import torch
import torch.nn as nn
from torchvision import models
from torchsummary import summary
net=models.vgg16()class VGGnet(nn.Module):def __init__(self,feature_extract=True,num_classes=5):super(VGGnet, self).__init__()#导入VGG16模型model = models.vgg16(pretrained=True)#加载features部分self.features = model.features#固定特征提取层参数set_parameter_requires_grad(self.features, feature_extract)#加载avgpool层self.avgpool=model.avgpool#改变classifier：分类输出层self.classifier = nn.Sequential(nn.Linear(512*7*7 , 1024),nn.ReLU(),nn.Linear(1024, 1024),nn.ReLU(),nn.Linear(1024, num_classes))def forward(self, x):x = self.features(x)x = self.avgpool(x)x = x.view(x.size(0), 512*7*7)out=self.classifier(x)return out#固定参数，不进行训练
def set_parameter_requires_grad(model, feature_extracting):if feature_extracting:for param in model.parameters():param.requires_grad = False

2.2 模型对比

net_self=VGGnet()print('model_build','**'*20)print(net_self)

结果：与原始模型（1.1中的结果）改变的是classifier层

模型训练
3.1数据及数据加载代码来源：Tensorflow2.1.0 自定义数据集：精灵宝可梦数据集

import os
from torch.utils.data import Dataset, DataLoader  #自定义的母类，必须的
from torchvision.transforms import transforms
from PIL import Image
import torch
import glob
import csv
import randomclass Pokemon(Dataset):def __init__(self, root, resize, mode):super(Pokemon, self).__init__()self.root = rootself.resize = resizeself.name2label = {}  # "sq...":0for name in sorted(os.listdir(os.path.join(root))):if not os.path.isdir(os.path.join(root, name)):continueself.name2label[name] = len(self.name2label.keys()) #将英文标签名转化数字0-4# print(self.name2label)# image, labelself.images, self.labels = self.load_csv('images.csv')  #csv文件存在 直接读取if mode == 'train':  # 60%                   self.images = self.images[:int(0.6 * len(self.images))]self.labels = self.labels[:int(0.6 * len(self.labels))]elif mode == 'val':  # 20% = 60%->80%self.images = self.images[int(0.6 * len(self.images)):int(0.8 * len(self.images))]self.labels = self.labels[int(0.6 * len(self.labels)):int(0.8 * len(self.labels))]else:  # 20% = 80%->100%self.images = self.images[int(0.8 * len(self.images)):]self.labels = self.labels[int(0.8 * len(self.labels)):]def __len__(self):return len(self.images)def __getitem__(self, idx):# idx~[0~len(images)]# self.images, self.labels# img: 'pokemon\\bulbasaur\\00000000.png'# label: 0img, label = self.images[idx], self.labels[idx]tf = transforms.Compose([   #常用的数据变换器lambda x:Image.open(x).convert('RGB'),  # string path= > image data #这里开始读取了数据的内容了transforms.Resize(   #数据预处理部分(int(self.resize * 1.25), int(self.resize * 1.25))), transforms.RandomRotation(15), transforms.CenterCrop(self.resize), #防止旋转后边界出现黑框部分transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])])img = tf(img)label = torch.tensor(label)  #转化tensorreturn img, label       #返回当前的数据内容和标签def load_csv(self, filename):if not os.path.exists(os.path.join(self.root, filename)): #如果没有保存csv文件，那么我们需要写一个csv文件，如果有了直接读取csv文件images = []for name in self.name2label.keys():   # 'pokemon\\mewtwo\\00001.pngimages += glob.glob(os.path.join(self.root, name, '*.png'))images += glob.glob(os.path.join(self.root, name, '*.jpg'))images += glob.glob(os.path.join(self.root, name, '*.jpeg'))random.shuffle(images)with open(os.path.join(self.root, filename), mode='w', newline='') as f:writer = csv.writer(f)for img in images:  # 'pokemon\\bulbasaur\\00000000.png'name = img.split(os.sep)[-2]        #从名字就可以读取标签label = self.name2label[name]# 'pokemon\\bulbasaur\\00000000.png', 0writer.writerow([img, label])  #写进csv文件# read from csv fileimages, labels = [], []with open(os.path.join(self.root, filename)) as f:reader = csv.reader(f)for row in reader:# 'pokemon\\bulbasaur\\00000000.png', 0img, label = rowlabel = int(label)images.append(img)labels.append(label)assert len(images) == len(labels)return images, labelsdef denormalize(self, x_hat):mean = [0.485, 0.456, 0.406]std = [0.229, 0.224, 0.225]# x_hat = (x-mean)/std# x = x_hat*std = mean# x: [c, h, w]# mean: [3] => [3, 1, 1]mean = torch.tensor(mean).unsqueeze(1).unsqueeze(1)std = torch.tensor(std).unsqueeze(1).unsqueeze(1)# print(mean.shape, std.shape)x = x_hat * std + meanreturn x
if __name__=='__main__':db = Pokemon('pokeman', 224, 'train')loader = DataLoader(db, batch_size=32, shuffle=True)for x, y in loader: #此时x,y是批量的数据print(x.shape)

3.2 训练

import torch
import torch.nn as nn
from torchvision import modelsclass VGGnet(nn.Module):def __init__(self,feature_extract=True,num_classes=5):super(VGGnet, self).__init__()model = models.vgg16(pretrained=True)self.features = model.featuresset_parameter_requires_grad(self.features, feature_extract)#固定特征提取层参数self.avgpool=model.avgpoolself.classifier = nn.Sequential(nn.Linear(512*7*7 , 1024),nn.ReLU(),nn.Linear(1024, 1024),nn.ReLU(),nn.Linear(1024, num_classes))def forward(self, x):x = self.features(x)x = self.avgpool(x)x = x.view(x.size(0), 512*7*7)out=self.classifier(x)return outdef set_parameter_requires_grad(model, feature_extracting):if feature_extracting:for param in model.parameters():param.requires_grad = Falseif __name__=="__main__":import torch.nn as nnfrom torch.utils.data import DataLoaderfrom data_read import Pokemon# In[]learning_rate=0.001num_epochs = 2               # train the training data n times, to save time, we just train 1 epochbatch_size = 32LR = 0.01              # learning rate# In[]train_dataset = Pokemon('pokeman', 224, 'train')val_dataset = Pokemon('pokeman', 224, 'val')test_dataset = Pokemon('pokeman', 224, 'test')train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)test_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)val_loader = DataLoader(train_dataset, batch_size=8, shuffle=True)# In[]device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')model=VGGnet().to(device)criterion = nn.CrossEntropyLoss()optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)# In[]total_step = len(train_loader)for epoch in range(num_epochs):for i, (images, labels) in enumerate(train_loader):images = images.to(device)labels = labels.to(device)# Forward passoutputs = model(images)loss = criterion(outputs, labels)# Backward and optimizeoptimizer.zero_grad()loss.backward()optimizer.step()if (i + 1) % 2 == 0:print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, i + 1, total_step, loss.item()))# In[]      # Test the modelmodel.eval()  #with torch.no_grad():correct = 0total = 0for images, labels in test_loader:images = images.to(device)labels = labels.to(device)outputs = model(images)_, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted == labels).sum().item()print('Test Accuracy  {} %'.format(100 * correct / total))

3.3 结果

数据集链接：所有分享的数据集都在这个文件夹，分类，迁移学习，图像分割等
数据集名称：VGG16(fine-tuning_pokeman)
百度云链接：https://pan.baidu.com/s/1eyDFV6YVaOHwr9QrRcKXFg
提取码：rlne

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