Pytorch迁移学习

环境：

Pytorch1.1，Python3.6，win10/ubuntu18，GPU

正文

1. Pytorch构建ResNet18模型并训练，进行真实图片分类；
2. 利用预训练的ResNet18模型进行Fine tune，直接进行图片分类；站在巨人的肩膀上，使用已经在ImageNet上训练好的模型，除了最后一层全连接层，中间层的参数全部迁移到目标模型上，如下图所示

       

项目结构如下所示

pokemon里面存放数据，分别是五个文件夹，其中每个文件夹分别存放一定数量的图片，总共1000多张图片；

best.mdl是保存下来的模型，可以直接加载进行分类

resnet.py是自己搭建的ResNet18模型

train_scratch.py利用resnet.py中的ResNet18模型进行图片分类

train_transfer.py利用下载的ResNet18模型进行图片分类

接下来进入正题：

pokemon.py：

import  torch
import  os, glob
import  random, csv

from    torch.utils.data import Dataset, DataLoader

from    torchvision import transforms
from    PIL import Image


class Pokemon(Dataset):

    def __init__(self, root, resize, mode):
        super(Pokemon, self).__init__()

        self.root = root
        self.resize = resize

        self.name2label = {} # "sq...":0
        for name in sorted(os.listdir(os.path.join(root))):
            if not os.path.isdir(os.path.join(root, name)):
                continue

            self.name2label[name] = len(self.name2label.keys())

        # print(self.name2label)

        # image, label
        self.images, self.labels = self.load_csv('images.csv')

        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 load_csv(self, filename):

        if not os.path.exists(os.path.join(self.root, filename)):
            images = []
            for name in self.name2label.keys():
                # 'pokemon\mewtwo\00001.png
                images += 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'))

            # 1167, 'pokemon\bulbasaur\00000000.png'
            print(len(images), images)

            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]
                    label = self.name2label[name]
                    # 'pokemon\bulbasaur\00000000.png', 0
                    writer.writerow([img, label])
                print('writen into csv file:', filename)

        # read from csv file
        images, labels = [], []
        with open(os.path.join(self.root, filename)) as f:
            reader = csv.reader(f)
            for row in reader:
                # 'pokemon\bulbasaur\00000000.png', 0
                img, label = row
                label = int(label)

                images.append(img)
                labels.append(label)

        assert len(images) == len(labels)

        return images, labels



    def __len__(self):

        return len(self.images)


    def 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 + mean

        return x


    def __getitem__(self, idx):
        # idx~[0~len(images)]
        # self.images, self.labels
        # img: 'pokemon\bulbasaur\00000000.png'
        # label: 0
        img, label = self.images[idx], self.labels[idx]

        tf = transforms.Compose([
            lambda x:Image.open(x).convert('RGB'), # string path= > image data
            transforms.Resize((int(self.resize*1.25), int(self.resize*1.25))),
            transforms.RandomRotation(15),
            transforms.CenterCrop(self.resize),
            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)


        return img, label





def main():

    import  visdom
    import  time
    import  torchvision

    viz = visdom.Visdom()

    # tf = transforms.Compose([
    #                 transforms.Resize((64,64)),
    #                 transforms.ToTensor(),
    # ])
    # db = torchvision.datasets.ImageFolder(root='pokemon', transform=tf)
    # loader = DataLoader(db, batch_size=32, shuffle=True)
    #
    # print(db.class_to_idx)
    #
    # for x,y in loader:
    #     viz.images(x, nrow=8, win='batch', opts=dict(title='batch'))
    #     viz.text(str(y.numpy()), win='label', opts=dict(title='batch-y'))
    #
    #     time.sleep(10)


    db = Pokemon('pokemon', 224, 'train')

    x,y = next(iter(db))
    print('sample:', x.shape, y.shape, y)

    viz.image(db.denormalize(x), win='sample_x', opts=dict(title='sample_x'))

    loader = DataLoader(db, batch_size=32, shuffle=True, num_workers=8)

    for x,y in loader:
        viz.images(db.denormalize(x), nrow=8, win='batch', opts=dict(title='batch'))
        viz.text(str(y.numpy()), win='label', opts=dict(title='batch-y'))

        time.sleep(10)

if __name__ == '__main__':
    main()

注释：Pokemon类功能是对数据集进行解析，把文件夹中的图片分成train,val,test三个集合