pytorch loads its own data set, and reads train.txt and test.txt files (with data set txt to generate complete code, detailed comments)

pytorch reads the specified train.txt, test.txt file and loads its own data set

The complete code of the txt generation script is as follows:

Read your own data set, scramble and divide, generate train.txt, test.txt (absolute path + label of each line of pictures, complete code, detailed comments)
https://blog.csdn.net/weixin_44414948/article /details/110205546

Examples of train.txt and test.txt are shown in the figure below:

Load the complete code of the pytorch data set:

import torch
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
from PIL import Image

root = r"C:\Users\hq\Desktop\HoldingObject"

# 自定义图片图片读取方式，可以自行增加resize、数据增强等操作
def MyLoader(path):
    return Image.open(path).convert('RGB')
    
class MyDataset (Dataset):
    # 构造函数设置默认参数
    def __init__(self, txt, transform=None, target_transform=None, loader=MyLoader):
        with open(txt, 'r') as fh:
            imgs = []
            for line in fh:
                line = line.strip('\n')  # 移除字符串首尾的换行符
                line = line.rstrip()  # 删除末尾空
                words = line.split( )  # 以空格为分隔符 将字符串分成
                imgs.append((words[0], int(words[1]))) # imgs中包含有图像路径和标签
        self.imgs = imgs
        self.transform = transform
        self.target_transform = target_transform
        self.loader = loader

    def __getitem__(self, index):
        fn, label = self.imgs[index]
        #调用定义的loader方法
        img = self.loader(fn)
        if self.transform is not None:
            img = self.transform(img)
        return img, label

    def __len__(self):
        return len(self.imgs)


train_data = MyDataset(txt=root + '\\' + 'train.txt', transform=transforms.ToTensor())
test_data = MyDataset(txt=root + '\\' + 'test.txt', transform=transforms.ToTensor())

#train_data 和test_data包含多有的训练与测试数据，调用DataLoader批量加载
train_loader = DataLoader(dataset=train_data, batch_size=64, shuffle=True)
test_loader = DataLoader(dataset=test_data, batch_size=64)

print('加载成功！')

Note: Support pytorch's transforms operation