model_watermark/block/data_get.py

# 数据格式定义部分
# 数据需准备成以下格式
# ├── 数据集路径：data_path
#     └── image：存放所有图片
#     └── train.txt：训练图片的绝对路径(或相对data_path下路径)和类别号，(image/mask/0.jpg 0 2\n)表示该图片类别为0和2，空类别图片无类别号
#     └── val.txt：验证图片的绝对路径(或相对data_path下路径)和类别
#     └── class.txt：所有的类别名称
# class.txt内容如下：
# 类别1
# 类别2

import numpy as np
import os

def data_get(args):
    data_dict = data_prepare(args).load()
    return data_dict


class data_prepare:
    def __init__(self, args):
        self.args = args
        self.data_path = os.path.join(args.data_path, args.dataset_name)
        self.dataset_name = args.dataset_name

    def load(self):
        data_dict = {}
        data_dict['train'] = self._load_label('train.txt')
        data_dict['test'] = self._load_label('test.txt')
        data_dict['class'] = self._load_class()
        return data_dict

    def _load_label(self, txt_name):
        with open(f'{self.args.data_path}/{self.args.dataset_name}/{txt_name}', encoding='utf-8') as f:
            txt_list = [_.strip().split(' ') for _ in f.readlines()]  # 读取所有图片路径和类别号
        data_list = [['', 0] for _ in range(len(txt_list))]  # [图片路径,类别独热编码]
        for i, line in enumerate(txt_list):
            image_path = line[0]
            # print(image_path)
            data_list[i][0] = image_path
            data_list[i][1] = np.zeros(self.args.output_class, dtype=np.float32)
            for j in line[1:]:
                data_list[i][1][int(j)] = 1
        return data_list

    def _load_class(self):
        with open(f'{self.args.data_path}/{self.args.dataset_name}/class.txt', encoding='utf-8') as f:
            txt_list = [_.strip() for _ in f.readlines()]
        return txt_list


if __name__ == '__main__':
    import argparse

    parser = argparse.ArgumentParser(description='Data loader for specific dataset')
    parser.add_argument('--data_path', default='../dataset', type=str, help='Root path to datasets')
    parser.add_argument('--dataset_name', default='CIFAR-10', type=str, help='Specific dataset name')
    parser.add_argument('--output_class', default=10, type=int, help='Number of output classes')
    parser.add_argument('--input_size', default=640, type=int)
    args = parser.parse_args()
    data_dict = data_get(args)
    print(len(data_dict['train']))