class ImageDataset(Dataset):
    def __init__(self, data_path, mode,  transform=None):
        self.path=data_path
        data_dir=pathlib.Path(data_path)
        self.mode=mode
        self.transform=transform
        self.image_path=list(data_dir.glob("NWPU-RESISC45/*/*"))
        self.image_path=[str(path) for path in self.image_path]
        random.seed(1)
        random.shuffle(self.image_path)
        if self.mode == 'database':
            self.image_path=self.image_path[12000:]
        elif self.mode == 'test':
            self.image_path=self.image_path[7000:12000]
        else:
            self.image_path=self.image_path[:7000]
        lable_names = sorted(item.name for item in data_dir.glob("NWPU-RESISC45/*/"))
        lable_to_index = dict((name, index) for index, name in enumerate(lable_names))
        self.image_label=[lable_to_index[pathlib.Path(path).parent.name] for path in self.image_path]


    def __getitem__(self, index):
        img = Image.open(os.path.join(self.path, self.image_path[index]))
        img = img.convert('RGB')
        if self.transform is not None:
            img = self.transform(img)
        label = torch.LongTensor([self.image_label[index]])
        return img, label, index

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