使用dataloader pytorch构建数据集时出错，无法导入名称“ read_data_sets”

Question

使用pytorch数据加载器将数据加载到数据集中。 遇到错误，无法导入名称“ read_data_sets”

尝试了类似问题的搜索结果。 如果对文件而不是模块感到困惑，并且在文件中找不到read_data_sets，该如何更改以解决问题？

class MRDataset(data.Dataset):
    def __init__(self, root_dir, task, plane, train=True, transform=None, weights=None):
        super().__init__()
        self.task = task
        self.plane = plane
        self.root_dir = root_dir
        self.train = train
        if self.train:
            self.folder_path = self.root_dir + 'train/{0}/'.format(plane)
            self.records = pd.read_csv(
                self.root_dir + 'train-{0}.csv'.format(task), header=None, names=['id', 'label'])
        else:
            transform = None
            self.folder_path = self.root_dir + 'valid/{0}/'.format(plane)
            self.records = pd.read_csv(
                self.root_dir + 'valid-{0}.csv'.format(task), header=None, names=['id', 'label'])

        self.records['id'] = self.records['id'].map(
            lambda i: '0' * (4 - len(str(i))) + str(i))
        self.paths = [self.folder_path + filename +
                      '.npy' for filename in self.records['id'].tolist()]
        self.labels = self.records['label'].tolist()

        self.transform = transform
        if weights is None:
            pos = np.sum(self.labels)
            neg = len(self.labels) - pos
            self.weights = torch.FloatTensor([1, neg / pos])
        else:
            self.weights = torch.FloatTensor(weights)

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

    def __getitem__(self, index):
        array = np.load(self.paths[index])
        label = self.labels[index]
        if label == 1:
            label = torch.FloatTensor([[0, 1]])
        elif label == 0:
            label = torch.FloatTensor([[1, 0]])

        if self.transform:
            array = self.transform(array)
        else:
            array = np.stack((array,)*3, axis=1)
            array = torch.FloatTensor(array)

        # if label.item() == 1:
        #     weight = np.array([self.weights[1]])
        #     weight = torch.FloatTensor(weight)
        # else:
        #     weight = np.array([self.weights[0]])
        #     weight = torch.FloatTensor(weight)

        return array, label, self.weights

有一个模型和培训班来运行。在火车中指定的参数。 运行火车应该加载数据并遍历模型