AttributeError: \'_MultiProcessingDataLoaderIter\' 对象没有属性 \'next\'

Question

我正在尝试使用Torch Dataset and DataLoader 加载数据集，但出现以下错误：

AttributeError: '_MultiProcessingDataLoaderIter' object has no attribute 'next'

我使用的代码是：

class WineDataset(Dataset):

    def __init__(self):
        # Initialize data, download, etc.
        # read with numpy or pandas
        xy = np.loadtxt('./data/wine.csv', delimiter=',', dtype=np.float32, skiprows=1)
        self.n_samples = xy.shape[0]

        # here the first column is the class label, the rest are the features
        self.x_data = torch.from_numpy(xy[:, 1:]) # size [n_samples, n_features]
        self.y_data = torch.from_numpy(xy[:, [0]]) # size [n_samples, 1]

    # support indexing such that dataset[i] can be used to get i-th sample
    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    # we can call len(dataset) to return the size
    def __len__(self):
        return self.n_samples

    dataset = WineDataset()
        
    train_loader = DataLoader(dataset=dataset,
                              batch_size=4,
                              shuffle=True,
                              num_workers=2)

我试图让num_workers = 0，仍然有同样的错误。

Python version 3.8.9
PyTorch version 1.13.0

Answer 1

当我尝试如下调用 next() 方法时，我也遇到了它

dataiter = iter(dataloader)
data = dataiter.next()

您需要改用以下内容，它可以完美运行：

dataiter = iter(dataloader)
data = next(dataiter)

最后，您的代码应如下所示：

class WineDataset(Dataset):

    def __init__(self):
        # Initialize data, download, etc.
        # read with numpy or pandas
        xy = np.loadtxt('./data/wine.csv', delimiter=',', dtype=np.float32, skiprows=1)
        self.n_samples = xy.shape[0]

        # here the first column is the class label, the rest are the features
        self.x_data = torch.from_numpy(xy[:, 1:]) # size [n_samples, n_features]
        self.y_data = torch.from_numpy(xy[:, [0]]) # size [n_samples, 1]

    # support indexing such that dataset[i] can be used to get i-th sample
    def __getitem__(self, index):
        return self.x_data[index], self.y_data[index]

    # we can call len(dataset) to return the size
    def __len__(self):
        return self.n_samples

    dataset = WineDataset()
        
    train_loader = DataLoader(dataset=dataset,
                              batch_size=4,
                              shuffle=True,
                              num_workers=2)

dataiter = iter(dataloader)
data = next(dataiter)