CODEWITHSUNDEEP
pythonpytorchtorchpytorch-dataloader
Adham Enaya
Adham Enaya

Reputation: 908

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

I am trying to load the dataset using Torch Dataset and DataLoader, but I got the following error:

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

the code I use is:

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)

I tried to make the num_workers=0, still have the same error.

Python version 3.8.9
PyTorch version 1.13.0

Upvotes: 37

Views: 59188

Answers (4)

Syed Jameel Ahmed
Syed Jameel Ahmed

Reputation: 1056

I too faced the same issue, when i tried to call the next() method as follows

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

You need to use the following instead and it works perfectly:

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

Finally your code should look like follows:

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

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

Upvotes: 94

Swarnendu
Swarnendu

Reputation: 21

I was facing the same issue. I am using torch ==2.3 version. This worked for me.

class WineDataset(Dataset):

    def __init__(self):                
        # data loading
        xy = np.loadtxt('path\\wine.csv', delimiter = ",", dtype = np.float32, skiprows =1 )
        self.x = torch.from_numpy(xy[:,1:])
        self.y = torch.from_numpy(xy[:,[0]]) # n_samples,1
        self.n_samples = xy.shape[0]

    def __getitem__(self,index):
        # dataset[0]
        return self.x[index], self.y[index]
         
    def __len__(self):
        #len(dataset)
        return self.n_samples    


if __name__ == '__main__':

    dataset = WineDataset()
    dataloader = DataLoader(dataset=dataset, batch_size=4, shuffle=True, num_workers=2)
    dataiter = iter(dataloader)
    data = next(dataiter)

Upvotes: 2

Khawar Islam
Khawar Islam

Reputation: 2924

Updated April 2023 Instead of changing from iter(trn_loader).next() to next(iter(trn_loader)). I prefer to solve pyTorch version problem because I have no idea how many .next() is present in the code.

conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 -c pytorch

Upvotes: 0

ChaosPredictor
ChaosPredictor

Reputation: 4081

In pytorch 1.12 the syntax:

iter(trn_loader).next()

work fine as well as:

next(iter(trn_loader))

From pytorch 1.13 the only working syntax is:

next(iter(trn_loader))

Upvotes: 13

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