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
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)