import torchnet
from torch.utils.data import DataLoader
[docs]class Dataset(object):
def __init__(self):
pass
def __len__(self):
pass
def __getitem__(self, idx):
if idx >= len(self):
raise IndexError("CustomRange index out of range")
pass
[docs] def batch(self, *args, **kwargs):
return torchnet.dataset.BatchDataset(self, *args, **kwargs)
[docs] def shuffle(self, *args, **kwargs):
return torchnet.dataset.ShuffleDataset(self, *args, **kwargs)
[docs] def parallel(self, *args, **kwargs):
return DataLoader(self, *args, **kwargs)
[docs] def split(self, *args, **kwargs):
return torchnet.dataset.SplitDataset(self, *args, **kwargs)