This is also the case for TensorDataset. This is potentially confusing for a new user. I had to look up the code to see if there's any relation or difference between the two versions.
My proposal for solving this is:
Use torch.utils.data.Dataset as baseclass for all the datasets in tnt. This might mean foregoing of batch, transform etc. methods, but I think they're actually confusing anyway.
Copy the code tochnet.dataset.TensorDataset to torch.utils.data.TensorDataset and ditch tochnet.dataset.TensorDataset
We have two base classes for datasets both in pytorch and torchnet.
torch.utils.data.Dataset
tochnet.dataset.Dataset
This is also the case for
TensorDataset
. This is potentially confusing for a new user. I had to look up the code to see if there's any relation or difference between the two versions.My proposal for solving this is:
torch.utils.data.Dataset
as baseclass for all the datasets in tnt. This might mean foregoing ofbatch
,transform
etc. methods, but I think they're actually confusing anyway.tochnet.dataset.TensorDataset
totorch.utils.data.TensorDataset
and ditchtochnet.dataset.TensorDataset
Sasank.