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Last Commit
Feb. 22, 2018
Dec. 10, 2016


PyTorch version of


torchnet is a framework for torch which provides a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming.

Most of the modules support NumPy arrays as well as PyTorch tensors on input, so could potentially be used with other frameworks.


Make sure you have PyTorch installed, then do:

pip install git+[email protected]

To update:

pip install --upgrade git+[email protected]

Differences with lua version

What's been ported so far:

  • Datasets:
    • BatchDataset
    • ListDataset
    • ResampleDataset
    • ShuffleDataset
    • TensorDataset [new]
    • TransformDataset
  • Meters:
    • APMeter
    • mAPMeter
    • AverageValueMeter
    • AUCMeter
    • ClassErrorMeter
    • ConfusionMeter
    • MovingAverageValueMeter
    • MSEMeter
    • TimeMeter
  • Engines:
    • Engine
  • Logger
    • Logger
    • VisdomLogger
    • MeterLogger [new, easy to plot multi-meter via Visdom]

Any dataset can now be plugged into torch.utils.DataLoader, or called .parallel(num_workers=8) to utilize multiprocessing.