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All Projects → kevinzakka → Torchkit

kevinzakka / Torchkit

Licence: mit
Research boilerplate for PyTorch.

Programming Languages

python
139335 projects - #7 most used programming language

Labels

deep-learningpytorch

torchkit

documentation license

torchkit is a lightweight library containing PyTorch utilities useful for day-to-day research. Its main goal is to abstract away a lot of the redundant boilerplate associated with research projects like experimental configurations, logging and model checkpointing. It consists of:

torchkit.Logger A wrapper around Tensorboard's SummaryWriter for safe logging of losses, learning rates and metrics.
torchkit.checkpoint A port of Tensorflow's checkpoint management tools containing:
  • Checkpoint: For saving and restoring any object with a state_dict attribute.
  • CheckpointManager: For automatically managing multiple checkpoints in an experimental run.
torchkit.layers A set of commonly used layers in research papers not available in vanilla PyTorch like "same" and "causal" convolution and SpatialSoftArgmax.
torchkit.featurizers Featurizers are convenience wrappers around PyTorch ResNets and InceptionNets, giving you fine-grained control over what layer to use as output, how to fine-tune them and how to update batch norm statistics. Featurizers can be easily extended to any model of choice.
torchkit.losses Some useful loss functions also unavailable in vanilla PyTorch like cross entropy with label smoothing and Huber loss.
torchkit.utils A growing list of PyTorch-related helper functions.

For more details about each module, see the documentation.

Installation

Use the following command to install directly with pip:

pip install git+https://github.com/kevinzakka/torchkit.git

Acknowledgments

  • I learned a ton about experiment management by re-implementing Debidatta Dwibedi's open-source implementation of Temporal Cycle Consistency Learning.
  • Thanks to Karan Desai's VirTex which I used to figure out documentation-related setup for torchkit and for just being an excellent example of stellar open-source research release.
  • Thanks to Brent Yi for encouraging me to use type hinting and for letting me use his awesome Bayesian filtering library's README as a template.
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