This package contains utilities that make it easy to work with hypernetworks in PyTorch.
You can either install the latest package version via
python3 -m pip install hypnettorch
or, you directly install the current sources
python3 -m pip install git+https://github.com/chrhenning/hypnettorch
If you actively develop the package, it is easiest to install it in development mode, such that all changes that are done to source files are directly visible when you use the package.
Clone the repository to a location of your choice
git clone https://github.com/chrhenning/hypnettorch.git
and move inside the cloned repo
cd ./hypnettorch
Now, you can simply install the package in editable mode, which will ensure that you can easily update the package sources (cf. development mode)
pip3 install --editable . --user
Since the package was installed in editable mode, you can always update the sources simply by pulling the most recent code
git pull
You can uninstall the package at any point by running python3 setup.py develop -u.
The basic functionalities of the package are quite intuitive and easy to use, e.g.,
import torch
from hypnettorch.mnets import MLP
from hypnettorch.hnets import HMLP
mnet = MLP(n_in=8, n_out=1, no_weights=True) # Create MLP with 8 inputs and 1 output.
hnet = HMLP(mnet.param_shapes) # Create hypernetwork for the above MLP.
weights = hnet.forward(cond_id=0) # Generate the weights of network `mnet`.
inputs = torch.rand(32, 8) # Random inputs.
mnet.forward(inputs, weights=weights) # Compute predictions using the generated weights.There are several tutorials. Check out the getting started tutorial when working with hypnettorch for the first time.
You may also checkout the examples. For instance, how to use hypernetworks for continual learning.
The documentation can be found here.
The documentation can be build using
python3 setup.py build_sphinx
and opened via the file index.html.
When using this package in your research project, please consider citing one of our papers for which this package has been developed.
@inproceedings{posterior:replay:2021:henning:cervera,
title={Posterior Meta-Replay for Continual Learning},
author={Christian Henning and Maria R. Cervera and Francesco D'Angelo and Johannes von Oswald and Regina Traber and Benjamin Ehret and Seijin Kobayashi and Benjamin F. Grewe and João Sacramento},
booktitle={Conference on Neural Information Processing Systems},
year={2021},
url={https://arxiv.org/abs/2103.01133}
}
@inproceedings{ehret2020recurrenthypercl,
title={Continual Learning in Recurrent Neural Networks},
author={Benjamin Ehret and Christian Henning and Maria R. Cervera and Alexander Meulemans and Johannes von Oswald and Benjamin F. Grewe},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://arxiv.org/abs/2006.12109}
}
@inproceedings{oshg2019hypercl,
title={Continual learning with hypernetworks},
author={Johannes von Oswald and Christian Henning and Benjamin F. Grewe and Jo{\~a}o Sacramento},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://arxiv.org/abs/1906.00695}
}