tonic 0.4.1

Event-based datasets and transformations based on pyTorch vision.

Battling with all the different file formats of publicly available neuromorphic datasets? No more! Tonic is a tool to facilitate the download, manipulation and loading of event-based/spike-based data. Have a look at the list of supported datasets and transformations! It's somewhat modeled after PyTorch Vision for an intuitive interface, so that you spend less time worrying about how to read files and more time on things that matter.

Install

pip install tonic

If you prefer conda, please check out the forge repository.

Getting started

Have a look at our introduction page to see how some of the moving parts work. There are some more short examples available here.

Quickstart

If you're looking for a minimal example to run, this is it!

import tonic
import tonic.transforms as transforms

transform = transforms.Compose([transforms.Denoise(filter_time=10000),
                                transforms.TimeJitter(std=10),])

testset = tonic.datasets.NMNIST(save_to='./data',
                                train=False,
                                transform=transform)

from torch.utils.data import DataLoader
testloader = DataLoader(testset, shuffle=True)

events, target = next(iter(testloader))

Discussion

Have a question about how something works? Ideas for improvement? Feature request? Please get in touch here on GitHub via the Discussions page!

Documentation

You can find the full documentation on Tonic on this site.

Citation

If you find this package helpful, please use the following citation:

@software{lenz_gregor_2021_5079802,
  author       = {Lenz, Gregor and
                  Chaney, Kenneth and
                  Shrestha, Sumit Bam and
                  Oubari, Omar and
                  Picaud, Serge and
                  Zarrella, Guido},
  title        = {Tonic: event-based datasets and transformations.},
  month        = jul,
  year         = 2021,
  note         = {{Documentation available under 
                   https://tonic.readthedocs.io}},
  publisher    = {Zenodo},
  version      = {0.4.0},
  doi          = {10.5281/zenodo.5079802},
  url          = {https://doi.org/10.5281/zenodo.5079802}
}