Spike manipulation and augmentation
Project description
Tonic provides publicly available spike-based datasets and a pipeline of data augmentation methods based on PyTorch, which enables multithreaded dataloading and shuffling as well as batching.
Have a look at the list of supported datasets and transformations!
Install
pip install tonic
Quickstart
import tonic
import tonic.transforms as transforms
transform = transforms.Compose([transforms.TimeJitter(variance=10),
transforms.FlipLR(flip_probability=0.5),
transforms.ToTimesurface(surface_dimensions=(7,7), tau=5e3),])
testset = tonic.datasets.NMNIST(save_to='./data',
train=False,
transform=transform)
testloader = tonic.datasets.DataLoader(testset, shuffle=True)
for surfaces, target in iter(testloader):
print("{} surfaces for target {}".format(len(surfaces), target))
Documentation
You can find the full documentation on Tonic here.
Project details
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