tonic.datasets.pokerdvs#

Module Contents#

Classes#

POKERDVS

POKER-DVS
class tonic.datasets.pokerdvs.POKERDVS(save_to: str, train: bool = True, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, transforms: Optional[Callable] = None)[source]#

Bases: tonic.dataset.Dataset

POKER-DVS

Events have (txyp) ordering.

@article{serrano2015poker,
  title={Poker-DVS and MNIST-DVS. Their history, how they were made, and other details},
  author={Serrano-Gotarredona, Teresa and Linares-Barranco, Bernab{'e}},
  journal={Frontiers in neuroscience},
  volume={9},
  pages={481},
  year={2015},
  publisher={Frontiers}
}
Parameters:

  • save_to (string) – Location to save files to on disk.

  • train (bool) – If True, uses training subset, otherwise testing subset.

  • transform (callable, optional) – A callable of transforms to apply to the data.

  • target_transform (callable, optional) – A callable of transforms to apply to the targets/labels.

  • transforms (callable, optional) – A callable of transforms that is applied to both data and labels at the same time.

base_url = 'https://nextcloud.lenzgregor.com/s/'#
train_filename = 'pips_train.tar.gz'#
test_filename = 'pips_test.tar.gz'#
train_url#
test_url#
train_md5 = '412bcfb96826e4fcb290558e8c150aae'#
test_md5 = 'eef2bf7d0d3defae89a6fa98b07c17af'#
classes = ['cl', 'he', 'di', 'sp']#
int_classes#
sensor_size = (35, 35, 2)#
dtype#
ordering#
__getitem__(index)[source]#
Returns:

a tuple of (events, target) where target is the index of the target class.

__len__()[source]#