VicenteAlex / DVS-Gesture-Chain

Implementation of the dataset defined in Spiking Neural Networks for event-based action recognition: A new task to understand their advantage

DVS Gesture Chain

Implementation of the dataset defined in Spiking Neural Networks for event-based action recognition: A new task to understand their advantage

Installation

From PyPI:

pip install dvsgc

From GitHub source code:

pip install requirements

Documentation

Just instantiate DVSGestureChain and run it as a torchvision.datasets.DatasetFolder:

Inherits from torchvision.datasets.DatasetFolder

CLASS dvsgc.DVSGestureChain(
            root, frames_number, split, validation=0.2, split_by='number', alpha_min=0.5, alpha_max=0.7, seq_len=4,
            class_num=3, repeat=True, dvsg_path=None, transform=None, target_transform=None)

Arguments:

• root (str): root path of the dataset
• frames_number (int): the integrated frame number
• split (str): split from: ['train', 'validation', 'test']
• validation (float): fraction of the training set to use for validation
• split_by (str): time or number
• alpha_min (float): lower bound for the gesture duration as a factor of its total duration
• alpha_max (float): upper bound for the gesture duration as a factor of its total duration
• seq_len (int): number of gestures in the chain
• class_num (int): number of classes to use (up to 11)
• repeat (bool): whether to allow repetition of the same gesture twice in a row
• dvsg_path (str): If DVS-Gesture events are saved in another folder they can be retrieved by indicating the path here, else they will be created again in root
• transform (Optional[Callable]): a function/transform that takes in a sample and returns a transformed version.
• target_transform (Optional[Callable]): a function/transform that takes in the target and transforms it.

Usage:

Example:

train_set = dvsgc.DVSGestureChain(root=dataset_dir, split='train', frames_number=T)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=N, shuffle=True, num_workers=4, pin_memory=True, drop_last=True)

validation_set = dvsgc.DVSGestureChain(root=dataset_dir, split='validation', frames_number=T)
validation_loader = torch.utils.data.DataLoader(validation_set, batch_size=N, shuffle=True, num_workers=4, pin_memory=True, drop_last=True)

Downloading the data:

DVS-GC requires to download the data from the original DVS-Gesture dataset, as this is not downloaded automatically, the first time the dataset is used the following message will appear: "This dataset can not be downloaded automatically, please download files manually and put files at root/download". Follow the instructions and download the DVS-Gesture files into that folder. Then re-run.

Saving memory:

The first time the dataset is used, the code will extract DVS-Gestures to the folder "extract", then build events from it in the folder "events_np" and then, from those events, create the frames for the DVS-GC dataset in a "DVSGC..." folder.

Once events_np is created, the extract folder and download folder are no longer necessary.

The "events_np" folder will be used every time a new DVS-GC dataset is created with different parameters. Alternatively, if an already existing DVS-GC dataset is used, "events_np" is not necessary.