中文README

This repository contains the origin codes and TensorBoard logs for the paper Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks. The trained models are too large that we can't upload them to this repository. But we used a identical seed during training, and we can ensure that the user can get almost the same accuracy when using our codes to train.

This table shows the accuracy of using PLIF neurons, tau_0=2 and max pooling:

This table shows the accuracy-A of using PLIF/LIF neurons, different tau/tau_0 and average/max pooling:

codes contains the origin codes:

models.py defines the networks.

train.py trains models on the training set, tests on the test set alternately, and records the maximum test accuracy, which is the accuracy-A in the paper.

train_val.py splits the origin training set into a new training set and validation set, trains on the new training set, tests on the validation set alternately, and records the test accuracy on the test set only once, with the model achieving the maximum validation accuracy, which is the accuracy-B in the paper.

logs contains A and B directories, which contains TensorBoard logs for different accuracies, respectively.

The origin codes uses the old version SpikingJelly. To maximize reproducibility, the user can download the latest SpikingJelly and rollback to the version that we used to train:

git clone https://github.com/fangwei123456/spikingjelly.git
cd spikingjelly
git reset --hard 73f94ab983d0167623015537f7d4460b064cfca1
python setup.py install

Here is the commit information:

commit 73f94ab983d0167623015537f7d4460b064cfca1
Author: fangwei123456 <[email protected]>
Date:   Wed Sep 30 16:42:25 2020 +0800

    增加detach reset的选项

The line 64 of train.py, and line 84 of train_val.py defines the dataset path:

dataset_dir = '/userhome/datasets/' + dataset_name

where /userhome/datasets/ is the root path of all datasets.

The root path of all datasets should have the following directory structure:

|-- CIFAR10
|   |-- cifar-10-batches-py
|   `-- cifar-10-python.tar.gz
|-- CIFAR10DVS
|   |-- airplane.zip
|   |-- automobile.zip
|   |-- bird.zip
|   |-- cat.zip
|   |-- deer.zip
|   |-- dog.zip
|   |-- events
|   |-- frames_num_20_split_by_number_normalization_None
|   |-- frog.zip
|   |-- horse.zip
|   |-- ship.zip
|   `-- truck.zip
|-- DVS128Gesture
|   |-- DvsGesture.tar.gz
|   |-- LICENSE.txt
|   |-- README.txt
|   |-- events_npy
|   |-- extracted
|   |-- frames_num_20_split_by_number_normalization_None
|   `-- gesture_mapping.csv
|-- FashionMNIST
|   |-- FashionMNIST
|-- MNIST
|   `-- MNIST
`-- NMNIST
    |-- Test.zip
    |-- Train.zip
    |-- events
    `-- frames_num_10_split_by_number_normalization_None

MNIST, Fashion-MNIST and CIFAR10 dataset can be available from torchvision. For neuromorphic datasets' installation, see

https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.4/spikingjelly.datasets.html

Here are the origin running codes for accuracy-B:

The code can recovery training from the interruption. It will load the exist model and continue training from the last epoch.

This table shows the definition of all arguments:

For more details about split_by和normalization, see

https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.4/spikingjelly.datasets.html#integrate-events-to-frames-init-en

SpkingJelly (0.0.0.0.12 or the latest version) has added the network with LIF/max-pooling as an example:

0.0.0.0.12: https://spikingjelly.readthedocs.io/zh_CN/0.0.0.0.12/clock_driven_en/14_classify_dvsg.html

latest: https://spikingjelly.readthedocs.io/zh_CN/latest/activation_based_en/classify_dvsg.html

The codes are written by the new version of SpikingJelly, which are faster than codes in this repository.

All networks in this paper are available at SpikingJelly:

0.0.0.0.12: https://github.com/fangwei123456/spikingjelly/blob/0.0.0.0.12/spikingjelly/clock_driven/model/parametric_lif_net.py

latest: https://github.com/fangwei123456/spikingjelly/blob/master/spikingjelly/activation_based/model/sew_resnet.py

@InProceedings{Fang_2021_ICCV,
    author    = {Fang, Wei and Yu, Zhaofei and Chen, Yanqi and Masquelier, Timothee and Huang, Tiejun and Tian, Yonghong},
    title     = {Incorporating Learnable Membrane Time Constant To Enhance Learning of Spiking Neural Networks},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {2661-2671}
}