OTTT-SNN

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[NeurIPS 2022] Online Training Through Time for Spiking Neural Networks

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- augmentation.py
- cifar10_dvs.py
- configure.py
- dataset_utils.py
- datasets.py
- dvs128_gesture.py
- spiking_resnet_imagenet.py
- spiking_vgg.py
- base.py
- configure.py
- neuron.py
- neuron_spikingjelly.py
- surrogate.py
- - functional.py
  - layer.py
  - neuron.py
  - spiking_vggws_ottt.py
- train_ottt_cifar.py
- __init__.py
- eval.py
- logger.py
- misc.py
- visualize.py
get_rate_cifar.py
get_rate_cifar10dvs.py
get_rate_imagenet.py
README.md
train_cifar.py
train_cifar10dvs.py
train_dvsgesture.py
train_imagenet.py

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# OTTT-SNN
This is the PyTorch implementation of paper: Online Training Through Time for Spiking Neural Networks **(NeurIPS 2022)**. \[[arxiv](https://arxiv.org/abs/2210.04195)\]\[[openreview](https://openreview.net/forum?id=Siv3nHYHheI)\].

**Update 2023/12**: Some modules of OTTT have been integrated into the latest code of [spikingjelly](https://github.com/fangwei123456/spikingjelly), and the new codes for the neuron model can support multi-gpu training. We provide the reference codes included in the spikingjelly repository in spikingjelly\_codes/reference\_codes/, where neuron.py, layer.py, and functional.py (located in spikingjelly/activation\_based/ in their repository) include some modules for OTTT, and spiking\_vggws\_ottt.py (located in spikingjelly/activation\_based/model/ in their repository) gives an example of how to define the model with OTTT modules. We also provide an example of how to train the model in spikingjelly\_codes/train\_ottt\_cifar.py for reference.

## Dependencies and Installation
- Python 3 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux))
- [PyTorch, torchvision](https://pytorch.org/)
- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
- Python packages: `pip install numpy opencv-python`

## Training
For OTTT$_A$, run as following:

python train_cifar.py -data_dir path_to_data_dir -dataset cifar10 -out_dir log_checkpoint_name -gpu-id 0

# For VGG-F model
	python train_cifar.py -data_dir path_to_data_dir -dataset cifar100 -out_dir log_checkpoint_name -gpu-id 0 -model online_spiking_vgg11f_ws

python train_cifar10dvs.py -data_dir path_to_data_dir -out_dir log_checkpoint_name -gpu-id 0

python train_imagenet.py -data_dir path_to_data_dir -out_dir log_checkpoint_name -gpu-id 0

For OTTT$_O$, add the argument -online\_update as:

python train_cifar.py -data_dir path_to_data_dir -dataset cifar10 -out_dir log_checkpoint_name -gpu-id 0 -online_update

The default hyperparameters in the code are the same as in the paper.

Note: Current codes only support single-gpu training.

## Testing
We provide the example code to calculate the firing rate statistics during evaluation. Run as following:

python get_rate_cifar.py -data_dir path_to_data_dir -dataset cifar10 -gpu-id 0 -resume path_to_checkpoint

python get_rate_imagenet.py -data_dir path_to_data_dir -gpu-id 0 -resume path_to_checkpoint

Some pretrained models can be downloaded from [Google Drive](https://drive.google.com/drive/folders/1eDn3mVgfBHTLBfb--WawgA5Qms4oFbZ4?usp=sharing) or [Baidu Drive](https://pan.baidu.com/s/1q0ljZiCVIUW41Hh-aol2Zg) (extraction code: gppq).

## Acknowledgement

Some codes for the neuron model and data prepoccessing are adapted from the [spikingjelly](https://github.com/fangwei123456/spikingjelly) repository, and the codes for some utils are from the [pytorch-classification](https://github.com/bearpaw/pytorch-classification) repository.

## Contact
If you have any questions, please contact <mingqing_xiao@pku.edu.cn>.

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