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Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021

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SNN_Calibration

Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021

Paper Version 1: [PMLR], [arXiv].

Paper Version 2: [IJCV].

When converting ANNs to SNNs, conventional methods ensure the minimization in parameter space, while we focus on the minimization in network output space:

introduction_figure

Feature Comparison of SNN calibration:

Features SNN Direct Training ANN-SNN Conversion SNN Calibration
Accuracy (T<100​) High Low High
Scalability to ImageNet Tiny Large Large
Training Speed Slow Fast Fast
# Required Data Full-set
(1.2M For ImageNet)
~1000 ~1000
Inference Speed Fast Slow Fast

Requirements

Pytorch 1.8

For ImageNet experiments, please be sure that you can initialize distributed environments

For CIFAR experiments, one GPU would suffice.

Update (May 31, 2022): New version of the code

We released a new version of the paper, and will update the code to match the experiments with that paper.

Update (Jan 14, 2022): ImageNet experiments

For imagenet experiments, please first download the checkpoints from Google Drive.

We recommend initializing distributed learning environments, and utlizing multi-GPU calibration.

For reproducibility, 8 GPUs are per run and distributed environments are highly encouraged.

For example:

python main_cal_imagenet.py --arch res34 --T 32 --usebn --calib light --dpath PATH/TO/DATA

Pre-training ANN on CIFAR10&100

Train an ANN model with main_train_cifar.py

python main_train_cifar.py --dataset CIFAR10 --arch VGG16 --usebn 

Pre-trained results:

Dataset Model Random Seed Accuracy
CIFAR10 VGG16 1000 95.76
CIFAR10 ResNet-20 1000 95.68
CIFAR100 VGG16 1000 77.98
CIFAR100 ResNet-20 1000 76.52

SNN Calibration on CIFAR10&100

Calibrate an SNN with main_cal_cifar.py.

python main_cal_cifar.py --dataset CIFAR10 --arch VGG16 --T 16 --usebn --calib advanced --dpath PATH/TO/DATA

--T is the time step, --calib is the calibration method, please use none, light, advanced for experiments.

The calibration will run 5 times, and return the mean accuracy as well as the standard deviation.

Example results:

Architecture Dataset T Random Seed Calibration Mean Acc Std.
VGG16 CIFAR10 16 1000 None 64.52 4.12
VGG16 CIFAR10 16 1000 Light 93.30 0.08
VGG16 CIFAR10 16 1000 Advanced 93.65 0.25
ResNet-20 CIFAR10 16 1000 None 67.88 3.63
ResNet-20 CIFAR10 16 1000 Light 93.89 0.20
ResNet-20 CIFAR10 16 1000 Advanced 94.33 0.12
VGG16 CIFAR100 16 1000 None 2.69 0.76
VGG16 CIFAR100 16 1000 Light 65.26 0.99
VGG16 CIFAR100 16 1000 Advanced 70.91 0.65
ResNet-20 CIFAR100 16 1000 None 39.27 2.85
ResNet-20 CIFAR100 16 1000 Light 73.89 0.15
ResNet-20 CIFAR100 16 1000 Advanced 74.48 0.16

If you feel this repo helps you, please consider citing the following articles:

@article{li2022converting,
  title={Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration},
  author={Li, Yuhang and Deng, Shikuang and Dong, Xin and Gu, Shi},
  journal={arXiv preprint arXiv:2205.10121},
  year={2022}
}

@article{li2024error,
  title={Error-Aware Conversion from ANN to SNN via Post-training Parameter Calibration},
  author={Li, Yuhang and Deng, Shikuang and Dong, Xin and Gu, Shi},
  journal={International Journal of Computer Vision},
  pages={1--24},
  year={2024},
  publisher={Springer}
}

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