Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021

Overview

SNN_Calibration

Pytorch Implementation of Spiking Neural Networks Calibration, ICML 2021

Feature Comparison of SNN calibration:

Features SNN Direct Training ANN-SNN Conversion SNN Calibration
Accuract (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.

Pre-training ANN on CIFAR10&100

Train an ANN model with main_train.py

python CIFAR/main_train.py --dataset CIFAR10 --arch VGG16 --dpath PATH/TO/DATA --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_calibration.py.

python CIFAR/main_calibration.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 for 5 times, and return the mean accuracy as well as the standard deviation.

Example results:

Architecture Datset 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

Pre-training ANN on ImageNet

To be updaed

Owner
Yuhang Li
Research Intern at @SenseTime Group Limited
Yuhang Li
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