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Differentiation on Spike Representation

This repository is the official PyTorch implementation of the paper: Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation (CVPR 2022).

Dependencies

• Python >= 3.6
• PyTorch >= 1.7.1
• torchvision
• Python packages: pip install numpy matplotlib progress

Training

Please run the following code. The hyperparameters in the code are the same as in the paper.

CIFAR-10

python -u cifar/main_single_gpu.py --path ./data --dataset cifar10 --model preresnet.resnet18_lif --name [checkpoint_name]

python -u cifar/main_single_gpu.py --path ./data --dataset cifar10 --model preresnet.resnet18_if --Vth 6 --alpha 0.5 --Vth_bound 0.01 --name [checkpoint_name]

CIFAR-100

python -u cifar/main_single_gpu.py --path ./data --dataset cifar100 --model preresnet.resnet18_lif --name [checkpoint_name]

python -u cifar/main_single_gpu.py --path ./data --dataset cifar100 --model preresnet.resnet18_if --Vth 6 --alpha 0.5 --Vth_bound 0.01 --name [checkpoint_name]

DVS-CIFAR10

python -u cifar/main_single_gpu.py --path ./data/CIFAR10DVS --dataset CIFAR10DVS --model vgg.vgg11_lif --lr=0.05 --epochs=300 --name [checkpoint_name]

python -u cifar/main_single_gpu.py --path ./data/CIFAR10DVS --dataset CIFAR10DVS --model vgg.vgg11_if --Vth 6 --alpha=0.5 --Vth_bound 0.01 --lr=0.05 --epochs=300 --name [checkpoint_name]

Low Latency Setting

DSR can achieve good results for low latency (e.g., T=5) by tuning the hyperparameters. The code for training with T=5 on CIFAR-10 is shown below. The accuracy is near 94.45% (Fig.3 in the paper). For other datasets, please reduce lr and tune Vth for better performance.

python -u cifar/main_single_gpu.py --path ./data --dataset cifar10 --model preresnet.resnet18_lif --timesteps 5  --lr 0.05 --Vth 0.6 --alpha 0.5 --Vth_bound 0.001 --delta_t 0.1

python -u cifar/main_single_gpu.py --path ./data --dataset cifar10 --model preresnet.resnet18_if --timesteps 5 --lr 0.05 --Vth 6 --alpha 0.5 --Vth_bound 0.01 

Training with Multiple GPUs

For the CIFAR-10, CIFAR-100, and DVS-CIFAR10 tasks, multiple GPUs can also be used. The example code is shown below.

python -u -m torch.distributed.launch --nproc_per_node [number_of_gpus] cifar/main_multiple_gpus.py --path ./data --dataset cifar10 --model preresnet.resnet18_lif --name [checkpoint_name]

ImageNet

For the ImageNet classification task, we conduct hybrid training.

First, we train an ANN.

python imagenet/main.py --arch preresnet_ann.resnet18 --data ./data/imagenet --name model_ann --optimizer SGD --wd 1e-4  --batch-size 256 --lr 0.1

Then, we calculate the maximum post-activation as the initialization for spike thresholds.

python imagenet/main.py --arch preresnet_cal_Vth.resnet18  --data ./data/imagenet --pre_train model_ann.pth --calculate_Vth resnet18_Vth

Next, we train the SNN.

python imagenet/main.py --dist-url tcp://127.0.0.1:20500 --dist-backend nccl --multiprocessing-distributed --world-size 1 --rank 0 --arch preresnet_snn.resnet18_if  --data ./data/imagenet --pre_train model_ann.pth --load_Vth resnet18_Vth.dict

The pretrained ANN model and calculated thresholds can be downloaded from here and here. Please put them in the path ./checkpoint/imagenet.

Credits

The code for the data preprocessing of DVS-CIFAR10 is based on the spikingjelly repo. The code for some utils are from the pytorch-classification repo.