Minimal code for running inference on spiking neural network trained for Event-based Video Reconstruction via Potential-assisted Spiking Neural Network, CVPR2022.

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Requirements

• Python >= 3.7 (3.9 recommended)
• PyTorch >= 1.6 (1.9 recommended)

• Spikingjelly = 0.0.0.0.6

  Running with Anaconda

  cuda_version=10.2 conda create -n snnrec conda activate snnrec conda install -y pytorch torchvision cudatoolkit=$cuda_version -c pytorch conda install pandas

Install Spikingjelly

pip install spikingjelly==0.0.0.0.6

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Inference

Usage: python rec_snn.py [-network NETWORK] [-path_to_pretrain_models PATH_TO_PRETRAIN_MODELS] [-path_to_event_files PATH_TO_EVENT_FILES] [-save_path SAVE_PATH] [-height HEIGHT] [-width WIDTH] [-num_events_per_pixel NUM_EVENTS_PER_PIXEL]

For example, to run EVSNN: python rec_snn.py -network EVSNN_LIF_final -path_to_pretrain_models ./pretrained_models/EVSNN.pth

To run PA-EVSNN python rec_snn.py -network PAEVSNN_LIF_AMPLIF_final -path_to_pretrain_models ./pretrained_models/PAEVSNN.pth

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Folder Structure

├── results/ - generated results are saved here |
└── utils/ - small utility functions ├── util.py └── ...