SpikeDHS

KO | EN

gitlite — search

#python #java #python3 #arduino #golang #machine-learning #rust #html #flask #javascript #seismology #nodejs

★ 16 Open GitHub ↗

This is the official code for our paper "Differentiable Hierarchical and Surrogate Gradient Search for Spiking Neural Networks, NeurIPS 2022, Spotlight"

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# SpikeDHS
This code is a demo of our NeurIPS 2022 paper (Spotlight) "Differentiable hierarchical and surrogate gradient search for spiking neural networks".

# Dataset
To proceed, please download the CIFAR10/100 dataset on your own.

# Environment
```
1. Python 3.8.*
2. CUDA 10.0
3. PyTorch 
4. TorchVision 
5. fitlog
```

# Install
Create a  virtual environment and activate it.
```shell
conda create -n SpikeDHS python=3.8
conda activate SpikeDHS
```
The code has been tested with PyTorch 1.6 and Cuda 10.2.
```shell
conda install pytorch=1.6.0 torchvision=0.7.0 cudatoolkit=10.2 -c pytorch
conda install matplotlib path.py tqdm
conda install tensorboard tensorboardX
conda install scipy scikit-image opencv
```

# Spikingjelly Installation 
Our project can also be developed by SpikingJelly. (ref: https://github.com/fangwei123456/spikingjelly)
```shell
git clone https://github.com/fangwei123456/spikingjelly.git
cd spikingjelly
python setup.py install
```

# Cofe for SpikeDHS
We provide search, decode and retrain code for CIFAR10/100.

## Search
For search procedure, execute: \
  `bash search.sh`

Once we have conducted a search, the next step is to decode the results in order to retrieve the searched architecture.

## Decode
For decode, execute: \
  `bash decode.sh`
  
Searched Architecture:
```bash
network_path_fea = [0,0,1,1,1,2,2,2] # default
cell_arch_fea = [[1, 1],
                    [0, 1],
                    [3, 2],
                    [2, 1],
                    [7, 1],
                    [8, 1]]
```
Replace the searched architecture in `LEAStereo.py`.

## Retrain
For retrain procedure, execute: \
  `bash train.sh`
  
# Paper Reference
```
@inproceedings{chedifferentiable,
  title={Differentiable hierarchical and surrogate gradient search for spiking neural networks},
  author={Che, Kaiwei and Leng, Luziwei and Zhang, Kaixuan and Zhang, Jianguo and Meng, Qinghu and Cheng, Jie and Guo, Qinghai and Liao, Jianxing},
  booktitle={Advances in Neural Information Processing Systems}
}
```

Our code is developed based on the code from papers "Hierarchical Neural Architecture Searchfor Deep Stereo Matching" and "Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation"  
code:  
https://github.com/XuelianCheng/LEAStereo  
https://github.com/NoamRosenberg/autodeeplab  
https://github.com/fangwei123456/spikingjelly

## License
This open-source project is not an official Huawei product, and Huawei is not expected to provide support for this project.

SpikeDHS

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README