https://github.com/BICLab/Attention-SNN

Offical implementation of "Attention Spiking Neural Networks" (IEEE T-PAMI2023)
https://github.com/BICLab/Attention-SNN

attention-mechanism dynamic-neural-network spiking-neural-networks

Last synced: 3 months ago
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Offical implementation of "Attention Spiking Neural Networks" (IEEE T-PAMI2023)

• Host: GitHub
• URL: https://github.com/BICLab/Attention-SNN
• Owner: BICLab
• License: mit
• Created: 2023-07-09T14:45:52.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-04-08T12:03:40.000Z (7 months ago)
• Last Synced: 2024-04-17T09:13:32.354Z (7 months ago)
• Topics: attention-mechanism, dynamic-neural-network, spiking-neural-networks
• Language: Python
• Homepage: https://ieeexplore.ieee.org/abstract/document/10032591
• Size: 254 KB
• Stars: 41
• Watchers: 1
• Forks: 6
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# [Attention Spiking Neural Networks](https://ieeexplore.ieee.org/document/10032591)

# [Attention Spiking Neural Networks - Supplementary Materials](https://github.com/BICLab/Attention-SNN/issues/3)

## **Requirements**

1. Python 3.7.4

2. PyTorch 1.7.1

3. tqdm 4.56.0

4. numpy 1.19.2

## **Instructions**

### 1. DVS128 Gesture

1. Download [DVS128 Gesture](https://www.research.ibm.com/dvsgesture/) and put the downloaded dataset to /MA_SNN/DVSGestures/data, then run /MA_SNN/DVSGestures/data/DVS_Gesture.py.

```

MA_SNN

├── /DVSGestures/

│  ├── /data/

│  │  ├── DVS_Gesture.py

│  │  └── DvsGesture.tar.gz

```

2. Change the values of T and dt in /MA_SNN/DVSGestures/CNN/Config.py then run the tasks in /MA_SNN/DVSGestures.

eg:

```

python Att_SNN_CNN.py

```

3. View the results in /MA_SNN/DVSGestures/CNN/Result/.

### 2. CIFAR10-DVS

1. Download [CIFAR10-DVS](https://figshare.com/articles/dataset/CIFAR10-DVS_New/4724671/2) and processing dataset using official matlab program, then put the result to /MA_SNN/CIFAR10DVS/data.

```

MA_SNN

├── /CIFAR10DVS/

│  ├── /data/

│  │  ├── /airplane/

│  │  |  ├──0.mat

│  │  |  ├──1.mat

│  │  |  ├──...

│  │  ├──automobile

│  │  └──...

```

2. Change the values of T and dt in /MA_SNN/CIFAR10DVS/CNN/Config.py then run the tasks in /MA_SNN/CIFAR10DVS.

eg:

```

python Att_SNN.py

```

3. View the results in /MA_SNN/CIFAR10DVS/CNN/Result/.

### 3. DVSGait Dataset

1. Download [DVSGait Dataset] and put the downloaded dataset to /MA_SNN/DVSGait/data.

2. Change the values of T and dt in /MA_SNN/DVSGait/CNN/Config.py then run the tasks in /MA_SNN/DVSGait.

eg:

```

python Att_SNN_CNN.py

```

3. View the results in /MA_SNN/DVSGait/CNN/Result/.

### 4. ImageNet Dataset

We adopt the MS-SNN (https://github.com/Ariande1/MS-ResNet) as the residual spiking neural network backbone. 

1. Download [ImageNet Dataset] and set the downloaded dataset path in utils.py.

2. then run the tasks in /Att_Res_SNN.

eg:

```

python -m torch.distributed.launch --master_port=[port] --nproc_per_node=[node_num] train_amp.py -net [model_type] -b [batchsize] -lr [learning_rate]

```

3. View the results in /checkpoint and /runs.

### 5. Extra

1. The implementation of Att-VGG-SNN in https://github.com/ridgerchu/SNN_Attention_VGG

2. /module/Attention.py defines the  Attention layer and /module/LIF.py,LIF_Module.py defines LIF module.

3. The CSA-MS-ResNet104 model is available at https://pan.baidu.com/s/1Uro7IVSerV23OKbG8Qn6pQ?pwd=54tl (Code: 54tl).

   

## **Citation**

```

@ARTICLE{10032591,

  author={Yao, Man and Zhao, Guangshe and Zhang, Hengyu and Hu, Yifan and Deng, Lei and Tian, Yonghong and Xu, Bo and Li, Guoqi},

  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 

  title={Attention Spiking Neural Networks}, 

  year={2023},

  volume={45},

  number={8},

  pages={9393-9410},

  doi={10.1109/TPAMI.2023.3241201}}

```