WiFi-CSI-Sensing-Benchmark - GitLite

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WiFi-CSI-Sensing-Benchmark

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최종 버전 다운로드 (.zip)

- CSI_samples.jpg
- Models.jpg
- readme.md
- transformer_block.jpg
- Widar_classes.jpg
dataset.py
LICENSE
NTU_Fi_model.py
README.md
requirements.txt
run.py
self_supervised.py
self_supervised_model.py
UT_HAR_model.py
util.py
widar_model.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/xyanchen/WiFi-CSI-Sensing-Benchmark

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd WiFi-CSI-Sensing-Benchmark

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

python run.py

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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# SenseFi: A Benchmark for WiFi CSI Sensing
## Introduction
SenseFi is the first open-source benchmark and library for WiFi CSI human sensing, implemented by PyTorch. The state-of-the-art networks, including MLP, CNN, RNN, Transformers, etc, are evaluated on four public datasets across different WiFi CSI platforms. The details are illustrated in our paper [*SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing*](https://arxiv.org/abs/2207.07859) that has been accepted by Patterns, Cell Press.

```
@article{yang2023benchmark,
  title={SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing},
  author={Yang, Jianfei and Chen, Xinyan and Wang, Dazhuo and Zou, Han and Lu, Chris Xiaoxuan and Sun, Sumei and Xie, Lihua},
  journal={Patterns},
  volume={4},
  number={3},
  publisher={Elsevier},
  year={2023}
}
```

## Requirements

1. Install `pytorch` and `torchvision` (we use `pytorch==1.12.0` and `torchvision==0.13.0`).
2. `pip install -r requirements.txt`

**Note that the project runs perfectly in Linux OS (`Ubuntu`). If you plan to use `Windows` to run the codes, you need to modify the all the `/` to `\\` in the code regarding the dataset directory for the CSI data loading.**

## Run
### Download Processed Data
Please download and organize the [processed datasets](https://drive.google.com/drive/folders/1R0R8SlVbLI1iUFQCzh_mH90H_4CW2iwt?usp=sharing) in this structure:
```
Benchmark
├── Data
    ├── NTU-Fi_HAR
    │   ├── test_amp
    │   ├── train_amp
    ├── NTU-Fi-HumanID
    │   ├── test_amp
    │   ├── train_amp
    ├── UT_HAR
    │   ├── data
    │   ├── label
    ├── Widardata
    │   ├── test
    │   ├── train
```
We also offer [pre-trained weights](https://drive.google.com/drive/folders/1NBVe9za8ntFnkE9B1vhv4gD6eM88P1KI?usp=sharing) for all models

### Supervised Learning
To run models with supervised learning (train & test):  
Run: `python run.py --model [model name] --dataset [dataset name]`

You can choose [model name] from the model list below
- MLP
- LeNet
- ResNet18
- ResNet50
- ResNet101
- RNN
- GRU
- LSTM
- BiLSTM
- CNN+GRU
- ViT

You can choose [dataset name] from the dataset list below
- UT_HAR_data
- NTU-Fi-HumanID
- NTU-Fi_HAR
- Widar

*Example: `python run.py --model ResNet18 --dataset NTU-Fi_HAR`*
### Unsupervised Learning
To run models with unsupervised (self-supervised) learning (train on **NTU-Fi HAR** & test on **NTU-Fi HumanID**):  
Run: `python self_supervised.py --model [model name] `

You can choose [model name] from the model list below
- MLP
- LeNet
- ResNet18
- ResNet50
- ResNet101
- RNN
- GRU
- LSTM
- BiLSTM
- CNN+GRU
- ViT

*Example: `python self_supervised.py --model MLP`*  
Method: [*AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning*](https://doi.org/10.48550/arXiv.2205.01629)

## Model Zoo
### MLP
- It consists of 3 fully-connected layers followed by activation functions 
### LeNet
- **self.encoder** : It consists of 3 convolutional layers followed by activation functions and Maxpooling layers to learn features
- **self.fc** : It consists of 2 fully-connected layers followed by activation functions for classification
### ResNet
- ***class*** **Bottleneck** : Each bottleneck consists of 3 convolutional layers followed by batch normalization operation and activation functions. And adds resudual connection within the bottleneck
- ***class*** **Block** : Each block consists of 2 convolutional layers followed by batch normalization operation and activation functions. And adds resudual connection within the block
- **self.reshape** : Reshape the input size into the size of 3 x 32 x 32
- **self.fc** : It consists of a fully-connected layer
### RNN
- **self.rnn** : A one-layer RNN structure with a hidden dimension of 64
- **self.fc** : It consists of a fully-connected layer
### GRU
- **self.gru** : A one-layer GRU structure with a hidden dimension of 64
- **self.fc** : It consists of a fully-connected layer
### LSTM
- **self.lstm** : A one-layer LSTM structure with a hidden dimension of 64
- **self.fc** : It consists of a fully-connected layer
### BiLSTM
- **self.lstm** : A one-layer bidirectional LSTM structure with a hidden dimension of 64
- **self.fc** : It consists of a fully-connected layer
### CNN+GRU
- **self.encoder** : It consists of 3 convolutional layers followed by activation functions
- **self.gru** : A one-layer GRU structure with a hidden dimension of 64
- **self.classifier** : It consistis a dropout layer followed by a fully-connected layer and an activation function
 ### ViT (Transformers)
- ***class*** **PatchEmbedding** : Divide the 2D inputs into small pieces of equal size. Then concatenate each piece with cls_token and do positional encoding operation
- ***class*** **ClassificationHead** : It consists of a layer-normalization layer followed by a fully-connected layer
- ***class*** **TransformerEncoderBlock** : It consists of multi-head attention block, residual add block and feed forward block. The structure is shown below:  
<img src="./img/transformer_block.jpg" width="200"/>

## Dataset
#### UT-HAR
[*A Survey on Behavior Recognition Using WiFi Channel State Information*](https://ieeexplore.ieee.org/document/8067693) [[Github]](https://github.com/ermongroup/Wifi_Activity_Recognition)  
- **CSI size** : 1 x 250 x 90
- **number of classes** : 7
- **classes** : lie down, fall, walk, pickup, run, sit down, stand up
- **train number** : 3977
- **test number** : 996

#### NTU-HAR
[*Efficientfi: Towards Large-Scale Lightweight Wifi Sensing via CSI Compression*](https://ieeexplore.ieee.org/document/9667414)  
- **CSI size** : 3 x 114 x 500
- **number of classes** : 6
- **classes** : box, circle, clean, fall, run, walk
- **train number** : 936
- **test number** : 264

#### NTU-HumanID
[*CAUTION: A Robust WiFi-based Human Authentication System via Few-shot Open-set Gait Recognition*](https://ieeexplore.ieee.org/abstract/document/9726794)  
- **CSI size** : 3 x 114 x 500
- **number of classes** : 14
- **classes** : gaits of 14 subjects
- **train number** : 546
- **test number** : 294

*Examples of NTU-Fi data*  
<img src="./img/CSI_samples.jpg" width="1000"/>

#### Widar
[*Widar3.0: Zero-Effort Cross-Domain Gesture Recognition with Wi-Fi*](https://ieeexplore.ieee.org/document/9516988) [[Project]](http://tns.thss.tsinghua.edu.cn/widar3.0/)  
- **BVP size** : 22 x 20 x 20
- **number of classes** : 22
- **classes** :  
Push&Pull, Sweep, Clap, Slide, Draw-N(H), Draw-O(H),Draw-Rectangle(H),  
Draw-Triangle(H), Draw-Zigzag(H), Draw-Zigzag(V), Draw-N(V), Draw-O(V), Draw-1,  
Draw-2, Draw-3, Draw-4, Draw-5, Draw-6, Draw-7, Draw-8, Draw-9, Draw-10  
- **train number** : 34926
- **test number** : 8726

*Classes of Widar data*  
<img src="./img/Widar_classes.jpg" width="800"/>

#### Notice
- Please download and unzip all the datasets with Linux system in order to avoid decoding errors.
- For UT-HAR, the data file is csv format and can be loaded via our code. If you use Excel to open it, it is not readable due to the encoding format derived from the original dataset.

## Datasets Reference
```
@article{yousefi2017survey,
  title={A survey on behavior recognition using WiFi channel state information},
  author={Yousefi, Siamak and Narui, Hirokazu and Dayal, Sankalp and Ermon, Stefano and Valaee, Shahrokh},
  journal={IEEE Communications Magazine},
  volume={55},
  number={10},
  pages={98--104},
  year={2017},
  publisher={IEEE}
}

@article{yang2022autofi,
  title={AutoFi: Towards Automatic WiFi Human Sensing via Geometric Self-Supervised Learning},
  author={Yang, Jianfei and Chen, Xinyan and Zou, Han and Wang, Dazhuo and Xie, Lihua},
  journal={arXiv preprint arXiv:2205.01629},
  year={2022}
}

@article{yang2022efficientfi,
  title={Efficientfi: Towards large-scale lightweight wifi sensing via csi compression},
  author={Yang, Jianfei and Chen, Xinyan and Zou, Han and Wang, Dazhuo and Xu, Qianwen and Xie, Lihua},
  journal={IEEE Internet of Things Journal},
  year={2022},
  publisher={IEEE}
}

@article{wang2022caution,
  title={CAUTION: A Robust WiFi-based Human Authentication System via Few-shot Open-set Gait Recognition},
  author={Wang, Dazhuo and Yang, Jianfei and Cui, Wei and Xie, Lihua and Sun, Sumei},
  journal={IEEE Internet of Things Journal},
  year={2022},
  publisher={IEEE}
}

@article{zhang2021widar3,
  title={Widar3. 0: Zero-effort cross-domain gesture recognition with wi-fi},
  author={Zhang, Yi and Zheng, Yue and Qian, Kun and Zhang, Guidong and Liu, Yunhao and Wu, Chenshu and Yang, Zheng},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
  publisher={IEEE}
}  
```