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ShuttleNet

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Official Implementation for ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton (AAAI'22)

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# ShuttleNet: Position-aware Rally Progress and Player Styles Fusion for Stroke Forecasting in Badminton (AAAI 2022)
Official code of the paper **ShuttleNet: Position-aware Rally Progress and Player Styles Fusion for Stroke Forecasting in Badminton**.
Paper: https://arxiv.org/abs/2112.01044

## Update
This repo has been moved to [CoachAI Projects](https://github.com/wywyWang/CoachAI-Projects) and will no longer be maintained.

## Overview
ShuttleNet contains two encoder-decoder modified Transformer as extractors, and a position-aware gated fusion network for fusing these contexts to tackle stroke forecasting in badminton.

## Setup
- Build environment
    ```
    conda env create -f environment.yml
    ```
- Put data in `data/dataset.csv`

## Run script
- To run all the cases, run the script `script_all.sh`:
    ```
    ./script_all.sh {model_type} {sample_num}
    ```
- To run the case with different seeds, run the script `script_seed.sh`:
    ```
    ./script_all.sh {model_type} {encode_length} {sample_num}
    ```
- To run the case with the specific seed and default samples, run the script `script_single.sh`:
    ```
    ./script_single.sh {model_type} {encode_length} {seed_value}
    ```
- You may need to modify model_type in `train.py` choices.

## Output files
- Trained models will be saved in the folder `model/{datetime}_{model_type}/`
    - Details of performance will also be contained in folder named `result.log`
- The performance will be saved in corresponding log file named {model_type}\_result\_{encode_length}

## Code structure
### Training
```=python
python train.py {model_type}
```

### Evaluate
```=python
python evaluate.py {model_path}
```

### Generate
```=python
python generator.py {model_path}
```

## Citation
If you use our dataset or find our work is relevant to your research, please cite:
```
@misc{ShuttleNet_2022_AAAI,
    author = {Wei-Yao Wang, Hong-Han Shuai, Kai-Shiang Chang, Wen-Chih Peng},
    title = {ShuttleNet: Position-aware Fusion of Rally Progress and Player Styles for Stroke Forecasting in Badminton},
    year = {2021}
    eprint = {2112.01044},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}
```

ShuttleNet

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