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implementation of "Action Quality Assessment with Temporal Parsing Transformer"

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# aqa_tpt
implementation of "Action Quality Assessment with Temporal Parsing Transformer"

## todo
- [ ] pretrain model on MTL-AQA
- [ ] test script

## Usage

### Requirement
- Python >= 3.8
- Pytorch >= 1.7.1
- cuda 10.1

### Dataset Preparation
- Please download the dataset from the [[link]](https://durhamuniversity-my.sharepoint.com/:u:/g/personal/fsvd68_durham_ac_uk/EfCexAQT19xArquObWijcaAB3xgRUpps50vfbezDh9wgAA?e=OOuVUH), and unzip the file. (the processed data is based on this [repo](https://github.com/yuxumin/CoRe))
- put the folder named "data_preprocessed" in ./data
- put the file named "data_preprocessed/model_rgb.pth" in ./data

The data structure should be:
```
$DATASET_ROOT
├── model_rgb.pth
├── data_preprocessed/
    ├── MTL_AQA/
        ├── frames_long
            ├── 01_01/
                ├── 00017977.jpg
                ...
            ...
            └── 07_25/
                ├── 00040170.jpg
                ...
        ├── info
            ├── final_annotations_dict_with_dive_number
            ├── test_split_0.pkl
            └── train_split_0.pkl
    ├── AQA_7/
        ├── frames
            ├── diving-out
                ├── 001
                    ├── img_00001.jpg
                    ...
            ...
        ├── info
            ├── split_4_test_list.mat
            ├── split_4_train_list.mat
```

### how to train
```
python -u -m torch.distributed.launch --nproc_per_node=8 train_pairencode1_decoder_1selfatt_self8head_ffn_sp_new.py --epoch_num=250 --dataset=MLT_AQA --bs_train=3 --bs_test=3 --use_pretrain=False --num_cluster=5 --margin_factor=3.2 --encode_video=False --hinge_loss=True --multi_hinge=True --d_model=512 --d_ffn=512 --exp_name=sp_new_103_5_3
```

## Acknowledgement
Our code is based on [CoRe](https://github.com/yuxumin/CoRe). Thanks for their great work!

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