This repo holds the code for: Spatial Temporal Graph Attention Network for Skeleton-Based Action Recognition(pdf)
- NTU-60
- Download the NTU-60 data from the https://github.com/shahroudy/NTURGB-D
- Generate the train/test splits with
python prepare/ntu_60/gendata.py
- NTU-120
- Download the NTU-120 data from the https://github.com/shahroudy/NTURGB-D
- Generate the train/test splits with
python prepare/ntu_120/gendata.py
- Knietics-400
- Download the data from ST-GCN repo: https://github.com/yysijie/st-gcn/blob/master/OLD_README.md#kinetics-skeleton
- Generate the train/test splits with
python prepare/kinetics_gendata.py
Change line 10 in train.py to the absolute path of this STGAT repo you download.
Set the data_path and label_path to the path of your dataset in line 4,5,13,14, and specify the cuda_visible_device & device_id in line 61, 62, of /train_val_test/config/your_dataset/your_config_file.yaml, respectively.
Change the config file depending on what you want.
`python train_val_test/train.py -config ./train_val_test/config/your_dataset/your_config_file.yaml`
Train with decoupled modalities by changing the 'num_skip_frame'(None to 1 or 2) option and 'decouple_spatial'(False to True) option in config file and train again.
Then combine the generated scores with:
`python train_val_test/ensemble.py`