Pytorch implementation of [Spatial-Temporal Hierarchical Pooling boosted Framework].

Requirements

You will need the following to run the above:

• Pytorch 1.9.1, Torchvision 0.10.1
• Python 3.6.8, Pillow 5.4.1
• If you want to train (and don't want to wait for 4 months):
  • A decent GPU
  • All the required NVIDIA software to run PyTorch on a GPU (cuda, etc)

Dataset

We use three datasets to evaluate our method, including C-MAPSS, UCI HAR, and ISRUC-S3.

C-MAPSS

You can access here, and put the downloaded dataset into directory 'CMAPSSData'.

For running the experiments on C-MAPSS, directly run main.py

UCI HAR

You can access here, and put the downloaded dataset into directory 'HAR'.

For running the experiments on UCI-HAR, you need to first run preprocess_UCI_HAR.py to pre-process the dataset. After that, run main.py

ISRUC-S3

You can access here, and download S3 and put the downloaded dataset into directory 'ISRUC'.

For running the experiments on UCI-HAR, you need to first run preprocess_ISRUC.py to pre-process the dataset. After that, run main.py

Acknowledgement

We thank the codes of preprocessing for UCI-HAR and ISRUC-S3.