kritical007 / painattnnet

Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals

Transformer Encoder with Multiscale Deep Learning for Pain Classification Using Physiological Signals

Directory Structure

PainAttnNet
|   environment.yml # Requirements for conda environment
|   LICENSE
|   README.md
|   requirments.txt # Requirements for pip environment
|          
+---jq
|       jq-win64.exe
|       
\---src
    |   batch_train.sh # Training script
    |   config.json # Training configurations
    |   logger_config.json # Logger configurations
    |   parser.py # Parser for training configurations
    |   train_kfold_cv.py # Main training script
    |  __init__.py
    |   
    +---models
    |   |   main_painAttnNet.py # Main model wrapper
    |   |   module_mscn.py* # Multiscale convolutional network
    |   |   module_se_resnet.py # Squeeze-and-excitation residual network
    |   |   module_transformer_encoder.py # Transformer encoder block
    |   \   __init__.py
    |           
    +---tests # Unit tests
    |   |   test_generate_kfolds_index.py
    |   |   test_mscn.py
    |   |   test_PainAttnNet_output.py
    |   |   test_process_bioVid.py
    |   |   test_se_resnet.py
    |   |   test_transformer_encoder.py
    |   \   __init__.py
    |           
    +---trainers # Training modules
    |   |   checkpoint_handler.py # Checkpoint handler
    |   |   device_prep.py # Device preparation, CPU or GPU
    |   |   main_trainer.py # Main trainer scripts
    |   |   metrics_manager.py # Metrics manager and other metrics functions
    |   \   __init__.py
    |           
    \---utils
        |   process_bioVid.py # Data processing for BioVid
        |   utils.py # Other utility functions
        \   __init__.py

Get Started

torchaudio==0.13.0
python==3.10.8
pytorch-cuda==11.7
pytorch==1.13.0
torchvision==0.14.0
scikit-learn==1.0.1
pandas
matplotlib
openpyxl

For Linux users, install jq package via conda or pip.

For Windows users, install jq package from here, and put the jq.exe file in the local directory.

Training

Training k-fold cross validation with script

sh batch_train.sh

Training individual fold in terminal

python train_kfold_cv.py --fold_i {fold index}

You can change settings at main_painAttnNet.py for tuning model structure, config.py for training configurations and train_kfold_cv.py for others.

Dataset

The dataset is available at BioVid Heat Pain Database.