Implement Deep Learning based Rppg Model using pytorch

model list

• DeepPhys : DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks
• MTTS :Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement
  • need to verification
• DeepPhys + LSTM
• 3D physNet : Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks

file list

• dataset  :  related to dataset

  • dataset_loader.py  :  pytorch.utils.dataset stored dataset file load(.hpy)
  • __NetworkName__Dataset.py  :  Customized dataset to fit each model.

• nets  :  related to Network Architecture
  ( funcs < layers < blocks < modules <= sub_models <= models)

  • blocks
  • funcs
  • layers
  • models
    • sub_models
  • modules

• log.py  :  custom log functions

• loss.py  :  available loss list & custom loss functions

• optim.py  :  available optimizer list & custom optimizer functions

• main.py

• params.json  : List of options for training

preprocessor list

• __TIME__  :  check features running time

  • preprocessing time
  • model init time
  • setting loss func time
  • setting optimizer time
  • training time per 1epoch
  • inference time per 1 batch

• __PREPROCESSING__  :  perform preprocessing before training & generate preprocessed file(.hpy)

• __MODEL_SUMMARY__  :  print model architecture summary using torchsummary

Usages

1. modify params.json

example
  "model_params":
    {
        "name": "DeepPhys",
        "name_comment":
                [
                    "DeepPhys",
                    "PhysNet"
                ]
    }

2. run main.py

Contacts

TVSTORM inc.
Kim Dae Yeol                               Kim Jin Soo
wagon0004@tvstorm.com          wlstn25092303@tvstorm.com

Funding

This work was supported by the ICT R&D program of MSIP/IITP. [2021(2021-0-00900), Adaptive Federated Learning in Dynamic Heterogeneous Environment]

##reference

1. ZitongYu/PhysNet