Skip to content

Bremen Big Data Challenge 2022 - Code of the team Kornstante

Notifications You must be signed in to change notification settings

HughIdiyit/BBDC-2022

Repository files navigation

BBDC 2022: A guide for executing the code of the team Kornstante

  • Firstly, the requirements given in requirements.txt have to be installed.
  • The data given in the challenge has to be located in a data directory within the root directory.

General approach

  • Mocap: Was solved using a one layer LSTM, with a history of 105 and prediction length of one frame.
  • Video: Was treated as an inbetweening problem. Two convolutional LSTMs were trained. One predicts in the forward direction, the other one predicts in the backward direction.
    • ConvLSTM forward: Takes 15 frames in front of a given gap as it's history and predicts the following 5 frames.
    • ConvLSTM backward: Firstly, the data is reversed along the time axis. The network takes 15 frames after a gap as it's history and predicts the 5 frames in front of the history frames.
  • The mocap and video prediction are carried out using a sliding window over the missing frames.

Training the models

  1. Run the preprocessing script preprocessing.py
    • The sequence lengths for the model input and label are passed as command-line arguments
    • The arguments are parsed and passed to the function calls
  2. The mocap model is trained in the mocap_train.py file
    • The training is configured via global variables in the file, e.g. BS for the batch size
    • The trained model is saved to the best_model_mocap folder
  3. Training the video prediction model
    • The video prediction is a bi-directional temporal reconstruction
    • The "forward" model is trained in video_train.py
    • The "reverse" model is trained via reverse_video_train.py
    • Both trained models are saved to their respective folders
      • torch_models/convlstm/ and torch_models/reverse_convlstm/

Executing the prediction

  • Run preprocessing.py

Mocap Prediction

  1. Run mocap_prediction.py
  2. Run mocap_postprocessing.py

Video Prediction

  1. Run vid_pred_twosided.py

Releases

No releases published

Packages

No packages published

Languages