This repository contains code for pre-training models using the data2vec algorithm for MAIJU smart jumpsuit data, and also code for fine-tuning the pre-trained models using labeled data. The code has been implemented using PyTorch. For a thorough description of the MAIJU smart jumpsuit, see e.g. https://www.nature.com/articles/s43856-022-00131-6.
The present data2vec implementation has been used in the following publication: E. Vaaras, M. Airaksinen, S. Vanhatalo, and O. Räsänen, "Evaluation of self-supervised pre-training for automatic infant movement classification using wearable movement sensors," in Proc. IEEE EMBC 2023, pp. 1–6.
If you use the present code or its derivatives, please cite the repository URL and/or the aforementioned publication.
Any PyTorch version newer than version 1.9.0 should work fine. You can find out how to install PyTorch here: https://pytorch.org/get-started/locally/. You also need to have Numpy and scikit-learn installed.
conf_pretrain_maiju_model_data2vec.py: Example configuration file for data2vec pre-training for MAIJU data, using the same configuration settings that were used in the present paper.conf_finetune_pretrained_maiju_model.py: Example configuration file for fine-tuning pre-trained models, using the same configuration settings that were used in the present paper.pretrain_maiju_model_data2vec.py: A script for running a single data2vec pre-training and/or using a pre-trained model to extract features.finetune_pretrained_maiju_model.py: A script for fine-tuning a pre-trained model.maiju_data_loader.py: A file containing an example data loader for simulated MAIJU data and functions for MAIJU data augmentation.maiju_nn_model.py: A file containing the neural network model implementations of the present paper.maiju_data2vec_pretrain_test_bench.py: A superscript that can be used for running and/or evaluating a number of different data2vec pre-trainings.data2vec_ema.py: A file containing the exponential moving average (EMA) implementation for the data2vec algorithm.transformer_encoder_pytorch.py: A file containing a slightly modified version of PyTorch's Transformer encoder implementation.py_conf_file_into_text.py: An auxiliary script for converting .py configuration files into lists of text that can be used for printing or writing the configuration file contents into a text file.
You can either use the command
python pretrain_maiju_model_data2vec.py
or
python pretrain_maiju_model_data2vec.py <configuration_file>
in order to run a single data2vec pre-training with one hyperparameter configuration. Using the former of these options requires having a configuration file named conf_pretrain_maiju_model_data2vec.py in the same directory as the file pretrain_maiju_model_data2vec.py. In the latter option, <configuration_file> is a .py configuration file containing the hyperparameters you want to use during pre-training.
In order to run multiple data2vec pre-trainings with different hyperparameter configurations, you can use the command
python maiju_data2vec_pretrain_test_bench.py <dir_to_configuration_files>
where <dir_to_configuration_files> is a directory containing at least one .py configuration file. This script runs through the data2vec model pre-training (pretrain_maiju_model_data2vec.py) with each of the configuration files located in <dir_to_configuration_files>, one at a time.
You can either use the command
python finetune_pretrained_maiju_model.py
or
python finetune_pretrained_maiju_model.py <configuration_file>
in order to fine-tune pre-trained models. Using the former of these options requires having a configuration file named conf_finetune_pretrained_maiju_model.py in the same directory as the file finetune_pretrained_maiju_model.py. In the latter option, <configuration_file> is a .py configuration file containing the hyperparameters you want to use during fine-tuning.