OWA_Layer

Implementation of the OWA layer in Fastai.

Implementation details in the associated paper: Dominguez-Catena, I.; Paternain, D.; Galar, M. A Study of OWA Operators Learned in Convolutional Neural Networks. Appl. Sci. 2021, 11, 7195. https://doi.org/10.3390/app11167195

Requirements

This experiments were run in a conda environment created according to:

channels:
  - pytorch
  - fastai
  - anaconda
  - defaults
  - conda-forge
dependencies:
  - cudatoolkit
  - fastai::fastai=1.0.61
  - ffmpeg
  - ipykernel
  - ipywidgets
  - jupyterlab_widgets
  - matplotlib
  - numpy
  - conda-forge::opencv
  - pandas
  - python
  - pytorch-gpu
  - scikit-learn
  - torchvision
  - seaborn

Usage

The code is centered around a reference pseudoexperiment and three main experiments with OWA layers, which can be run secuentially from their associated scripts:

python exp_reference.py
python exp_init.py
python exp_feats.py
python exp_metrics.py

From there, a jupyter notebook (analyze_experiment.ipynb) processes the results and presents both the tables and figures from the paper.

Documentation

The code is still largely uncommented. We will try to improve the legibility with time, but for now, feel free to email me with any question.

Thank you for your understanding.