rishic3 / frowning-bias Goto Github PK

Final Report: ./Final_Project_Submission.ipynb

Setup: mamba env create -f environment.yml conda env create -f environment.yml If you are using local GPU, use ./environment_GPU.yml instead.

Links:

• https://www.ugrad.cs.jhu.edu/~dlu17/README.txt
• https://www.ugrad.cs.jhu.edu/~dlu17/list_patition_label.txt
• https://www.ugrad.cs.jhu.edu/~dlu17/aligned_train.zip
• https://www.ugrad.cs.jhu.edu/~dlu17/aligned_test.zip
• https://www.ugrad.cs.jhu.edu/~dlu17/original.zip
• https://drive.google.com/drive/folders/0B4E10azXECctYU5zYXRnbDdtMWM?resourcekey=0-hF-Mu5W-DSA2NmmEPXSTSg

Directory Structure: see ./tree.txt

Splits:

• baseline splits at ./data/RAF/splits. generated by split.py

Notebooks:

• EDA available at ./notebooks/raf_eda.

How to use this repository:

• Downloading data: Run the ./download_data.py file. See ./notebooks/colab.ipynb for an example of setting up the local directory on colab using our scripts.
• Split dataset: Run the ./split.py file. It is random seeded, so should produce same scripts as used in our project.
• Exploratory data analysis: See ./notebooks/raf_eda.ipynb.
• Hyperparameter search: Run the script ./scripts/hparam.sh. Update the for loop to include the hyperparameters of interest. This file runs train.py and saves the models/stats to the specified results folder. (train.py uses the train/val "subsplit" instead of the original train/test split, this is only used for hparam searching)
• Model training & evaluation:
  • Baseline: Run the train_all.py script with specified settings. See ./scripts/baseline/train_baseline.sh for an example. Evaluation script available at ./scripts/baseline/eval_baseline.sh.
  • Resampling Technique: Run the train_all.py script but now give parameters --equalized-by and --equalized-how flags. See ./scripts/fairness1/train.sh for an example. Evalution script available at ./scripts/fairness1/eval_single_attr.sh.
  • Pixel Inversion: Run the train_all_inversion.py script.
  • Attribute Aware: Run the train_all_attraware.py script. See ./scripts/fairness3 for examples.
  • Affirmative Action: Run the train_all_affraction.py script. See ./scripts/fairness2/train_affraction.sh and ./scripts/fairness2/eval_affraction.sh.
  • Note: for all prediction & evaluation, you will need to update model names with your own local names. Use the provided scripts as a template.
• Statistical analysis & figures: generated externally using Google Sheets. See here.

Credits:

1. https://github.com/zachmurphy1/transformer-radiographs