Setup Instructions

• install the conda enviroment to esure compatability of packages.

 conda create --name jbi --file jbi_env.yml

• Enviroment was developed using python3 3.9 and CUDA 12.2

Preparing datasets

• The code expects you define a pytorch dataset that is able to load 1 image at a time
• In our workflows we usually provide a csv file that contains image paths,labels , train/test grouping and other metdata
• if working on your own imaging modality create a new {modality}_factor.py where you define data loaders
• Each factory.py has a get_loaders method that is used to load specific dataloaders.
• We do this so we can have dataloaders that provide 2-3 based on our training experiments
• The code right now expects the dataloaders to output
  • Baseline Dataloaders : (image,task_label,path_to_image)
  • Debiasing Dataloaders : (image,task_label,demographic_label,path_to_image)
• if you define your own loader modify loader_factory.py to load said object.

Running HyperParameter Search

• Once a dataloader is defined we are able to run experiments using resnet18 and densenet121.
• We can run a parameter search sweep using:
• This will help us define our baseline model with "ideal" parameters.

bash ./scripts/step0_run_param_search.sh

Training Baseline Model

• There is a notebook notebooks/view_optuna_dashboard.ipynb where we can view the best hyper parameters based on validation loss
• Modify configs/skin_configs/train_skin.json with the best hyper parameters
• Train the baseline model using:

bash ./scripts/step1_run_best_models.sh

Observing TCAV in model layers

• modify configs/skin_configs/tcav_experiment_config.json to point at your data and your model weights
• Run the Following script:

bash ./scripts/step2_run_tcav.sh

• We should get an image simlar to:
• From those results our "debiasing" taret will be
• "model.features.denseblock3.denselayer16.conv2"
  • NOTE: We add the "model" because our debias training uses module that wraps around the network so
  • features.denseblock becomes model.features.denseblock

Grid search for adversarial Debiasing

• configs/skin_configs/param_searches/param_search_debias.json should have the same parameters as the baseline network training configs/skin_configs/train_skin.json. we will only search over possible lambda values.
• leaving out batch_size and optimizer will result varying optimal configurations. Resulting in unfair comparisons.

bash scripts/step3_run_debias_param_search.sh

• The notebook can be used to view the best parameters. notebooks/view_optuna_dashboard.ipynb

Run and evaluate the final model

• Once your hyper paramters are update run

bash scripts/step4_run_debi