Environment setup

Create the environment from the environment.yml file:

 conda env create -f environment.yml

Dataset

CUB-200

• Download the CUB-200 dataset from the link
• Preprocess the noisy concepts in the dataset using the following command:

cd scripts_data
python download_cub.py

Dataset splits

The train-test-val splits of all the datasets are given in the corresponding json files in the scripts_data directory.

Hyperparameters

• For CUB-200, check config/BB_cub.yml file.
• For HAM10000, check config/BB_derma.yml file

Steps to reproduce the results

• Prior to start the training process, edit data_root, json_root and logs parameters in the config file config/BB_cub.yaml to set the path of images, json files for train-test-val splits and the output to be saved respectively.
• Prior to follow the steps, refer to ./iPython/Cub-Dataset-understanding.ipynb file to understand the CUB-200 dataset. This step is optional.
• Preprocess the noisy concepts as described earlier.
• Follow the steps below for CUB-200 dataset:

Step 1: Train and prune

python main_lth_pruning.py --dataset "cub"

Step 2: Save the activations as image embeddings to train for CAVs

python main_lth_save_activations.py --dataset "cub"

Step 3: Train classifier using the image embeddings to obtain CAVs

python main_lth_generate_cavs.py --dataset "cub"

Step 4: Train PCBM

python /ocean/projects/asc170022p/shg121/PhD/Project_Pruning/main_lth_pcbm.py --dataset "cub"

Step 5: Get top-3 concepts from PCBM

python /ocean/projects/asc170022p/shg121/PhD/Project_Pruning/main_lth_get_concepts.py --dataset "cub"

Step 6: Compute Grad-CAM based saliency maps for the local explanations

Edit labels_for_tcav parameter in the file config/BB_cub.yaml for the desired class label to generate the Grad-CAM saliency maps. By default, we generate the saliency map for the 2nd image of the desired class in the test-set.

python main_heatmap_save.py --config "config/BB_cub.yaml"

Step 7: Generate plots

./iPython/Analysis-CUB_Test-GradCAM.ipynb

Bash scripts

All the bash scripts to follow steps are included in ./bash_script file.