Explaining why Lottery Ticket Hypothesis Works or Fails

Objective:

Code for the project of the course CMU 16-824: Visual Learning and Recognition. In this project, we study the relationship between pruning and explainability. We validate if the explanations generated from the pruned network using Lottery ticket hypothesis (LTH) are consistent or not. Specifically we prune a neural network using LTH. Next we generate and compare the local and global explanations using Grad-CAM and Concept activations respectively. Overview of our method is as follows:

Report

Follow the link.

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

MNIST

• Download and create the even / odd dataset using the following command:

cd scripts_data
python download_mnist_E_O.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 MNIST Even / odd, check config/BB_mnist.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 --config "config/BB_cub.yaml"

Step 2: Test the pruned models to record the performance

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

Step 3: Save the activations as image embeddings to train for CAVs

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

Step 4: Train classifier using the image embeddings to obtain CAVs

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

Step 5: Estimate TCAV score for the global explanations

Edit labels_for_tcav and concepts_for_tcav parameters in the file config/BB_cub.yaml for the desired class label and concept label to generate the TCAV score for

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

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.

Pre-trained models

Get the pretrained models following the link . This link contains all check points of all the pre-trained pruned models for CUB-200 dataset. Download it and give the path till results in log parameter ./config/BB_cub.yaml file.

Results

Performance of the pruned models by LTH

Local explanations by Grad-CAM

Global explanations by Grad-CAM

Contact

shawn24@bu.edu (preferred), shg121@pitt.edu, beingshantanu2406@gmail.com (personal), shantan2@andrew.cmu.edu

Licensed under the MIT License

Copyright (c) Shantanu , 2022