Explanation by Progressive Exaggeration Pytorch

Unofficial PyTorch reimplementation of ICLR 2020 paper: Explanation By Progressive Exaggeration.

Paper

Installation

$ pip install -r requirements.txt

Usage

1. Prepare the dataset for training

./notebooks/PreprocessData.ipynb

2. Train a classifier. Skip this step if you have a pretrained classifier.
   Training logs of the classifier are saved at: ./$log_dir$/$name$.
   Model checkpoints of the classifier are saved at: ./checkpoints/classifier/$name$ ($log_dir$ and $name$ are defined in the corresponding config file).
   

2.a. To train a multi-label classifier on all 40 attributes

python train_classifier.py --config 'configs/celebA_DenseNet_Classifier.yaml'

2.b. To train a binary classifier on 1 attribute

python train_classifier.py --config 'configs/celebA_Young_Classifier.yaml'

3. Process the output of the classifier and create input for Explanation model by discretizing the posterior probability. The input data for the Explanation model is saved at: $log_dir$/$name$/explainer_input/

./notebooks/ProcessClassifierOutput.ipynb

4. Train explainer model. The output is saved at: $log_dir$/$name.

python train_explainer.py --config 'configs/celebA_Young_Explainer.yaml'

5. Explore the trained Explanation model and see qualitative results.

./notebooks/TestExplainer.ipynb

6. Save results of the trained Explanation model for quantitative experiments.

python test_explainer.py --config 'configs/celebA_Young_Explainer.yaml'

7. Use the saved results to perform experiments as shown in paper

./notebooks/Experiment_CelebA.ipynb