Explainability Requires Interactivity

This repository contains the code to train all custom models used in the paper Explainability Requires Interactivity as well as to create all static explanations (heat maps and generative). For our interactive framework, see the sister repositor.

Precomputed generative explanations are located at static_generative_explanations.


Install the conda environment via conda env create -f env.yml (depending on your system you might need to change some versions, e.g. for pytorch, cudatoolkit and pytorch-lightning).

For some parts you will need the FairFace model, which can be downloaded from the authors’ repo. You will only need the res34_fair_align_multi_7_20190809.pt file.

Training classification networks

CelebA dataset

You first need to download and decompress the CelebAMask-HQ dataset (or here). Then run the training with

python train.py --dset celeb --dset_path /PATH/TO/CelebAMask-HQ/ --classes_or_attr Smiling --target_path /PATH/TO/OUTPUT

/PATH/TO/FLOWERS102/ should contain a CelebAMask-HQ-attribute-anno.txt file and an CelebA-HQ-img directory. Any of the columns in CelebAMask-HQ-attribute-anno.txt can be used; in the paper we used Heavy_Makeup, Male, Smiling, and Young.

Flowers102 dataset

You first need to download and decompress the Flowers102 data. Then run the training with

python train.py --dset flowers102 --dset_path /PATH/TO/FLOWERS102/ --classes_or_attr 49-65 --target_path /PATH/TO/OUTPUT/

/PATH/TO/FLOWERS102/ should contain an imagelabels.mat file and an images directory. Classes 49 and 65 correspond to the “Oxeye daisy” and “California poppy”, while 63 and 54 correspond to “Black-eyed Susan” and “Sunflower” as in the paper.

Generating heatmap explanations

Heatmap explanations are generated using the Captum library. After training, run explanations via

python static_exp.py --model_path /PATH/TO/MODEL.pt --img_path /PATH/TO/IMGS/ --model_name celeb --fig_dir /PATH/TO/OUTPUT/

/PATH/TO/IMGS/ contains (only) image files and can be omitted in order to run the default images exported by train.py. To run on FairFace, choose --model_name fairface and add --attr age or --attr gender. Other explanation methods can be easily added by modifying the explain_all function in static_exp.py. Explanations are saved to fig_dir. Only tested for the networks trained on the facial images data in the previous step, but any resnet18 with scalar output layer should work just as well.

Generating generative explanations

First, clone the original NVIDIA StyleGAN2-ada-pytorch repo. Make sure everything works as expected (e.g. run the getting started code). If the code is stuck at loading TODO, usually ctrl-C will let the model fall back to a smaller reference implementation which is good enough for our use case. Next, export the repo into your PYTHONPATH (e.g. via export PYTHONPATH=$PYTHONPATH:/PATH/TO/stylegan2-ada-pytorch/). To generate explanations, you will need to 0) train an image model (see above, or use the FairFace model); 1) create a dataset of latent codes + labels; 2) train a latent space logistic regression models; and 3) create the explanations. As each of the steps can be very slow, we split them up

Create labeled latent dataset

First, make sure to either train at least one image model as in the first step and/or download the FairFace model.

python generative_exp.py --phase 1 --attrs Smiling,ff-skin-color --base_dir /PATH/TO/BASE/ --generator_path /PATH/TO/STYLEGAN2.pkl --n_train 20000 --n_valid 5000

The base_dir is the directory where all files/sub-directories are stored and should be the same as the target_path from train.py (e.g., just .). It should contain e.g. the celeb-Smiling directory and the res34_fair_align_multi_7_20190809.pt file if using --attrs Smiling,ff-skin-color.

Train latent space model

After the first step, run

python generative_exp.py --phase 2 --attrs Smiling,ff-skin-color --base_dir /PATH/TO/BASE/ --epochs 50

with same base_dir and attrs.

Create generative explanations

Finally, you can generate generative explanations via

python generative_exp.py --phase 3 --base_dir /PATH/TO/BASE/ --eval_attr Smiling --generator_path /PATH/TO/STYLEGAN2.pkl --attrs Smiling,ff-skin-color --reconstruction_steps 1000 --ampl 0.09 --input_img_dir /PATH/TO/IMAGES/ --output_dir /PATH/TO/OUTPUT/

Here, eval_attr is the final evaluation model’s class that you want to explain; attrs are the same as before, the directions in latent space; input_img_dir is a directory with (only) image files that are to be explained. Explanations are saved to output_dir.