Awesome

ACE:

This is the codebase for the CVPR 2023 paper Adversarial Counterfactual Visual Explanations.

Environment

Through anaconda, install our environment:

conda env create -f environment.yaml
conda activate ace

Downloading pre-trained models

To use ACE, you must download the pretrained DDPM models. Please extract them to a folder of choice /path/to/models. We provide links and instructions to download all models.

Download Link:

• CelebA Models
  • Classifier: Link
  • Diffusion Model: Link
• CelebaA HQ
  • Diffusion Model: Link
  • Classifier: Please download checkpoints_decision_densenet.tar.gz . The classifier is the celebamaskhq/checkpoint.tar. Link
• BDDOIA/100k
  • Diffusion Model: :warning: This DDPM can only generate images from a warm-up stage. Link
  • Classifier: Please download checkpoints_decision_densenet.tar.gz . The classifier is the bdd/checkpoint.tar. Link
• ImageNet:
  • Diffusion Model: download the 256x256 diffusion (not class conditional) DDPM model through the openai/guided-diffusion repo. Link.
  • Classifier: we used the pretrained models given by PyTorch.
• Evaluation Models
  • CelebA Oracle: Link
  • CelebA HQ Oracle: Please download checkpoints_oracle_attribute.tar.gz. The classifier is the celebamaskhq/checkpoint.tar. Link
  • VGGFace2 Model: Please download the resnet50_ft model. Link.
  • SiamSiam Model: Please download the ResNet-50 model trained with a batch size of 256. Link

Generating Adversarial Counterfactual Explanations

To generate counterfactual explanations, use the main.py python script. We added a commentary to every possible flag for you to know what they do. Nevertheless, in the script folder, we provided several scripts to generate all counterfactual explanations using our proposed method: ACE.

We follow the same folder ordering as DiME. Please see all details in DiME's repository. Similarly, we took advantage of their multi-chunk processing -- more info in DiME's repo.

To reduce the GPU burden, we implemented a checkpoint strategy to enable counterfactual production on a reduced GPU setup. --attack_joint_checkpoint True sets this modality on. Please check this repo for a nice explanation and visualization. The flag --attack_checkpoint_backward_steps n uses n DDPM iterations before computing the backward gradients. It is 100% recommended to use a higher --attack_checkpoint_backward_steps value and a batch size of 1 than --attack_checkpoint_backward_steps 1 and a larger batch size!!!

When you finished processing all counterfactual explanations, we store the counterfactual and the pre-explanation. You can easily re-process the pre-explanations using the postprocessing.py python script.

Evaluating ACE

We provided a generic code base to evaluate counterfactual explanation methods. All evaluation script filenames begin with compute. Please look at their arguments on each individual script. Notes:

• All evaluations are based on the file organization created with our file system.
• compute_FID and compute_sFID are bash scripts. The first argument is the output_path as in the main script. The second one is the experiment name. We implemented a third one, a temporal folder where everything will be computed - useful for testing multiple models at the same time.

Citation

Is you found our code useful, please cite our work:

@inproceedings{Jeanneret_2023_CVPR,
    author    = {Jeanneret, Guillaume and Simon, Lo\"ic and Fr\'ed\'eric Jurie},
    title     = {Adversarial Counterfactual Visual Explanations},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023}
}

Code Base

We based our repository on our previous work Diffusion Models for Counterfactual Explanations.