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Sparse Visual Counterfactual Explanations in Image Space

Welcome to the codebase for our paper Sparse Visual Counterfactual Explanations in Image Space. We will guide you through the process of generating your own l1.5 visual counterfactual explanations on ImageNet with the multiple norm robust model Madry + FT.

Examples of l1.5 VCEs for ImageNet

Setup

Before we can start with the generation, we have to setup the project and install required packages.

  • Start by extracting the content of the .zip file that also contains this readme.md somewhere on your computer. We will refer to the extraction directory as project_path.

  • Navigate into the project_path

  • Download the weights for Madry + FT from here into your project_path

  • Unzip the model file via unzip madry_ft.zip

  • Open utils/datasets/paths.py and adjust the base_data_path in line 6, the default value is /scratch/datasets/. Note that we assume that you have extracted ILSVRC2012 to base_data_path/imagenet. If this does not match your folder layout, you can also directly manipulate get_imagenet_path in line 64. For example if your dataset is located in /home/user/datasets/ilsvrc2012/ you could change the function to:

    def get_imagenet_path():  
    path = '/home/user/datasets/ilsvrc2012/' 
    return path

  • Create a new conda env via conda env create -f reqs.yml

  • Activate the conda environment via conda activate vces

Creating VCEs

The following script will generate VCEs for samples from the ILSVRC2012 validation set using a l1.5 threat model using the Auto-Frank-Wolfe method described in the paper. The images will have the same structure as Figure 4 in the main paper and Figures 9 and 10 in the Appendix.

python imagenet_visual_transfer_hierachy.py --gpu 0 --bs 128 --samples 1 > log

Note that the --gpu argument can take multiple gpu indices, for example --gpu 4 5 6 7. The batchsize argument --bs is the number of samples per gpu, so if you encounter out-of-memory errors you can reduce it without altering results. The script searches for wordnet subsets that contain between 3 to 10 ImageNet classes and then randomly selects N images per class and creates counterfactuals in all classes in this wordnet cluster. N can be changed via --samples N , however be careful as even for N=1, the script creates VCEs for 619 images into multiple target classes. The resulting images can be found in ImageNetHierachyTransfer/l2_improved_3_ep_best/VisualCounterfactuals. The folder contains multiple individual png and pdf files that show the transformation in all classes and the folders additionally contain the individual images. The format is the following: {wordnet_cluster}_{imagenet_class}_{linear_idx}_{imagenet_val_idx}