Text-to-Image Models for Counterfactual Explanations: a Black-Box Approach

This is the official code for the WACV 2024 paper Text-to-Image Models for Counterfactual Explanations: a Black-Box Approach.

Set up your working space

Environment

Create and activate the conda environment.

conda env create -f time_env.yml
conda activate time

Downloading the models and datasets

Please follow ACE's instructions (link) to download the CelebA HQ and BDD100k classification models and the other models necessary for the evaluation.

To download the BDD100k datasets here. To prepare the CelebA HQ dataset, download it here. Additionally, download the list_eval_partition.txt from this link and modify it following this comment.

Counterfactual Explanation Generation

Training

Before generating counterfactual explanations with TIME, you first need to extract the predictions of the target classifier. To do so, first, you need to run the get_predictions.py python code. The resulting output is a .csv file stored in the utils folder, where one column is the image filename and the other its respective prediction.

Once completed, you need to train the context and class-specific textual embeddings. To do so, you need to use the training.py python code. We based our code on this jupyter notebook.

To train the context embedding, run the code as follows:

DATASET=name-your-dataset
PATHDATA=/path/to/data
CONTEXTTOKENS=context.pth  # output filename
LQ=0  # query label to train on, e.g. 0 for forward/stop binary task in bdd
CUSTOMTOKENS="'|<C*1>|' '|<C*2>|' '|<C*3>|'"
INITTOKENS="centered realistic celebrity"

python training.py \
    --output_path $CONTEXTTOKENS \
    --dataset $DATASET \
    --data_dir $PATHDATA \
    --label_query $LQ --training_label -1 \
    --custom_tokens $CUSTOMTOKENS \
    --custom_tokens_init $INITTOKENS \
    --phase context \
    --mini_batch_size 1 \
    --enable_xformers_memory_efficient_attention

Here, the SD model will learn the text embeddings linked with the |<C*1>|, |<C*2>|, and |<C*3>| text code. These embeddings will be warmed-up with the embeddings coupled with the words in INITTOKENS. The output is a small .pth file containing the token code and its learned text embedding.

To train the class-related bias tokens, run the same code but change the --phase flag to class, and the --training-label to 1 or 0:

DATASET=name-your-dataset
PATHDATA=/path/to/data
CONTEXTTOKENS=context.pth  # output filename
CLASSTOKEN0=class-0.pth
LQ=0
NEGTOKENS="'|<AN*1>|' '|<AN*2>|' '|<AN*3>|'"
INITTOKENS="serious serious serious"


# training tokens for binary task $LQ with prediction 1
python training.py \
    --embedding-files $CONTEXTTOKENS \
    --output_path $CLASSTOKEN0 \
    --dataset $DATASET \
    --data_dir $PATHDATA \
    --label_query $LQ --training_label 0 \
    --custom_tokens $NEGTOKENS \
    --custom_tokens_init $INITTOKENS \
    --phase class \
    --mini_batch_size 1 \
    --base_prompt 'A |<C*1>| |<C*2>| |<C*3>| photo' \
    --enable_xformers_memory_efficient_attention


CLASSTOKEN1=class-1.pth
POSTOKENS="'|<AP*1>|' '|<AP*2>|' '|<AP*3>|'"
INITTOKENS="smile smile smile"

# training tokens for binary task $LQ with prediction 0
python training.py \
    --embedding-files $CONTEXTTOKENS \
    --output_path $CLASSTOKEN1 \
    --dataset $DATASET \
    --data_dir $PATHDATA \
    --label_query $LQ --training_label 1 \
    --custom_tokens $POSTOKENS \
    --custom_tokens_init $INITTOKENS \
    --phase class \
    --mini_batch_size 1 \
    --base_prompt 'A |<C*1>| |<C*2>| |<C*3>| photo' \
    --enable_xformers_memory_efficient_attention

Generation

Generating the explanations is straightforward. You need to use the generate-ce.py python file as follows:

CONTEXTTOKENS=context.pth
CLASSTOKEN0=class-0.pth
CLASSTOKEN1=class-1.pth
STEPS="15 20 25 35"
GS="4 4 4 4"
OUTPUTPATH=/path/to/results
LABEL_QUERY=31
LABEL_TARGET=-1
CLASSIFIERPATH=/path/to/classifier/weights

python generate-ce.py \
    --embedding_files $CONTEXTTOKENS $CLASSTOKEN0 $CLASSTOKEN1 \
    --use_negative_guidance_denoise \
    --use_negative_guidance_inverse \
    --guidance-scale-denoising $GS \
    --guidance-scale-invertion $GS \
    --num_inference_steps $STEPS \
    --output_path $OUTPUTPATH \
    --exp_name $EXPNAME \
    --label_target $LABEL_TARGET \
    --label_query $LABEL_QUERY \
    --neg_custom_token '|<AN*1>| |<AN*2>| |<AN*3>|' \
    --pos_custom_token '|<AP*1>| |<AP*2>| |<AP*3>|' \
    --base_prompt 'A |<C*1>| |<C*2>| |<C*3>| photo' \
    --chunks $CHUNKS --chunk $CHUNK \
    --enable_xformers_memory_efficient_attention \
    --partition 'val' --dataset $DATASET \
    --data_dir $PATHDATA \
    --classifier_path $CLASSIFIERPATH

The output system filenames are equal to the one in our previous paper Adversarial Visual Counterfactual Explanations. In this case, STEPS is the noise inversion level, and GS is the gradient scale.

Evaluation

We evaluate our pipeline using Adversarial Visual Counterfactual Explanations code.

Citation

If you find our code or paper useful, please cite our work

@InProceedings{Jeanneret_2024_WACV,
      title     = {Text-to-Image Models for Counterfactual Explanations: a Black-Box Approach}, 
      author    = {Guillaume Jeanneret and Loïc Simon and Frédéric Jurie},
      booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
      month     = {January},
      year      = {2024}
}

guillaumejs2403/TIME