Official PyTorch Implementation
Yossi Gandelsman, Alexei A. Efros and Jacob Steinhardt
We provide an environment.yml file that can be used to create a Conda environment:
conda env create -f environment.yml
conda activate prsclipTo obtain the projected residual stream components for the ImageNet validation set, including the contributions from multi-head attentions and MLPs, please run one of the following instructions:
python compute_prs.py --dataset imagenet --device cuda:0 --model ViT-H-14 --pretrained laion2b_s32b_b79k --data_path <PATH>
python compute_prs.py --dataset imagenet --device cuda:0 --model ViT-L-14 --pretrained laion2b_s32b_b82k --data_path <PATH>
python compute_prs.py --dataset imagenet --device cuda:0 --model ViT-B-16 --pretrained laion2b_s34b_b88k --data_path <PATH>To obtain the precomputed text representations of the ImageNet classes, please run:
python compute_text_projection.py --dataset imagenet --device cuda:0 --model ViT-H-14 --pretrained laion2b_s32b_b79k
python compute_text_projection.py --dataset imagenet --device cuda:0 --model ViT-L-14 --pretrained laion2b_s32b_b82k
python compute_text_projection.py --dataset imagenet --device cuda:0 --model ViT-B-16 --pretrained laion2b_s34b_b88kTo verify that the MLPs and the attention from the class token to itself can be mean-ablated, please run:
python compute_ablations.py --model ViT-H-14
python compute_ablations.py --model ViT-L-14
python compute_ablations.py --model ViT-B-16To convert the text labels for TextSpan to CLIP text representations, please run:
python compute_text_set_projection.py --device cuda:0 --model ViT-L-14 --pretrained laion2b_s32b_b82k --data_path text_descriptions/google_3498_english.txt
python compute_text_set_projection.py --device cuda:0 --model ViT-L-14 --pretrained laion2b_s32b_b82k --data_path text_descriptions/image_descriptions_general.txtPlease download the dataset from here:
mkdir imagenet_seg
cd imagenet_seg
wget http://calvin-vision.net/bigstuff/proj-imagenet/data/gtsegs_ijcv.matTo get the evaluation results, please run:
python compute_segmentations.py --device cuda:0 --model ViT-H-14 --pretrained laion2b_s32b_b79k --data_path imagenet_seg/gtsegs_ijcv.mat --save_img
python compute_segmentations.py --device cuda:0 --model ViT-L-14 --pretrained laion2b_s32b_b82k --data_path imagenet_seg/gtsegs_ijcv.mat --save_img
python compute_segmentations.py --device cuda:0 --model ViT-B-16 --pretrained laion2b_s34b_b88k --data_path imagenet_seg/gtsegs_ijcv.mat --save_imgSave the results with the --save_img flag.
To find meaningful directions for all the attenion heads, run:
python compute_complete_text_set.py --device cuda:0 --model ViT-B-16 --texts_per_head 20 --num_of_last_layers 4 --text_descriptions image_descriptions_general
python compute_complete_text_set.py --device cuda:0 --model ViT-L-14 --texts_per_head 20 --num_of_last_layers 4 --text_descriptions image_descriptions_general
python compute_complete_text_set.py --device cuda:0 --model ViT-H-14 --texts_per_head 20 --num_of_last_layers 4 --text_descriptions image_descriptions_generalTo download the Waterbirds datasets, run:
wget https://nlp.stanford.edu/data/dro/waterbird_complete95_forest2water2.tar.gz
tar -xf waterbird_complete95_forest2water2.tar.gzTo compute the overall accuracy, run:
python compute_text_projection.py --dataset binary_waterbirds --device cuda:0 --model ViT-L-14 --pretrained laion2b_s32b_b82k
python compute_use_specific_heads.py --model ViT-L-14 --dataset binary_waterbirds Please see a demo for the spatial decomposition of CLIP in demo.ipynb.
Please see a nearest neighbors search demo in nns.ipynb.
@inproceedings{
gandelsman2024interpreting,
title={Interpreting {CLIP}'s Image Representation via Text-Based Decomposition},
author={Yossi Gandelsman and Alexei A. Efros and Jacob Steinhardt},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=5Ca9sSzuDp}
}
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