https://github.com/hila-chefer/transformer-mm-explainability

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
https://github.com/hila-chefer/transformer-mm-explainability

clip detr explainability explainable-ai interpretability lxmert transformer transformers visualbert visualization vqa

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[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

• Host: GitHub
• URL: https://github.com/hila-chefer/transformer-mm-explainability
• Owner: hila-chefer
• License: mit
• Created: 2021-03-23T22:11:18.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2023-08-24T17:45:14.000Z (over 1 year ago)
• Last Synced: 2025-01-22T18:04:57.659Z (10 days ago)
• Topics: clip, detr, explainability, explainable-ai, interpretability, lxmert, transformer, transformers, visualbert, visualization, vqa
• Language: Jupyter Notebook
• Homepage:
• Size: 25.3 MB
• Stars: 819
• Watchers: 8
• Forks: 107
• Open Issues: 12
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
[ICCV 2021- Oral] PyTorch Implementation of `Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers `_

=================================================================================================================================================================

|youtube|

.. |youtube| image:: https://img.shields.io/static/v1?label=ICCV2021&message=12MinuteVideo&color=red

                   :target: https://www.youtube.com/watch?v=bQTL34Dln-M

Notebooks for LXMERT + DETR:

----------------------------

|DETR_LXMERT|

.. |DETR_LXMERT| image:: https://colab.research.google.com/assets/colab-badge.svg

                   :target: https://colab.research.google.com/github/hila-chefer/Transformer-MM-Explainability/blob/main/Transformer_MM_Explainability.ipynb

Notebook for CLIP:

----------------------------

|CLIP|

.. |CLIP| image:: https://colab.research.google.com/assets/colab-badge.svg

                   :target: https://colab.research.google.com/github/hila-chefer/Transformer-MM-Explainability/blob/main/CLIP_explainability.ipynb

**Demo**: You can check out a demo on `Huggingface spaces `_ or scan the following QR code.

.. image:: https://user-images.githubusercontent.com/19412343/176676771-d26f2146-9901-49e7-99be-b030f3d790de.png

   :width: 100

Notebook for ViT:

----------------------------

|ViT|

.. |ViT| image:: https://colab.research.google.com/assets/colab-badge.svg

                   :target: https://colab.research.google.com/github/hila-chefer/Transformer-MM-Explainability/blob/main/Transformer_MM_explainability_ViT.ipynb

.. sectnum::

Using Colab

----------------

* Please notice that the notebook assumes that you are using a GPU. To switch runtime go to Runtime -> change runtime type and select GPU.

* Installing all the requirements may take some time. After installation, please restart the runtime.

Running Examples

----------------

Notice that we have two `jupyter` notebooks to run the examples presented in the paper.

* `The notebook for LXMERT <./LXMERT.ipynb>`_ contains both the examples from the paper and examples with images from the internet and free form questions.

  To use your own input, simply change the `URL` variable to your image and the `question` variable to your free form question.

  .. image:: LXMERT.PNG

  .. image:: LXMERT-web.PNG

* `The notebook for DETR <./DETR.ipynb>`_ contains the examples from the paper.

  To use your own input, simply change the `URL` variable to your image.

  .. image:: DETR.PNG

Reproduction of results

-----------------------

^^^^^^^^^^

VisualBERT

^^^^^^^^^^

Run the `run.py` script as follows:

.. code-block:: bash

   CUDA_VISIBLE_DEVICES=0 PYTHONPATH=`pwd` python VisualBERT/run.py --method= --is-text-pert= --is-positive-pert= --num-samples=10000 config=projects/visual_bert/configs/vqa2/defaults.yaml model=visual_bert dataset=vqa2 run_type=val checkpoint.resume_zoo=visual_bert.finetuned.vqa2.from_coco_train env.data_dir=/path/to/data_dir training.num_workers=0 training.batch_size=1 training.trainer=mmf_pert training.seed=1234

.. note::

  If the datasets aren't already in `env.data_dir`, then the script will download the data automatically to the path in `env.data_dir`.

^^^^^^

LXMERT

^^^^^^

#. Download `valid.json `_:

    .. code-block:: bash

      pushd data/vqa

      wget https://nlp.cs.unc.edu/data/lxmert_data/vqa/valid.json

      popd

#. Download the `COCO_val2014` set to your local machine.

   .. note::

      If you already downloaded `COCO_val2014` for the `VisualBERT`_ tests, you can simply use the same path you used for `VisualBERT`_.

#. Run the `perturbation.py` script as follows:

    .. code-block:: bash

      CUDA_VISIBLE_DEVICES=0 PYTHONPATH=`pwd` python lxmert/lxmert/perturbation.py  --COCO_path /path/to/COCO_val2014 --method  --is-text-pert  --is-positive-pert 

^^^^

DETR

^^^^

#. Download the COCO dataset as described in the `DETR repository `_.

   Notice you only need the validation set.

   

#. Lower the IoU minimum threshold from 0.5 to 0.2 using the following steps:

         

   * Locate the `cocoeval.py` script in your python library path:

      

     find library path:

    

      .. code-block:: python

         import sys

         print(sys.path)

         

     find `cocoeval.py`: 

  

      .. code-block:: bash

      

         cd /path/to/lib

         find -name cocoeval.py

         

   * Change the `self.iouThrs` value in the `setDetParams` function (which sets the parameters for the COCO detection evaluation) in the `Params` class as follows:

      

     insead of:

    

      .. code-block:: python

       self.iouThrs = np.linspace(.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)

     use: 

  

      .. code-block:: python

       self.iouThrs = np.linspace(.2, 0.95, int(np.round((0.95 - .2) / .05)) + 1, endpoint=True)

#. Run the segmentation experiment, use the following command:

    .. code-block:: bash

       CUDA_VISIBLE_DEVICES=0 PYTHONPATH=`pwd`  python DETR/main.py --coco_path /path/to/coco/dataset  --eval --masks --resume https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth --batch_size 1 --method 

Citing

-------

If you make use of our work, please cite our paper:

    .. code-block:: latex

       @InProceedings{Chefer_2021_ICCV,

          author    = {Chefer, Hila and Gur, Shir and Wolf, Lior},

          title     = {Generic Attention-Model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers},

          booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},

          month     = {October},

          year      = {2021},

          pages     = {397-406}

       }

Credits

-------

* VisualBERT implementation is based on the `MMF `_ framework.

* LXMERT implementation is based on the `offical LXMERT `_ implementation and on `Hugging Face Transformers `_.

* DETR implementation is based on the `offical DETR `_ implementation.

* CLIP implementation is based on the `offical CLIP `_ implementation.

* The CLIP huggingface spaces demo was made by Paul Hilders, Danilo de Goede, and Piyush Bagad from the University of Amsterdam as part of their `final project `_.