MMXAI is a library & interface that explain Multimodal Machine Learning models.
MSc Computing Group Project - An Explainable AI (XAI) Interface for Multimodal Classification Models, Department of Computing, Imperial College London.
This project aims to explore ways to explain predicition decisions by Multimodal Machine Learning models and build a web app for users to interact with. Given a user model and an input examples, the software will provide interpretations for the model’s behaviour. The project started out by targeting classification problems and specifically the challenge of classifying hateful memes. The backend is packaged up as a library named MMXAI, which is powered by popular explanation methods, and aims to be model & framwork agnostic. We welcome contributors to extend it to more ML tasks / more explanation methods!
Interested? play with the interface yourself!
Meet the team behind this.
Create a virtual environment in your preferred way, then run these commands. These will add mmxai and the dependencies to your pip list and will be enough if you only want to work on mmxai methods. Please see examples/ for how to use each XAI methods.
git clone git@gitlab.doc.ic.ac.uk:g207004202/explainable-multimodal-classification.git
cd explainable-multimodal-classification
pip install --editable .
pip install -r requirements.txt
Installing MMF
In the web app, we use several pretrained models from MMF, who does not provice interfaces with these models. The /mmf directory contains the extended and modified files for this purpose. These are integrated in a fork of MMF. We recommend install from this fork rather than install from MMF and manually replace the files.
git clone https://github.com/junqi-jiang/mmf.git
cd mmf
pip install --editable .
We use MMBT, late_fusion, vilbert, visual bert models from MMF. Due to a known issue in MMF, some downloaded pretrained models have curious config files which will cause error, some extra steps are needed to solve it.
when first loading each pretrained model, the files are downloaded automatically to your default torch cache directory.
cd ~/.cache/torch/mmf/data/models/
ls
This will list all the pretrained checkpoints. cd into visual_bert.finetuned.hateful_memes.from_coco and vilbert.finetuned.hateful_memes.from_cc_original, use your preferred text editor to open the config.yaml, find and modify the following terms:
- cache_dir: ${resolve_cache_dir:MMF_CACHE_DIR}
- data_dir: ${resolve_dir:MMF_DATA_DIR, data}
- save_dir: ${env:MMF_SAVE_DIR, ./save}
- resume_file: (replace by your absolute path to the cache folder)/data/models/(vilbert.pretrained.cc_original or visualbert)/model.pth
For vilbert, they are on line 217, 220, 221, 41. For visual bert, they are on line 161, 164, 165, 41.
This step is only needed for using vilbert and visual bert. Download checkpoints from this link, unzip it, put them wherever you want.
Then go to the location where the fork of MMF was installed. Find mmf/tools/scripts/features/frcnn/feature_extraction_frcnn.py, change the paths at line 28 and 29 to the unzipped files config.yaml and model_finetuned.bin.
After these steps, you will be able to load and use pretrained MMF models (mmbt, late_fusion, vilbert, visual_bert), see examples/.
cd [wherever you cloned the explainable-multimodal-classification repo]
cd web_app
python3 app.py
This repository contains the code for the following components:
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Implementations of XAI methods Lime, Shap, Torchray extended to multimodal classification context: mmxai/interpretability/classification/
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Implementation of text inpainting: mmxai/text_removal/
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Our integrated XAI interface application: web_app/
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Extended MMF source code to allow easy interfacing with pretrained models for Facebook AI's the hateful memes challenge: mmf_modified/
The components above are well-separated. Future developers can easily expand this project further:
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Supporting more machine learning tasks: The multimodal XAI implementations currently work for multimodal binary classification tasks, specifically the MMF hateful memes classification. If there is a need for explaining other tasks like VQA, image captioning with reading, just add a directory under mmxai/interpretability specifying the type of the task (multilabel classification, multiclass classification, regression etc.) and work on it.
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Adding more XAI methods: We mainly focused on feature importance branch of XAI methods. There are many other types of XAI methods to explore. If anyone feels like doing that for any machine learning task, create a folder under /mmxai/interpretability/[replaced_by_task_name]/ for the method.
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Enabling extra functionalities: For the hateful memes task, we incorporated image inpainting as an option for our web app users to remove text chunks from their uploaded meme image to avoid potential occlusion of any image information. Add any extra tricks under /mmxai/.
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Adding more features to the web interface: Add to /mmxai. We have packed the /mmxai/* as a python library. Following the below installation steps and then import mmxai in the codes under /web_app where needed.