GraphVQA: Language-Guided Graph Neural Networks for Scene Graph Question Answering

This repo provides the source code of our paper: GraphVQA: Language-Guided Graph Neural Networks for Scene Graph Question Answering (NAACL 2021 MAI Workshop) [PDF].

@inproceedings{2021graphvqa,
  author    = {Weixin Liang and
               Yanhao Jiang and
               Zixuan Liu},
  title     = {{GraghVQA}: Language-Guided Graph Neural Networks for Graph-based Visual
               Question Answering},
    booktitle = "Proceedings of the Third Workshop on Multimodal Artificial Intelligence",
    month = jun,
    year = "2021",
    address = "Mexico City, Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.maiworkshop-1.12",
    doi = "10.18653/v1/2021.maiworkshop-1.12",
    pages = "79--86"
}

Related Paper

LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering (NeurIPS KR2ML 2020). Weixin Liang, Feiyang Niu, Aishwarya Reganti, Govind Thattai and Gokhan Tur. [PDF] [Lightning Talk] [Blog] [Github] [Poster] [NeurIPS KR2ML 2020]

Abstract

Images are more than a collection of objects or attributes --- they represent a web of relationships among interconnected objects. Scene Graph has emerged as a new modality for a structured graphical representation of images. Scene Graph encodes objects as nodes connected via pairwise relations as edges. To support question answering on scene graphs, we propose GraphVQA, a language-guided graph neural network framework that translates and executes a natural language question as multiple iterations of message passing among graph nodes. We explore the design space of GraphVQA framework, and discuss the trade-off of different design choices. Our experiments on GQA dataset show that GraphVQA outperforms the state-of-the-art model by a large margin (88.43% vs. 94.78%). Our code is available at https://github.com/codexxxl/GraphVQA

Usage

0. Dependencies

Create a conda environment with python version = 3.6

0.1. Install torchtext, spacy

Run following commands in the created conda environment (Note: torchtext requires version: torchtext<0.9.0)

conda install -c pytorch torchtext
conda install -c conda-forge spacy
conda install -c conda-forge cupy
python -m spacy download en_core_web_sm
conda install -c anaconda nltk

Excute python and run following:

import nltk
nltk.download('wordnet')

0.2. Install PyTorch Geometric

Follow the link below to install PyTorch Geometric via binaries: https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html#installation-via-binaries

Example installation commands with PyTorch 1.4.0 and CUDA 10.0: (Note you need to replace torch-1.4.0+cu100 field with your own installed PyTorch and CUDA versions.)

pip install --no-index torch-scatter -f https://pytorch-geometric.com/whl/torch-1.4.0+cu100.html
pip install --no-index torch-sparse -f https://pytorch-geometric.com/whl/torch-1.4.0+cu100.html
pip install --no-index torch-cluster -f https://pytorch-geometric.com/whl/torch-1.4.0+cu100.html
pip install --no-index torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.4.0+cu100.html
pip install torch-geometric

1. Download Data

Download scene graphs raw data from: https://nlp.stanford.edu/data/gqa/sceneGraphs.zip
Download questions raw data from: https://nlp.stanford.edu/data/gqa/questions1.2.zip

Put sceneGraph json files: train_sceneGraphs.json, val_sceneGraphs.json into sceneGraphs/

Put questions json files: train_balanced_questions.json, val_balanced_questions.json, test_balanced_questions.json, testdev_balanced_questions.json into questions/original/

After this step, the data file structure should look like this:

GraphVQA
    questions/
        original/
            train_balanced_questions.json
            val_balanced_questions.json
            test_balanced_questions.json
            testdev_balanced_questions.json
    sceneGraphs/
        train_sceneGraphs.json
        val_sceneGraphs.json

2. Modify Root Directory

Replace line 13 in Constants.py with your own root directory that contains this source code folder:
ROOT_DIR = pathlib.Path('/Users/yanhaojiang/Desktop/cs224w_final/')

Note ROOT_DIR does not contain the repo name GraphVQA. E.g. for the ROOT_DIR above, my source code folder would be /Users/yanhaojiang/Desktop/cs224w_final/GraphVQA .

3. Preprocess Question Files (just need to run once)

Run command:

python preprocess.py

4. Test Installations and Data Preparations

Following commands should run without error:

python pipeline_model_gat.py 
python gqa_dataset_entry.py

5. Training

5.1. Main Model: GraphVQA-GAT

Single GPU training:
CUDA_VISIBLE_DEVICES=0 python mainExplain_gat.py --log-name debug.log --batch-size=200 --lr_drop=90

Distributed training:
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --use_env mainExplain_gat.py --workers=4 --batch-size=200 --lr_drop=90

To kill a distributed training:
kill $(ps aux | grep mainExplain_gat.py | grep -v grep | awk '{print $2}')

5.2. Baseline and Test Models

Baseline and other test models are trained in similar ways with corresponding mainExplain_{lcgn, gcn, gine}.py file excuted. Their related files are appended in \baseline_and_test_models. (Note move them out of this folder to train).
Corresponding to GraphVQA-GAT's model and training files: gat_skip.py, pipeline_model_gat.py, and mainExplain_gat.py, those model files are:

Baseline LCGN: lcgn.py, pipeline_model_lcgn.py, mainExplain_lcgn.py
GraphVQA-GCN: pipeline_model_gcn.py, mainExplain_gcn.py
GraphVQA-GINE: pipeline_model_gine.py, mainExplain_gine.py

6. Evaluation

We re-organize the evaluation script provided by GQA official, the original script and evaluation data can be found at https://cs.stanford.edu/people/dorarad/gqa/evaluate.html Step 1: Generate evaluation dataset To evaluate your model, there are two options:

Use validation_balanced set of programs.
Use validation_all set provided by GQA official.

6.1 Data Preparation

First download evaluation data from: https://nlp.stanford.edu/data/gqa/eval.zip. then unzip the file and move val_all_question.json to expainableGQA/questions/original/ now we will have

GraphVQA
    questions/
        original/
            val_all_questions.json

6.2 Evaluation

Option 1: Since after running Step 3(preprocess.py), we already have

GraphVQA
    questions/
        val_balanced_programs.json

then, run commands

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --use_env mainExplain_gat.py --workers=4 --batch-size=4000 --evaluate --resume=outputdir/your_checkpoint.pth --evaluate_sets='val_balanced --output_dir='./your_outputdir/' --evaluate_sets='val_unbiased'

you should get results json file located in './your_outputdir/dump_result.json'

then, run python eval.py --predictions=./your_outputdir/dump_results.json --consistency

Option 2: If you want to use validation_all set, then, run commands

python preprocess.py --val-all=True

we should get

GraphVQA
    questions/
        val_all_programs.json

then, run commands

CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 --use_env mainExplain_gat.py --workers=4 --batch-size=4000 --evaluate --resume=outputdir/your_checkpoint.pth --evaluate_sets='val_balanced --output_dir='./your_outputdir/' --evaluate_sets='val_all'

you should get results json file located in './your_outputdir/dump_results.json'

then, run

python eval.py --predictions=./your_outputdir/dump_results.json --consistency

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graphvqa's Introduction