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Heterogeneous Graph Learning for Visual Commonsense Reasoning (NeurlPS 2019)

This repository contains data and PyTorch code for the paper Heterogeneous Graph Learning for Visual Commonsense Reasoning (arxiv). We are currently not sharing the whole code, we will send a notification as soon as possible it is available again. We are sharing a simple implementation version of our paper here for research purposes, which can also achieve similar results of our paper.

This repo should be ready to replicate my results from the paper. If you have any issues with getting it set up though, please file a github issue. Still, the paper is just an arxiv version, so there might be more updates in the future.

This repository contains trained models and PyTorch version code for the above paper, If the paper significantly inspires you, we request that you cite our work:

Bibtex

@inproceedings{yu2019heterogeneous,
  title={Heterogeneous Graph Learning for Visual Commonsense Reasoning},
  author={Yu, Weijiang and Zhou, Jingwen and Yu, Weihao and Liang, Xiaodan and Xiao, Nong},
  booktitle={Advances in Neural Information Processing Systems},
  pages={2765--2775},
  year={2019}
}

Background

This repository is for the new task of Visual Commonsense Reasoning. A model is given an image, objects, a question, and four answer choices. The model has to decide which answer choice is correct. Then, it's given four rationale choices, and it has to decide which of those is the best rationale that explains why its answer is right.

In particular, I have code and checkpoints for the HGL model, as discussed in the HGL paper. Here's a diagram that explains what's going on:

We'll treat going from Q->A and QA->R as two separate tasks: in each, the model is given a 'query' (question, or question+answer) and 'response choices' (answer, or rationale). Essentially, we'll use BERT and detection regions to ground the words in the query, then contextualize the query with the response. We'll perform several steps of reasoning on top of a representation consisting of the response choice in question, the attended query, and the attended detection regions. See the paper for more details.

Setup

  • Get the dataset. Follow the steps in data/README.md. This includes the steps to get the pretrained BERT embeddings. You might find the dataloader useful (in dataloaders/vcr.py), as it handles loading the data in a nice way using the allennlp library.

  • Install cuda 9.0 if it's not available already. You might want to follow this this guide but using cuda 9.0. I use the following commands (my OS is ubuntu 16.04):

wget https://developer.nvidia.com/compute/cuda/9.0/Prod/local_installers/cuda_9.0.176_384.81_linux-run
chmod +x cuda_9.0.176_384.81_linux-run
./cuda_9.0.176_384.81_linux-run --extract=$HOME
sudo ./cuda-linux.9.0.176-22781540.run
sudo ln -s /usr/local/cuda-9.0/ /usr/local/cuda
export LD_LIBRARY_PATH=/usr/local/cuda-9.0/
  • Install anaconda if it's not available already, and create a new environment. You need to install a few things, namely, pytorch 1.0, torchvision (from the layers branch, which has ROI pooling), and allennlp.
wget https://repo.anaconda.com/archive/Anaconda3-5.2.0-Linux-x86_64.sh
conda update -n base -c defaults conda
conda create --name hgl python=3.6
source activate hgl

conda install numpy pyyaml setuptools cmake cffi tqdm pyyaml scipy ipython mkl mkl-include cython typing h5py pandas nltk spacy numpydoc scikit-learn jpeg

conda install pytorch cudatoolkit=9.0 -c pytorch
pip install git+git://github.com/pytorch/vision.git@24577864e92b72f7066e1ed16e978e873e19d13d

pip install -r allennlp-requirements.txt
pip install --no-deps allennlp==0.8.0
python -m spacy download en_core_web_sm


# this one is optional but it should help make things faster
pip uninstall pillow && CC="cc -mavx2" pip install -U --force-reinstall pillow-simd
  • If you don't want to download from scratch, then download my checkpoint.

  • That's it! Now to set up the environment, run source activate hgl && export PYTHONPATH=/home/yuwj/code/hgl (or wherever you have this directory).

Train & Val

Setting up the configuration

VCR_IMAGES_DIR = '/home/yuwj/VCR_dataset/vcr1images' # directory of images
VCR_ANNOTS_DIR = '/home/yuwj/VCR_dataset/vcr1annots' # directory of annotations
DATALOADER_DIR = '/home/yuwj/code/hgl' # directory of project
BERT_DIR = '/home/yuwj/VCR_dataset/bert_presentations' # directory of bert embedding

You can train a model using train.py. This also has code to obtain model predictions. Use eval_q2ar.py to get validation results combining Q->A and QA->R components. You can submit to the leaderboard using the script in eval_for_leaderboard.py.

Train

  • For question answering, run:
python train.py -params multiatt/default.json -folder answer_save -train -val
  • for Answer justification, run
python train.py -params multiatt/default.json -folder reason_save -train -val -rationale

Val

You can combine the validation predictions using

python eval_q2ar.py -answer_preds answer_save/valpreds.npy -rationale_preds reason_save/valpreds.npy

Submitting to the leaderboard

python eval_for_leaderboard.py -params models/multiatt/default.json -answer_ckpt answer_save/best.th -rationale_ckpt reason_save/best.th -output submisson.csv

Help

Feel free to open an issue if you encounter trouble getting it to work!

Acknowledgement

Thank @rowanz for his generously releasing nice code r2c.

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