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GitHub - rowanz/r2c: Recognition to Cognition Networks

 3 years ago
source link: https://github.com/rowanz/r2c
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README.md

r2c

This repository contains data and PyTorch code for the paper From Recognition to Cognition: Visual Commonsense Reasoning (arxiv). For more info, check out the project page at visualcommonsense.com.

visualization

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. I'm super excited about VCR but it should be viewed as knowledge that's still in the making :)

Setting up and using the repo

  1. Get the dataset. Follow the steps in data/README.md.

  2. 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/
  1. 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 (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 r2c python=3.6
source activate r2c

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 -c pytorch
pip install git+git://github.com/pytorch/[email protected]

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
  1. If you don't want to download from scratch, then download my checkpoint.
wget https://s3-us-west-2.amazonaws.com/ai2-rowanz/r2c/flagship_answer/best.th -P models/saves/flagship_answer/
wget https://s3-us-west-2.amazonaws.com/ai2-rowanz/r2c/flagship_rationale/best.th -P models/saves/flagship_rationale/
  1. That's it!

Using the repo after you download it

You can train a model using models/train.py. This also has code to obtain model predictions. Use eval_all.py in the main directory to get results combining Q->A and QA->R components.

help

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

Bibtex

@article{zellers2018vcr,
  title={From Recognition to Cognition: Visual Commonsense Reasoning},
  author={Zellers, Rowan and Bisk, Yonatan and Farhadi, Ali and Choi, Yejin},
  journal={arXiv preprint arXiv:1811.10830},
  year={2018}
}

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