Pythia is a modular framework for Visual Question Answering research, which formed the basis for the winning entry to the VQA Challenge 2018 from Facebook AI Research (FAIR)’s A-STAR team. Please check our paper for more details.
(A-STAR: Agents that See, Talk, Act, and Reason.)
- Motivation
- Citing pythia
- Installing pythia environment
- Quick start
- Preprocess dataset
- Test with pretrained models
- Ensemble models
- Customize config
- Docker demo
- AWS s3 dataset summary
- Acknowledgements
- References
The motivation for Pythia comes from the following observation – a majority of today’s Visual Question Answering (VQA) models fit a particular design paradigm, with modules for question encoding, image feature extraction, fusion of the two (typically with attention), and classification over the space of answers. The long-term goal of Pythia is to serve as a platform for easy and modular research & development in VQA and related directions like visual dialog.
The name Pythia is an homage to the Oracle of Apollo at Delphi, who answered questions in Ancient Greece. See here for more details.
If you use Pythia in your research or wish to refer to the baseline results included, please use the following BibTeX entry.
@misc{pythia2018,
author = {Yu Jiang and Vivek Natarajan and Xinlei Chen and Marcus Rohrbach and Dhruv Batra and Devi Parikh},
title = {Pythia v0.1: The Winning Entry to the VQA Challenge 2018},
howpublished = {\url{https://github.com/facebookresearch/pythia}},
year = {2018}
}
- Install Anaconda (Anaconda recommended: https://www.continuum.io/downloads).
- Install cudnn v7.0 and cuda.9.0
- Create environment for pythia
conda create --name vqa python=3.6
source activate vqa
pip install demjson pyyaml
pip install http://download.pytorch.org/whl/cu90/torch-0.3.1-cp36-cp36m-linux_x86_64.whl
pip install torchvision
pip install tensorboardX
We provide preprocessed data files to directly start training and evaluating. Instead of using the original train2014
and val2014
splits, we split val2014
into val2train2014
and minival2014
, and use train2014
+ val2train2014
for training and minival2014
for validation.
Download data. This step may take some time. Check the sizes of files at the end of readme.
git clone [email protected]:facebookresearch/pythia.git
cd Pythia
mkdir data
cd data
wget https://s3-us-west-1.amazonaws.com/pythia-vqa/data/vqa2.0_glove.6B.300d.txt.npy
wget https://s3-us-west-1.amazonaws.com/pythia-vqa/data/vocabulary_vqa.txt
wget https://s3-us-west-1.amazonaws.com/pythia-vqa/data/answers_vqa.txt
wget https://s3-us-west-1.amazonaws.com/pythia-vqa/data/imdb.tar.gz
wget https://s3-us-west-1.amazonaws.com/pythia-vqa/data/rcnn_10_100.tar.gz
wget https://s3-us-west-1.amazonaws.com/pythia-vqa/data/detectron.tar.gz
wget https://s3-us-west-1.amazonaws.com/pythia-vqa/data/large_vocabulary_vqa.txt
wget https://s3-us-west-1.amazonaws.com/pythia-vqa/data/large_vqa2.0_glove.6B.300d.txt.npy
gunzip imdb.tar.gz
tar -xf imdb.tar
gunzip rcnn_10_100.tar.gz
tar -xf rcnn_10_100.tar
rm -f rcnn_10_100.tar
gunzip detectron.tar.gz
tar -xf detectron.tar
rm -f detectron.tar
Optional command-line arguments for train.py
python train.py -h
usage: train.py [-h] [--config CONFIG] [--out_dir OUT_DIR] [--seed SEED]
[--config_overwrite CONFIG_OVERWRITE] [--force_restart]
optional arguments:
-h, --help show this help message and exit
--config CONFIG config yaml file
--out_dir OUT_DIR output directory, default is current directory
--seed SEED random seed, default 1234, set seed to -1 if need a
random seed between 1 and 100000
--config_overwrite CONFIG_OVERWRITE
a json string to update yaml config file
--force_restart flag to force clean previous result and restart
training
Run model without finetuning
cd ../
python train.py
If there is a out of memory error, try:
python train.py --config_overwrite '{data:{image_fast_reader:false}}'
Run model with features from detectron with finetuning
python train.py --config config/keep/detectron.yaml
Check result for the default run
cd results/default/1234
The results folder contains the following info
results
|_ default
| |_ 1234 (default seed)
| | |_config.yaml
| | |_best_model.pth
| | |_best_model_predict_test.pkl
| | |_best_model_predict_test.json (json file for predicted results on test dataset)
| | |_model_00001000.pth (mpdel snapshot at iter 1000)
| | |_result_on_val.txt
| | |_ ...
| |_(other_cofig_setting)
| | |_...
|_ (other_config_file)
|
The log files for tensorbord are stored under boards/
If you want to start from the original VQA dataset and preprocess data by yourself, use the following instructions in data_preprocess.md. This part is not necessary if you download all data from quick start.
Note: all of these models below are trained with validation set included
Description | performance (test-dev) | Link |
---|---|---|
detectron_100_resnet_most_data | 70.01 | https://s3-us-west-1.amazonaws.com/pythia-vqa/pretrained_models/detectron_100_resnet_most_data.tar.gz |
baseline | 68.05 | https://s3-us-west-1.amazonaws.com/pythia-vqa/pretrained_models/baseline.tar.gz |
baseline +VG +VisDal +mirror | 68.98 | https://s3-us-west-1.amazonaws.com/pythia-vqa/pretrained_models/most_data.tar.gz |
detectron_finetune | 68.49 | https://s3-us-west-1.amazonaws.com/pythia-vqa/pretrained_models/detectron.tar.gz |
detectron_finetune+VG +VisDal +mirror | 69.24 | https://s3-us-west-1.amazonaws.com/pythia-vqa/pretrained_models/detectron_most_data.tar.gz |
The best pretrained model can be downloaded as follows:
mkdir pretrained_models/
cd pretrained_models
wget https://s3-us-west-1.amazonaws.com/pythia-vqa/pretrained_models/detectron_100_resnet_most_data.tar.gz
gunzip detectron_100_resnet_most_data.tar.gz
tar -xf detectron_100_resnet_most_data.tar
rm -f detectron_100_resnet_most_data.tar
Get ResNet152 features and Detectron features with fixed 100 bounding boxes
cd data
wget https://s3-us-west-1.amazonaws.com/pythia-vqa/data/detectron_fix_100.tar.gz
gunzip detectron_fix_100.tar.gz
tar -xf detectron_fix_100.tar
rm -f detectron_fix_100.tar
wget https://s3-us-west-1.amazonaws.com/pythia-vqa/data/resnet152.tar.gz
gunzip resnet152.tar.gz
tar -xf resnet152.tar
rm -f resnet152.tar
Test the best model on the VQA test2015 dataset
python run_test.py --config pretrained_models/detectron_100_resnet_most_data/1234/config.yaml \
--model_path pretrained_models/detectron_100_resnet_most_data/1234/best_model.pth \
--out_prefix test_best_model
The results will be saved as a json file test_best_model.json
, and this file can be uploaded to the evaluation server on EvalAI (https://evalai.cloudcv.org/web/challenges/challenge-page/80/submission).
Download all the models above
python ensemble.py --res_dirs pretrained_models/ --out ensemble_5.json
Results will be saved in ensemble_5.json
. This ensemble can get accuracy 71.65 on test-dev.
To run an ensemble of 30 pretrained models, download the models and image features as follows. This gets an accuracy of 72.18 on test-dev.
wget https://s3-us-west-1.amazonaws.com/pythia-vqa/ensembled.tar.gz
To change models or adjust hyper-parameters, see config_help.md
To quickly tryout a model interactively with nvidia-docker
git clone https://github.com/facebookresearch/pythia.git
nvidia-docker build pythia -t pythia:latest
nvidia-docker run -ti --net=host pythia:latest
This will open a jupyter notebook with a demo model to which you can ask questions interactively.
Here, we listed the size of some large files in our AWS S3 bucket.
Description | size |
---|---|
data/rcnn_10_100.tar.gz | 71.0GB |
data/detectron.tar.gz | 106.2 GB |
data/detectron_fix_100.tar.gz | 162.6GB |
data/resnet152.tar.gz | 399.6GB |
ensembled.tar.gz | 462.1GB |
We would like to thank Peter Anderson, Abhishek Das, Stefan Lee, Jiasen Lu, Jianwei Yang, Licheng Yu, Luowei Zhou for helpful discussions, Peter Anderson for providing training data for the Visual Genome detector, Deshraj Yadav for responses on EvalAI related questions, Stefan Lee for suggesting the name Pythia, Meet Shah for building the docker demo for Pythia and Abhishek Das, Abhishek Kadian for feedback on our codebase.
- P. Anderson, X. He, C. Buehler, D. Teney, M. Johnson, S. Gould, and L. Zhang. Bottom-up and top-down attenttion for image captioning and visual question answering. In CVPR, 2018.
- S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra,C. Lawrence Zitnick, and D. Parikh. VQA: Visual question answering. In ICCV, 2015
- A. Das, S. Kottur, K. Gupta, A. Singh, D. Yadav, J. M.Moura, D. Parikh, and D. Batra. Visual Dialog. In CVPR,2017
- Y. Goyal, T. Khot, D. Summers-Stay, D. Batra, and D. Parikh. Making the V in VQA matter: Elevating the role of image understanding in Visual Question Answering. In CVPR, 2017.
- Hu R, Andreas J, Rohrbach M, Darrell T, Saenko K. Learning to reason: End-to-end module networks for visual question answering. In ICCV, 2017.
- D. Teney, P. Anderson, X. He, and A. van den Hengel. Tips and tricks for visual question answering: Learnings from the 2017 challenge. CoRR, abs/1708.02711, 2017.
- Z. Yu, J. Yu, C. Xiang, J. Fan, and D. Tao. Beyond bilinear: Generalized multimodal factorized high-order pooling for visual question answering. TNNLS, 2018.