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Community Regularization of Visually Grounded Dialog

Akshat Agarwal*, Swaminathan Gurumurthy*, Vasu Sharma, Mike Lewis, Katia Sycara

Carnegie Mellon University, University of Pittsburgh

This repository contains a PyTorch implementation for our arXiv paper 1808.04359 on Community Regularization for Visually Grounded Dialog. The task requires goal-oriented exchange of information in natural language, however asking the agents to maximize information exchange while requiring them to adhere to the rules of human languages is an ill-posed optimization problem. Our solution, Community Regularization, involves each agent interacting with and learning from multiple agents, which results in more grammatically correct, relevant and coherent dialog without sacrificing information exchange. If you find this work useful, please cite our paper using the following BibTeX:

@inproceedings{agarwal2018community,
  title={Community Regularization of Visually-Grounded Dialog},
  author={Agarwal, Akshat and Gurumurthy, Swaminathan and Sharma, Vasu and Lewis, Michael and Sycara, Katia},
  booktitle={Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Montreal, Canada},
  year={2019},
  organization={IFAAMAS}
}

Installation and Downloading Data

# set up a clean virtual environment
virtualenv -p python3 ~/visualdialog
source ~/visualdialog/bin/activate # you will have to run this command in every new terminal, alternatively add macro to your .bashrc

pip3 install torch torchvision (or as appropriate from pytorch.org)
sudo apt-get install -y tensorboardX h5py 

git clone https://github.com/agakshat/visualdialog-pytorch.git
cd visualdialog-pytorch

# download visual dialog data
mkdir data
cd data
wget https://filebox.ece.vt.edu/~jiasenlu/codeRelease/visDial.pytorch/data/vdl_img_vgg.h5
wget https://filebox.ece.vt.edu/~jiasenlu/codeRelease/visDial.pytorch/data/visdial_data.h5
wget https://filebox.ece.vt.edu/~jiasenlu/codeRelease/visDial.pytorch/data/visdial_params.json
# however, these data files have 512x7x7 image embeddings, in place of which we 
# used 4096 size image embeddings. we download that in another folder
mkdir v09
cd v09
wget https://computing.ece.vt.edu/~abhshkdz/visdial/data/v0.9/visdial_params.json
wget https://computing.ece.vt.edu/~abhshkdz/visdial/data/v0.9/data_img_vgg16_relu7.h5

mkdir save

Training

# now run the code

# Option 1: Train from scratch, including 15 epochs of supervised learning
# followed by RL through curriculum
python main.py --num_abots 3 --num_qbots 1 --scratch --outf save/temp_dir

# Option 2: Start training from RL, assuming pretrained supervised learning agents
python main.py --num_abots 3 --num_qbots 1 --curr  --model_path save/pretrained_SL.pth --outf save/temp_dir

Important Command Line Arguments:

  1. --data_dir specifies path to data folder. Default is data/
  2. --v09_data_dir specifies path to alternative (v09 img files) data folder. Default is data/v09/ (There is no need to change these if you installed using the exact commands as above)
  3. --num_qbots and --num_abots specifies number of Q-Bots and A-Bots, respectively
  4. --model_path specifies the torch .pt file with the pretrained agents to be loaded.
  5. --outf specifies the save directory where the trained models will be saved epoch-wise, along with tensorboard logs
  6. --scratch if specified, the agents are trained from scratch, starting with supervised learning
  7. --curr if specified, the agents are trained from the beginning of the curriculum, assuming that --model_path has been specified to load SL pretrained model files
  8. --start_curr K if specified, the agents start curriculum training not from the beginning, but after the first 10-K rounds of curriculum have happened. Look at main.py for details.
  9. --batch_size default is 75, which you might need to reduce depending on the GPU being used. Note that as curriculum training progresses, progressively greater amount of GPU memory is used, becoming constant only when the agents are training purely via RL.

Evaluation

# To run only the evaluation, get image retrieval percentile scores and/or view generated dialog:
python main.py --num_abots 3 --num_qbots 1 --curr  --model_path save/pretrained_SL.pth --outf save/temp_dir --eval 1

# To get answer retrieval Mean Rank, MRR and Recall@k metrics:
python evaluate_mrr.py --num_abots 3 --num_qbots 1 --model_path save/pretrained_model_file.pth

Example of generated dialog

ex

Acknowledgement

Credits to Jiasen Lu for his network definitions of the A-Bot encoders and decoders