Guesswhat?! models

This repo aims at reproducing the results from the series of GuessWhat?! papers, namely:

GuessWhat?! Visual object discovery through multi-modal dialogue [1] https://arxiv.org/abs/1611.08481
End-to-end optimization of goal-driven and visually grounded dialogue systems [2] - https://arxiv.org/abs/1703.05423

The code was equally developed by Florian Strub (University of Lille) and Harm de Vries (University of Montreal)

The project is part of the CHISTERA - IGLU Project.

WARNING: After refactoring the code of the original paper, we fixed a bug in the codebase (the last generated question was ignored in some cases). New scores are greatly above the scores reported in [1] but some results analysis are now obsolete (qgen stop learning to stop, greedy has the highest accuracy). We apologize for the inconvenience.

Introduction

We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task.

Installation

Download

Our code has internal dependences called submodules. To properly clone the repository, please use the following git command:\

git clone --recursive https://github.com/GuessWhatGame/guesswhat.git'

Requirements

The code works on both python 2 and 3. It relies on the tensorflow python API. It requires the following python packages:

pip install \
    tensorflow-gpu \
    nltk \
    tqdm \
    image

File architecture

In the following, we assume that the following file/folder architecture is respected:

guesswhat
├── config         # store the configuration file to create/train models
|   ├── oracle
|   ├── guesser
|   ├── qgen
|   └── looper
|
├── out            # store the output experiments (checkpoint, logs etc.)
|   ├── oracle
|   ├── guesser
|   ├── qgen
|   └── looper
|
├── data          # contains the Guesshat data
|   └── img       # contains the coco img
|        ├── ft_vgg_img
|        ├── ft_vgg_crop
|        └── raw
|
└── src            # source files

To complete the git-clone file arhictecture, you can do:

cd guesswhat
mkdir data; mkdir data/img ; mkdir data/img/raw ; mkdir data/img/ft_vgg_img ; mkdir data/img/ft_vgg_crop
mkdir out; mkdir out/oracle ; mkdir out/guesser; mkdir out/qgen; mkdir out/looper ;

Of course, one is free to change this file architecture!

Data

GuessWhat?! relies on two datasets:

the GuessWhat?! dataset that contains the dialogue inputs
The MS Coco dataset that contains the image inputs

To download the GuessWhat?! dataset please follow the following instruction:

wget https://s3-us-west-2.amazonaws.com/guess-what/guesswhat.train.jsonl.gz -P data/
wget https://s3-us-west-2.amazonaws.com/guess-what/guesswhat.valid.jsonl.gz -P data/
wget https://s3-us-west-2.amazonaws.com/guess-what/guesswhat.test.jsonl.gz -P data/

To download the MS Coco dataset, please follow the following instruction:

wget http://msvocds.blob.core.windows.net/coco2014/train2014.zip -P data/img/
unzip data/img/train2014.zip -d data/img/raw

wget http://msvocds.blob.core.windows.net/coco2014/val2014.zip -P data/img/
unzip data/img/val2014.zip -d data/img/raw

# creates a folder `raw` with filenames as expected by preprocessing script below
python ./src/guesswhat/preprocess_data/rewire_coco_image_id.py \ 
   -image_dir `pwd`/data/img/raw \
   -data_out `pwd`/data/img/raw

NB: Please check that md5sum are correct after downloading the files to check whether they have been corrupted. To do so, you can use the following command:

md5sum $file

Pretrained networks

Pretrained networks can be downloaded here:

V1 of the code:

Tensorflow (1.0<->1.2): network.
Tensorflow (1.3): network.

You need to use the following tag to checkout the corresponding code:

git checkout tags/v1

V2 of the code:

coming sooon! It would include GW?! with advanced FiLM models, new RL algorithms, new tools!

Note that the reported results comes from the first version (v1) of pre-trained networks.

Reproducing results

To launch the experiments in the local directory, you first have to set the pyhton path:

export PYTHONPATH=src:${PYTHONPATH}

Note that you can also directly execute the experiments in the source folder.

Process Data

Before starting the training, one needs to compute the image features and the word dictionary

Extract image features

Following the original papers, we are going to extract fc8 features from the coco images by using a VGG-16 network.

Solution 1: You can directly download the vgg features:

wget www.florian-strub.com/github/ft_vgg_img.zip -P data/images
unzip data/images/ft_vgg_img.zip -d data/images/

Solution 2: You can download vgg-16 pretrained network provided by slim-tensorflow:

wget http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz -P data/
tar zxvf data/vgg_16_2016_08_28.tar.gz -C data/

GuessWhat?! requires to both computes the image features from the full image To do so, you need to use the pythn script guesswhat/src/guesswhat/preprocess_data/extract_img_features.py .

array=( img crop )
for mode in "${array[@]}"; do
   python src/guesswhat/preprocess_data/extract_img_features.py \
     -img_dir data/img/raw \
     -data_dir data \
     -out_dir data/img/ft_vgg_$mode \
     -network vgg \
     -ckpt data/vgg_16.ckpt \
     -feature_name fc8 \
     -mode $mode
done

Noticeably, one can also extract VGG-fc7 or Resnet features. Please follow the script documentation for more advanced setting.

Create dictionary

To create the GuessWhat?! dictionary, you need to use the pythn script guesswhat/src/guesswhat/preprocess_data/create_dico.py .

python src/guesswhat/preprocess_data/create_dictionary.py -data_dir data -dict_file dict.json -min_occ 3

Train Oracle

To train the oracle, you need to select/configure the input you want to use. To do so, you have update the file config/oracle/config.json By default, the oracle is trained with spatial+category but one may add/remove inputs. More information are available in the config folder.

Once the config file is set, you can launch the training step:

python src/guesswhat/train/train_oracle.py \
   -data_dir data \
   -img_dir data/img/ft_vgg_img \
   -crop_dir data/img/ft_vgg_crop \
   -config config/oracle/config.json \
   -exp_dir out/oracle \
   -no_thread 2

After training, we obtained the following results:

Set	Loss	Error
Train	0.130	17.5%
Valid	0.155	20.6%
Test	0.157	21.1%

Train Guesser

Identically, you first have to update the config/guesser/config.json

python src/guesswhat/train/train_guesser.py \
   -data_dir data \
   -img_dir data/ft_vgg_img \
   -config config/guesser/config.json \
   -exp_dir out/guesser \
   -no_thread 2

After training, we obtained the following results:

Set	Loss	Error
Train	0.681	27.6%
Valid	0.906	34.7%
Test	0.947	35.8%

Train QGen

Identically, you first have to update the config/guesser/config.json

python src/guesswhat/train/train_qgen_supervised.py \
   -data_dir data \
   -img_dir data/ft_vgg_img \
   -config config/qgen/config.json \
   -exp_dir out/qgen \
   -no_thread 2

After training, we obtained the following results:

Set	Loss
Train	1.31
Valid	1.75
Test	1.76

Train Looper

The looper use three pretrained models to play the GuessWhat?! game. Therefore, it provides a user-simulation scheme to perform RL training methods.

In this codebase, the QGen is fine-tuned by using REINFORCE. The QGen keep playing GuessWhat?! with the Oracle and it is rewarded when the Guesser find the correct object at the end of the dialogue.

To do so, one need to first pretrain the three models. Each model has a configuration hash and checkpoint. These configuration hash will be used as an entry point for the Looper.

python src/guesswhat/train/train_qgen_reinforce.py
    -data_dir data/ \
    -exp_dir out/loop/ \
    -config config/looper/config.json \
    -img_dir data/ft_vgg_img \
    -crop_dir data/ft_vgg_crop \
    -networks_dir out/ \
    -oracle_identifier <oracle_identifier> \
    -qgen_identifier <qgen_identifier> \
    -guesser_identifier <guesser_identifier> \
    -evaluate_all false \
    -store_games true ´\
    -no_thread 2

Activate the flag evaluate_all to also compute the accuracy with BeamSearch and Sampling (Time-consuming).

Detailled accuracies:

New Images	Cross-entropy	Reinforce
Sampling	39.2%	56.5 %
Greedy	40.8%	58.4 %
BeamSearch	44.6%	58.4 %

New Objects	Cross-entropy	Reinforce
Sampling	41.6%	58.5%
Greedy	43.5%	60.3%
BeamSearch	47.1%	60.2%

Note that those scores are exact accuracies.

Plot dataset

It is possible to plot figures analysing guesswhat raw dataset or QGen generated games (if you set the flag, store_games to True). To do so,

python src/guesswhat/statistics/statistics/plot_them_all.py
   -data_dir data 
   -out_dir out 
   -name train
   -ignore_incomplete false

The "ignore_incomplete flag" will take into account or ignore incomplete games in the dataset.
The "name" flag correspond to the following variable guesswhat.${name}.jsonl.gz

Note: If you want to compute you personal plot, you can user the AbstractPloter interface and append your implementation into the plot_them_all.py script.

FAQ

When I start a python script, I have the following message: ImportError: No module named generic.data_provider.iterator (or equivalent module). It is likely that your python path is not correctly set. Add the "src" folder to your python path (PYTHONPATH=src)

Citation

GuessWhat?! framework - https://arxiv.org/abs/1611.08481

@inproceedings{guesswhat_game,
author = {Harm de Vries and Florian Strub and Sarath Chandar and Olivier Pietquin and Hugo Larochelle and Aaron C. Courville},
title = {GuessWhat?! Visual object discovery through multi-modal dialogue},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2017}
}

Reinforcement Learning applied to GuessWhat?! - https://arxiv.org/abs/1703.05423

@inproceedings{end_to_end_gw,
author = {Florian Strub and Harm de Vries and J\'er\'emie Mary and Bilal Piot and Aaron C. Courville and Olivier Pietquin},
title = {End-to-end optimization of goal-driven and visually grounded dialogue systems},
booktitle = {International Joint Conference on Artificial Intelligence (IJCAI)},
year = {2017}
}

Acknowledgement

SequeL Team
Mila Team

We would also like people that help improving the code base namely: Rui Zhao, Hannes Schulz.

bplank/guesswhat

Guesswhat?! models

Summary:

Introduction

Installation

Download

Requirements

File architecture

Data

Pretrained networks

Reproducing results

Process Data

Extract image features

Create dictionary

Train Oracle

Train Guesser

Train QGen

Train Looper

Plot dataset

FAQ

Citation

Acknowledgement