LeeDoYup/bottom-up-attention-tf

Bottom-Up and Top-Down Attention for Visual Question Answering

An tensorflow implementation of the winning entry of the 2017 VQA Challenge. The model details are in "Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge" paper.
This implementation is motivated from pytorch implementation link.

Some Info.

This code do not use visual-genome dataset fore pretraining.
More number of hidden neurons are used than original paper (512 >> 1024)
batch normalization is used in classifier.
This implementation requires large RAM size (~ 100 GB) by pretrained features.

Results

I checked the final results and it can be differ whether early-stopping is used.

Model	Validation Accuracy	Training Time
Reported Model	63.15	12 - 18 hours (Tesla K40)
TF Model	61~64	< 1 hours (Tesla P40)

Model Architecture

Proposed Model (in paper)

proposed_model

Implemented Graph (tensorboard)

Learning curve (score)

lr_curve

Main Codes

./dataset.py : data preprocessing and tensorflow dataset modules.
./models/ops.py : tensorflow operation warpper.
./models/vqa_model.py : model class
./models/language_model.py : word embedding and question embedding
./models/top_down_attention.py : proposed top-down-attention module

Prerequisites

Make sure you are on a machine with a NVIDIA GPU and Python 3 with about 100 GB RAM space.
This code needs more memory than pytorch version (70~80 GB).
Some issues are resolving to increase memory efficiency.
If you resolve, i always welcome PR.

- tensorflow 1.13.0
- h5py

Data Setup

Data download and preprocessing module is from original_repo and [pytorch_repo]((https://github.com/hengyuan-hu/bottom-up-attention-vqa)

Make sure your dataset should be downloaded to ./data/ directory.

>> mkdir data
>> sh tools/download.sh
>> sh tools/process.sh

Run

For various hyperparameter setting, refer the arguments in main.py.

>> python main.py

If you want to visualize your result and graph

>> tensorboard --logdir='./tensorboard' (--ip=YOUR_IP) (--port=YOUR_PORT)

Early stopping

Evaluation on validation data is available at every epoch.
Also early-stpping is enable to prevent from overfitting.
However, because of memory issue, the code in main.py is annotated.

Reference

"Tips and Tricks for Visual Question Answering: Learnings from the 2017 Challenge" paper.
pytorch implementation of hengyuan-hu link