Awesome

Single-Hop: A Simple Baseline for the GQA Dataset

This repository contains a simple model for the GQA dataset, using a single-hop attention over the visual features based on the encoded question, as specified in models_gqa/single_hop.py. It should serve as the baseline for more complicated models on the GQA dataset. The baseline is described as "single-hop" in the LCGN paper.

(If you are looking for the code for our LCGN model, please check out this repo.)

It is applicable to three types of features from GQA:

• spatial features: 7 x 7 x 2048 ResNet spatial features (from the GQA dataset release)
• objects features: N_{det} x 2048 Faster R-CNN ResNet object features (from the GQA dataset release)
• "perfect-sight" object names and attributes: obtained from the ground-truth scene graphs in GQA at both training and test time. This setting uses two one-hot vectors to represent each object's class name and attributes, and concatenate them as its visual feature. It does not use the relation annotations in the scene graphs.

It gets the following performance on the validation (val_balanced), the test-dev (testdev_balanced) and the test (test_balanced) split of the GQA dataset:

*This setting requires using the GQA ground-truth scene graphs at both training and test time (only the object names and attributes are used; their relations are not used). Hence, it is not applicable to the test or the challenge setting.

Installation

1. Install Python 3 (Anaconda recommended: https://www.continuum.io/downloads).
2. Install TensorFlow (we used TensorFlow 1.12.0 in our experiments):
   pip install tensorflow-gpu (or pip install tensorflow-gpu==1.12.0 to install TensorFlow 1.12.0)
3. Install a few other dependency packages (NumPy, HDF5, YAML):
   pip install numpy h5py pyyaml
4. Download this repository or clone with Git, and then enter the root directory of the repository:
   git clone https://github.com/ronghanghu/gqa_single_hop_baseline.git && cd gqa_single_hop_baseline

Download the GQA dataset

Download the GQA dataset from https://cs.stanford.edu/people/dorarad/gqa/, and symbol link it to exp_gqa/gqa_dataset. After this step, the file structure should look like

exp_gqa/gqa_dataset
    questions/
        train_all_questions/
            train_all_questions_0.json
            ...
            train_all_questions_9.json
        train_balanced_questions.json
        val_all_questions.json
        val_balanced_questions.json
        submission_all_questions.json
        test_all_questions.json
        test_balanced_questions.json
    spatial/
        gqa_spatial_info.json
        gqa_spatial_0.h5
        ...
        gqa_spatial_15.h5
    objects/
        gqa_objects_info.json
        gqa_objects_0.h5
        ...
        gqa_objects_15.h5
    sceneGraphs/
        train_sceneGraphs.json
        val_sceneGraphs.json
    images/
        ...

Note that on GQA images are not needed for training or evaluation -- only questions, features and scene graphs (if you would like to run on the "perfect-sight" object names and attributes) are needed.

Training on GQA

Note:

• All of these three models are trained on the GQA train_balanced split, using a single GPU with a batch size of 128 for 100000 iterations, which takes a few hours on our machines.
• By default, the above scripts use GPU 0. To run on a different GPU, append GPU_ID parameter to the commands above (e.g. appending GPU_ID 2 to use GPU 2). During training, the script will write TensorBoard events to exp_gqa/tb/{exp_name}/ and save the snapshots under exp_gqa/tfmodel/{exp_name}/ (where {exp_name} is one of spatial, objects or scene_graph).

Pretrained models:

• You may skip the training step and directly download the pre-trained models from the links at the top. The downloaded models should be saved under exp_gqa/tfmodel/{exp_name}/ (where {exp_name} is one of spatial, objects or scene_graph).

Training steps：

0. Add the root of this repository to PYTHONPATH: export PYTHONPATH=.:$PYTHONPATH
1. Train with spatial features:
   python exp_gqa/train.py --cfg exp_gqa/cfgs/spatial.yaml
2. Train with objects features:
   python exp_gqa/train.py --cfg exp_gqa/cfgs/objects.yaml
3. Train with "perfect-sight" object names and attributes (one-hot embeddings):
   python exp_gqa/train.py --cfg exp_gqa/cfgs/scene_graph.yaml

Testing on GQA

Note:

• The above evaluation script will print out the final VQA accuracy only on when testing on val_balanced or testdev_balanced split.
• When running test on the submission_all split to generate the prediction file, the displayed accuracy will be zero, but the prediction file will be correctly generated under exp_gqa/results/{exp_name}/ (where {exp_name} is one of spatial, objects or scene_graph), and the prediction file path will be displayed at the end. It takes a long time to generate prediction files.
• By default, the above script uses GPU 0. To run on a different GPU, append GPU_ID parameter to the commands above (e.g. appending GPU_ID 2 to use GPU 2).

Testing steps:

0. Add the root of this repository to PYTHONPATH: export PYTHONPATH=.:$PYTHONPATH
1. Test with spatial features:
   • test locally on the val_balanced split:
     python exp_gqa/test.py --cfg exp_gqa/cfgs/spatial.yaml TEST.SPLIT_VQA val_balanced
   • test locally on the testdev_balanced split:
     python exp_gqa/test.py --cfg exp_gqa/cfgs/spatial.yaml TEST.SPLIT_VQA testdev_balanced
   • generate the submission file on submission_all for EvalAI (this takes a long time):
     python exp_gqa/test.py --cfg exp_gqa/cfgs/spatial.yaml TEST.SPLIT_VQA submission_all
2. Test with objects features:
   • test locally on the val_balanced split:
     python exp_gqa/test.py --cfg exp_gqa/cfgs/objects.yaml TEST.SPLIT_VQA val_balanced
   • test locally on the testdev_balanced split:
     python exp_gqa/test.py --cfg exp_gqa/cfgs/objects.yaml TEST.SPLIT_VQA testdev_balanced
   • generate the submission file on submission_all for EvalAI (this takes a long time):
     python exp_gqa/test.py --cfg exp_gqa/cfgs/objects.yaml TEST.SPLIT_VQA submission_all
3. Test with "perfect-sight" object names and attributes (one-hot embeddings):
   • test locally on the val_balanced split:
     python exp_gqa/test.py --cfg exp_gqa/cfgs/scene_graph.yaml TEST.SPLIT_VQA val_balanced
   • test locally on the testdev_balanced split (This won't work unless you have a file testdev_sceneGraphs.json under exp_gqa/gqa_dataset/sceneGraphs/ that contains scene graphs for test-dev images, which we don't):
     python exp_gqa/test.py --cfg exp_gqa/cfgs/scene_graph.yaml TEST.SPLIT_VQA testdev_balanced
   • generate the submission file on submission_all for EvalAI (This won't work unless you have a file submission_sceneGraphs.json under exp_gqa/gqa_dataset/sceneGraphs/ that contains scene graphs for all images, which we don't):
     python exp_gqa/test.py --cfg exp_gqa/cfgs/scene_graph.yaml TEST.SPLIT_VQA submission_all

Acknowledgements

The outline of the configuration code (such as models_gqa/config.py) is modified from the Detectron codebase.