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Language-Conditioned Graph Networks for Relational Reasoning

This repository contains the code for the following paper:

• R. Hu, A. Rohrbach, T. Darrell, K. Saenko, Language-Conditioned Graph Networks for Relational Reasoning. in ICCV 2019 (PDF)

@inproceedings{hu2019language,
  title={Language-Conditioned Graph Networks for Relational Reasoning},
  author={Hu, Ronghang and Rohrbach, Anna and Darrell, Trevor and Saenko, Kate},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
  year={2019}
}

Project Page: http://ronghanghu.com/lcgn

This is the (original) TensorFlow implementation of LCGN. A PyTorch implementation is available in the PyTorch branch.

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 PyTorch (needed only in CLEVR and CLEVR-Ref+ experiments, and only for feature extraction):
   pip install torch torchvision
4. Install a few other dependency packages (NumPy, HDF5, YAML):
   pip install numpy h5py pyyaml
5. Download this repository or clone with Git, and then enter the root directory of the repository:
   git clone https://github.com/ronghanghu/lcgn.git && cd lcgn

Train and evaluate on the CLEVR dataset and the CLEVR-Ref+ dataset

The LCGN model is applied to the 14 x 14 x 1024 ResNet-101-C4 features from CLEVR and CLEVR-Ref+.

On CLEVR, we train on the training split and evaluate on the validation and test splits. It gets the following performance on the validation (val) and the test (test) split of the CLEVR:

On CLEVR-Ref+, following the IEP-Ref code, we cross-valid on the training set and evaluate on the validation set. It gets the following performance on the validation (locplus_val in our code) split of the CLEVR-Ref+:

Download and preprocess the data

1. Download the CLEVR dataset from http://cs.stanford.edu/people/jcjohns/clevr/, and symbol link it to exp_clevr/clevr_dataset. After this step, the file structure should look like

exp_clevr/clevr_dataset/
  images/
    train/
      CLEVR_train_000000.png
      ...
    val/
    test/
  questions/
    CLEVR_train_questions.json
    CLEVR_val_questions.json
    CLEVR_test_questions.json
  ...

If you want to run any experiments on the CLEVR-Ref+ dataset for the referential expression comprehension task, you can download it from here, and symbol link it to exp_clevr/clevr_locplus_dataset. After this step, the file structure should look like

exp_clevr/clevr_locplus_dataset/
  images/
    train/
      CLEVR_train_000000.png
      ...
    val/
    test/
  refexps/
    clevr_ref+_train_refexps.json
    clevr_ref+_val_refexps.json
  scenes/
    clevr_ref+_train_scenes.json
    clevr_ref+_val_scenes.json

2. Build imdbs for the datasets

cd exp_clevr/data/
# build imdbs
python build_clevr_imdb.py
python build_clevr_locplus_imdb.py   # only needed if you want to run on CLEVR-Ref+
cd ../../

3. Extract ResNet-101-C4 features for the CLEVR images

Here, we use Justin Johnson's CLEVR feature extraction script, which requires PyTorch.

mkdir -p exp_clevr/data/features/spatial/
python exp_clevr/data/extract_features.py \
  --input_image_dir exp_clevr/clevr_dataset/images/train \
  --output_h5_file exp_clevr/data/features/spatial/train.h5
python exp_clevr/data/extract_features.py \
  --input_image_dir exp_clevr/clevr_dataset/images/val \
  --output_h5_file exp_clevr/data/features/spatial/val.h5
python exp_clevr/data/extract_features.py \
  --input_image_dir exp_clevr/clevr_dataset/images/test \
  --output_h5_file exp_clevr/data/features/spatial/test.h5

Training on CLEVR and CLEVR-Ref+

Note:

• By default, the scripts below use a single GPU (GPU 0). To run on multiple or different GPUs, append GPUS parameter to the commands below (e.g. appending GPUS 1 to use GPU 1, or GPUS 0,1 to use both GPU 0 & 1).
• During training, the script will save the snapshots under exp_clevr/tfmodel/{exp_name}/, where {exp_name} is one of lcgn (for CLEVR) or lcgn_ref (for CLEVR-Ref+).

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_clevr/tfmodel/{exp_name}/, where {exp_name} is one of lcgn (for CLEVR) or lcgn_ref (for CLEVR-Ref+).

Training steps：

0. Add the root of this repository to PYTHONPATH: export PYTHONPATH=.:$PYTHONPATH
1. Train on CLEVR (for VQA):
   python exp_clevr/main.py --cfg exp_clevr/cfgs/lcgn.yaml train True
2. Train on CLEVR-Ref+ (for REF):
   python exp_clevr/main_ref.py --cfg exp_clevr/cfgs_ref/lcgn_ref.yaml train True

Testing on CLEVR and CLEVR-Ref+

Note:

• As there is no ground-truth answers for test split in the downloaded CLEVR data, when testing on the CLEVR test set the displayed accuracy will be zero. You may email the prediction outputs in exp_clevr/results/lcgn/ to the CLEVR dataset authors for the test split accuracy.
• By default, the scripts below use a single GPU (GPU 0). To run on multiple or different GPUs, append GPUS parameter to the commands below (e.g. appending GPUS 1 to use GPU 1, or GPUS 0,1 to use both GPU 0 & 1).

Testing steps:

0. Add the root of this repository to PYTHONPATH: export PYTHONPATH=.:$PYTHONPATH
1. Test on CLEVR (for VQA):
   • test locally on the val split:
     python exp_clevr/main.py --cfg exp_clevr/cfgs/lcgn.yaml
   • test locally on the test split (the displayed accuracy will be zero; use prediction outputs in exp_clevr/results/lcgn/):
     python exp_clevr/main.py --cfg exp_clevr/cfgs/lcgn.yaml TEST.SPLIT_VQA test TEST.DUMP_PRED True
2. Test on CLEVR-Ref+ (for REF):
   • test locally on the val split:
     python exp_clevr/main_ref.py --cfg exp_clevr/cfgs_ref/lcgn_ref.yaml

Train and evaluate on the GQA dataset

The LCGN model 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.

Note: we also release our simple but well-performing "single-hop" baseline for the GQA dataset in a standalone repo. This "single-hop" model can serve as a basis for developing more complicated models.

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:

• Our models are trained on the GQA train_balanced split, which takes a few hours on our machines.
• By default, the scripts below use a single GPU (GPU 0). To run on multiple or different GPUs, append GPUS parameter to the commands below (e.g. appending GPUS 1 to use GPU 1, or GPUS 0,1 to use both GPU 0 & 1).
• During training, the script will save the snapshots under exp_gqa/tfmodel/{exp_name}/, where {exp_name} is one of lcgn_spatial, lcgn_objects or lcgn_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 lcgn_spatial, lcgn_objects or lcgn_scene_graph.

Training steps：

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

Testing on GQA

Note:

• The evaluation script below 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 lcgn_spatial, lcgn_objects or lcgn_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 scripts below use a single GPU (GPU 0). To run on multiple or different GPUs, append GPUS parameter to the commands below (e.g. appending GPUS 1 to use GPU 1, or GPUS 0,1 to use both GPU 0 & 1).

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/main.py --cfg exp_gqa/cfgs/lcgn_spatial.yaml TEST.SPLIT_VQA val_balanced
   • test locally on the testdev_balanced split:
     python exp_gqa/main.py --cfg exp_gqa/cfgs/lcgn_spatial.yaml TEST.SPLIT_VQA testdev_balanced
   • generate the submission file on submission_all for EvalAI (the displayed accuracy will be zero; this takes a long time):
     python exp_gqa/main.py --cfg exp_gqa/cfgs/lcgn_spatial.yaml TEST.SPLIT_VQA submission_all TEST.DUMP_PRED True
2. Test with objects features:
   • test locally on the val_balanced split:
     python exp_gqa/main.py --cfg exp_gqa/cfgs/lcgn_objects.yaml TEST.SPLIT_VQA val_balanced
   • test locally on the testdev_balanced split:
     python exp_gqa/main.py --cfg exp_gqa/cfgs/lcgn_objects.yaml TEST.SPLIT_VQA testdev_balanced
   • generate the submission file on submission_all for EvalAI (the displayed accuracy will be zero; this takes a long time):
     python exp_gqa/main.py --cfg exp_gqa/cfgs/lcgn_objects.yaml TEST.SPLIT_VQA submission_all TEST.DUMP_PRED True
3. Test with "perfect-sight" object names and attributes (one-hot embeddings):
   • test locally on the val_balanced split:
     python exp_gqa/main.py --cfg exp_gqa/cfgs/lcgn_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/main.py --cfg exp_gqa/cfgs/lcgn_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/main.py --cfg exp_gqa/cfgs/lcgn_scene_graph.yaml TEST.SPLIT_VQA submission_all TEST.DUMP_PRED True

Acknowledgements

Part of the CLEVR and GQA dataset preprocessing code, many TensorFlow operations (such as models_clevr/ops.py) and multi-GPU training code are obtained from the MAC codebase.

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

The ResNet feature extraction script (exp_clevr/data/extract_features.py) is obtained from the CLEVR-IEP codebase.