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Referring Relationships

Referring Relationships model

This repository contains code used to produce the results in the following paper:

Referring Relationships

Ranjay Krishna, Ines Chami, Michael Bernstein, Li Fei-Fei
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

If you are using this repository, please use the following citation:

@inproceedings{krishna2018referring,
  title={Referring Relationships},
  author={Krishna, Ranjay and Chami, Ines and Bernstein, Michael and Fei-Fei, Li },
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
  year={2018}
}

Clone the repository and install the dependencies.

You can clone the repository and install the requirements by running the following:

git clone https://github.com/stanfordvl/ReferringRelationships.git
cd ReferringRelationships
virtualenv -p python3 env
source env/bin/activate
pip install -r requirements.txt

To download the dataset used in the project, run:

./scripts/download_data.sh

Note that we only distribute the annotations for the datasets. To download the images for these datasets, please use the following links:

Model training

To train the models, you will need to create an hdf5 dataset and then run the following script to test and evaluate the model:

# For the VRD dataset.
./scripts/create_vrd_dataset.sh $LOCATION_OF_VRD_TRAIN_IMAGES $LOCATION_OF_VRD_TEST_IMAGES
./scripts/train_vrd.sh
./scripts/evaluate_vrd.sh
# For the CLEVR dataset.
./scripts/create_clevr_dataset.sh $LOCATION_OF_CLEVR_TRAIN_IMAGES $LOCATION_OF_CLEVR_VAL_IMAGES
./scripts/train_clevr.sh $LOCATION_OF_MODEL
./scripts/evaluate_clevr.sh $LOCATION_OF_MODEL
# For the Visual Genome dataset.
./scripts/create_visualgenome_dataset.sh $LOCATION_OF_VISUAL_GENOME_IMAGES
./scripts/train_visualgenome.sh $LOCATION_OF_MODEL
./scripts/evaluate_visualgenome.sh $LOCATION_OF_MODEL

This script will train the model and save the weights in the --save-dir directory. It will also save the configuration parameters in a params.json file and log events in train.log.

However, if you decide that you want more control over the training or evaluation scripts, check out the instructions below.

Customized dataset creation

The script data.py will save masks for objects and subjects in train/val/test directories that will be created in the directory --save-dir. The script also saves numpy arrays for relationships.

The script has the following command line arguments to modify the dataset pre-processing:

  -h, --help            show this help message and exit
  --test                When true, the data is not split into training and
                        validation sets
  --val-percent         Fraction of images in validation split.
  --save-dir            where to save the ground truth masks, this Location
                        where dataset should be saved.
  --img-dir             Location where images are stored.
  --annotations         Json with relationships for each image.
  --image-metadata      Image metadata json file.
  --image-dim           The size the images should be saved as.
  --output-dim          The size the predictions should be saved as.
  --seed                The random seed used to reproduce results.
  --num-images          The random seed used to reproduce results.
  --save-images         Use this flag to specify that the images should also
                        be saved.
  --max-rels-per-image  Maximum number of relationships per image.

Customized Training.

The model can be trained by calling python train.py with the following command line arguments to modify your training:

optional arguments:
  -h, --help            Show this help message and exit
  --opt                 The optimizer used during training. Currently supports
                        rms, adam, adagrad and adadelta.
  --lr                  The learning rate for training.
  --lr_decay            The learning rate decay.
  --batch-size          The batch size used in training.
  --epochs              The number of epochs to train.
  --seed                The random seed used to reproduce results.
  --overwrite           Train even if that folder already contains an existing
                        model.
  --save-dir            The location to save the model and the results.
  --models-dir          The location of the model weights
  --use-models-dir      Indicates that new models can be saved in the models
                        directory set by --models-dir.
  --save-best-only      Saves only the best model checkpoint.

  --use-subject         Boolean indicating whether to use the subjects.
  --use-predicate       Boolean indicating whether to use the predicates.
  --use-object          Boolean indicating whether to use the objects.

  --embedding-dim       Number of dimensions in our class embeddings.
  --hidden-dim          Number of dimensions in the hidden unit.
  --feat-map-dim        The size of the feature map extracted from the image.
  --input-dim           Size of the input image.
  --num-predicates      The number of predicates in the dataset.
  --num-objects         The number of objects in the dataset.
  --dropout             The dropout probability used in training.

  --train-data-dir      Location of the training data.
  --val-data-dir        Location of the validation data.
  --image-data-dir      Location of the images.
  --heatmap-threshold   The thresholds above which we consider a heatmap to
                        contain an object.

Customized evaluation

The evaluations can be run using python evaluate.py with the following options:

  -h, --help            show this help message and exit
  --batch-size          The batch size used in training.
  --seed                The random seed used to reproduce results.
  --workers             Number workers used to load the data.
  --heatmap-threshold   The thresholds above which we consider a heatmap to
                        contain an object.
  --model-checkpoint    The model to evaluate.
  --data-dir            Location of the data to evluate with.

Customized discovery evaluation.

The discovery based experiments can be run by setting the following flags during training and using python evaluate_discovery.py when evaluating.

  --discovery           Used when we run the discovery experinent where
                        objects are dropped during training.
  --always-drop-file    Location of list of objects that should always be
                        dropped.
  --subject-droprate    Rate at which subjects are dropped.
  --object-droprate     Rate at which objects are dropped.
  --model-checkpoint    The model to evaluate.
  --data-dir            Location of the data to evluate with.

Contributing.

We welcome everyone to contribute to this reporsitory. Send us a pull request.

License:

The code is under the MIT license. Check LICENSE for details.

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