Generative Neural Visual Artist (GeNeVA): GeNeVA-GAN Training Code

Code to train and evaluate the GeNeVA-GAN model for the GeNeVA task proposed in our ICCV 2019 paper Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction.

GeNeVA Task	GeNeVA-GAN

Example images generated by GeNeVA-GAN on CoDraw (top row) and i-CLEVR (bottom row); shown with the provided instructions

Setup

All the models in the paper were trained and tested on 2 NVIDIA P100 GPUs.

1. Generate data folder for CoDraw and i-CLEVR datasets

See GeNeVA - Datasets - Generation Code

2. Install Miniconda

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
rm Miniconda3-latest-Linux-x86_64.sh

3. Create a conda environment for this repository

conda env create -f environment.yml

4. Activate the environment

source activate geneva  # or `conda activate geneva` for newer conda installs

5. Run setup

python setup.py install

6. Run visdom

visdom

Training progress for all the experiments can be tracked in visdom which by default starts at http://localhost:8097/.

Training the object detector and localizer

python scripts/train_object_detector_localizer.py --num-classes=24 --train-hdf5=../GeNeVA_datasets/data/iCLEVR/clevr_obj_train.h5 --valid-hdf5=../GeNeVA_datasets/data/iCLEVR/clevr_obj_val.h5 --cuda-enabled  # for i-CLEVR
python scripts/train_object_detector_localizer.py --num-classes=58 --train-hdf5=../GeNeVA_datasets/data/CoDraw/codraw_obj_train.h5 --valid-hdf5=../GeNeVA_datasets/data/CoDraw/codraw_obj_val.h5 --cuda-enabled  # for CoDraw

Note: The above commands also have several options, which can be found in the python script, that need to be set. Batch size (--batch-size) is not per-GPU but combined across GPUs.

This trains the object detector and localizer model used for evaluating GeNeVA-GAN on Precision, Recall, F1-Score, and rsim metrics. For comparison with results in our paper, you should skip training the model yourself and download the pre-trained models (iclevr_inception_best_checkpoint.pth and codraw_inception_best_checkpoint.pth) from the GeNeVA Project Page.

Training on CoDraw

Modify geneva/config.yml and example_args/codraw-d-subtract.args if needed and run:

python geneva/inference/train.py @example_args/codraw-d-subtract.args

Training on i-CLEVR

Modify geneva/config.yml and example_args/iclevr-d-subtract.args if needed and run:

python geneva/inference/train.py @example_args/iclevr-d-subtract.args

Evaluating a trained model on CoDraw test set

You will have to add the line --load_snapshot=</path/to/trained/model> to example_args/codraw-d-subtract.args to specify the checkpoint to load from and then run:

python geneva/inference/test.py @example_args/codraw-d-subtract.args

Evaluating a trained model on i-CLEVR test set

You will have to add the line --load_snapshot=</path/to/trained/model> to example_args/iclevr-d-subtract.args to specify the checkpoint to load from and then run:

python geneva/inference/test.py @example_args/iclevr-d-subtract.args

Reference

If you use this code or the GeNeVA datasets as part of any published research, please cite the following paper:

Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou, Yoshua Bengio, and Graham W. Taylor. "Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction". Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2019.

@InProceedings{El-Nouby_2019_ICCV,
    author    = {El{-}Nouby, Alaaeldin and Sharma, Shikhar and Schulz, Hannes and Hjelm, Devon and El Asri, Layla and Ebrahimi Kahou, Samira and Bengio, Yoshua and Taylor, Graham W.},
    title     = {Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction},
    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
    month     = {Oct},
    year      = {2019}
}

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License

See LICENSE.txt. For Third Party Notices, see NOTICE.txt.