Dataset | arXiv

CanvasVAE

Official tensorflow implementation of the following work.

Kota Yamaguchi, CanvasVAE: Learning to Generate Vector Graphic Documents, ICCV 2021

Content

• bin: Job launchers
• src/preprocess: Preprocessing jobs to fetch and build TFRecord dataset
• src/pixel-vae: PixelVAE trainer
• src/canvas-vae: CanvasVAE trainer and evaluation

Setup

Install python dependencies. Perhaps this should be done inside venv.

pip install -r requirements.txt

Note that Tensorflow has a version-specific system requirement for GPU environment. Check if the compatible CUDA/CuDNN runtime is installed.

Crello experiments

Download and extract Crello dataset. The following script will download the dataset to data/crello-dataset directory.

bin/download_crello.sh

Prepare image data and learn a PixelVAE model for image embedding. The resulting image encoder will be saved to data/pixelvae/encoder. This training takes long. We recommend sufficient GPU resources to run this step (e.g., Tesla P100x4).

bin/generate_crello_image.sh
bin/train_pixelvae.sh

The training progress can be monitored via tensorboard:

tensorboard --logdir tmp/pixelvae/jobs

Once a PixelVAE is trained, build the crello document dataset, and learn CanvasVAE models. The trainer script takes a few arguments to control hyperparameters. See src/canvas-vae/canvasvae/main.py for the list of available options. This step can be run in a single GPU environment (e.g., Tesla P100x1).

bin/generate_crello_document.sh
bin/train_canvasvae.sh crello-document --latent-dim 512 --kl 32

The trainer outputs logs, evaluation results, and checkpoints to tmp/canvasvae/jobs/<job_id>. The training progress can be monitored via tensorboard:

tensorboard --logdir tmp/canvasvae/jobs

The resulting models can be further inspected in the notebook.

• notebooks/crello-analysis.ipynb

RICO experiments

Download UI SCREENSHOTS AND HIERARCHIES WITH SEMANTIC ANNOTATIONS dataset first. This seems to require Google account. In the following, we assume the downloaded archive file is placed in tmp/rico_dataset_v0.1_semantic_annotations.zip.

Once downloaded, preprocess and learn CanvasVAE models.

bin/generate_rico.sh tmp/rico_dataset_v0.1_semantic_annotations.zip
bin/train_canvasvae.sh rico --latent-dim 256 --kl 16

The resulting models can be inspected in the notebook.

• notebooks/rico-analysis.ipynb