We haven't yet refactored our code repository, hence it contains unused functionality. Please do not rely on the default values of the command-line arguments.

The master branch is used to train the model we used in the paper for the tables and figures. The development branch implements some ideas improving our model.

Our codebase is in Python3 and using Tensorflow 2.1. We suggest creating a new virtual environment.

• The required packages can be installed by running pip install -r requirements.txt
• Update PYTHONPATH by running export PYTHONPATH="${PYTHONPATH}:<CODE_PATH>"
• You can optionally set environment variables or alternatively use FLAGS.
  • COSE_DATA_DIR or --data_dir: Path to data files.
  • COSE_LOG_DIR or --experiment_dir: Path to experiment/model files.
  • COSE_EVAL_DIR or --eval_dir: Path to save model evaluation results. This is required only when the evaluation scripts are called.

We use DiDi dataset diagram drawings without text. Please skip their preprocessing steps as we provide it. Move the .NDJSON file to COSE_DATA_DIR/didi_wo_text/.

• Run data_scripts/didi_json_to_tfrecords.py to create TFRecord files required for model training and evaluation. Set DATA_DIR variable in this script to COSE_DATA_DIR/didi_wo_text/.

• Run python data_scripts/calculate_data_statistics.py to create data statistics file required for model to apply data normalization. Set DATA_DIR variable in this script to COSE_DATA_DIR/didi_wo_text/.

Similarly, QuickDraw dataset can also be used. Note that our model require raw files.

In training_commands.json file, we provide commands for training our main and some of the ablation models. For example, our model can be trained by running

python ink_training_eager_predictive.py --experiment_id <UNIQUE_ID> --gt_targets --use_start_pos --num_pred_inputs 32 --stop_predictive_grad --pred_input_type hybrid --stroke_loss nll_gmm --n_t_samples 4 --batch_size 128 --affine_prob 0.3 --resampling_factor 2 --scale_factor 0 --grad_clip_norm 1 --encoder_model transformer --transformer_scale --transformer_pos_encoding --transformer_layers 6 --transformer_heads 4 --transformer_dmodel 64 --transformer_hidden_units 256 --transformer_dropout 0.0 --latent_units 8 --decoder_model t_emb --decoder_dropout 0.0 --decoder_layers 4 --decoder_hidden_units 512,512,512,512 --predictive_model transformer --learning_rate_type transformer --p_transformer_layers 6 --p_transformer_heads 4 --p_transformer_dmodel 64 --p_transformer_hidden_units 256 --p_transformer_dropout 0.0 --p_transformer_scale --embedding_loss nll_gmm --embedding_gmm_components 10 --loss_predicted_embedding --loss_reconstructed_ink --position_model transformer --data_name didi_wo_text --metadata_type position --disable_pen_loss --mask_encoder_pen

where you are expected to pass a unique identifier (--experiment_id). We recommend using timestamp (i.e., output of date +%s).

Qualitative and quantitative evaluation can be done easily by running

python eval.py --model_ids <UNIQUE_ID> --qualitative --quantitative --embedding_analysis

where UNIQUE_ID is the same as above.

You can download our main model and run evaluation script as explained above.

An interactive demo is provided in the smarting-js folder, using the pre-trained models shared above.

@article{aksan2020cose,
  title={CoSE: Compositional Stroke Embeddings},
  author={Aksan, Emre and Deselaers, Thomas and Tagliasacchi, Andrea and Hilliges, Otmar},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

@article{gervais2020didi,
  title={The DIDI dataset: Digital Ink Diagram data},
  author={Gervais, Philippe and Deselaers, Thomas and Aksan, Emre and Hilliges, Otmar},
  journal={arXiv preprint arXiv:2002.09303},
  year={2020}
}