QualityAE

Code for "IDLat: An Importance-Driven Latent Generation Method for Scientific Data", IEEE Transactions on Visualization and Computer Graphics (IEEE VIS 2022).

To train IDLat on Vortex dataset, run

mkdir ./results/vortex_600_beta5_a7
OUTDIR=./results/vortex_600_beta5_a7
python -u train.py --config=./configs/config_vortex.yaml --name=vortex_600_beta5_a7 --train  > ${OUTDIR}/vortex_600_beta5_a7.log

To evalute IDLat on Vortex dataset and generate latent representations with uniform Importance Map (e.g., Importance value = 0.9), run

python eval.py --config=./configs/config_vortex.yaml \
               --snapshot ./results/vortex_600_beta5_a7/snapshots/best.pt \
               --tqdm \
               --output_dir ./results/vortex_600_beta5_a7/outputs/ \
               --map_value 0.9 \
               --map_name 'uni09' 

To evalute IDLat on Vortex dataset and generate latent representations with isosurface distance map (e.g., isovalue = 7), run

python eval.py --config=./configs/vortex/config_vortex2.yaml \
               --snapshot ./results/vortex_600_beta5_a7/snapshots/best.pt \
               --tqdm \
               --output_dir ./results/vortex_600_beta5_a7/outputs/ \
               --map_name 'iso7' 

Please modify configure file as needed.

Citation

If you use this code for your research, please cite our paper.

@ARTICLE{shen2022IDLat,  
  title={IDLat: An Importance-Driven Latent Generation Method for Scientific Data},   
  author={Shen, Jingyi and Li, Haoyu and Xu, Jiayi and Biswas, Ayan and Shen, Han-Wei},  
  journal={IEEE Transactions on Visualization and Computer Graphics},   
  year={2022},  
  pages={1-11},  
  doi={10.1109/TVCG.2022.3209419}}