This repository contains the core implementation of our paper:
[Discovering Design Concepts for CAD Sketches](NeurIPS 2022)
Yuezhi Yang,
Hao Pan
Following is the suggested way to install the dependencies of our code:
git clone --recursive https://github.com/yyuezhi/SketchConcept.git
cd SketchConcept
conda create -n sketch_concept python=3.8
conda activate sketch_concept
conda install pytorch==1.13.1 lz4 pytorch-cuda=11.6 matplotlib=3.7.1 -c pytorch -c nvidia
pip install -e SketchGraphs
pip install tensorboard scipy networkx numpy==1.23.4
We provide preprocessed SketchGraph dataset and corresponding index file which filters the sketches we used to train the model. Please download the pretrained models from Google Drive and put them in Data/. For file size issue, the data are splited into two patches of roughly equal size.
We provide the pretrained models for design intent interpretation. Please download the pretrained models from Google Drive and put them in experiment_directory/sample_pretrain_exp/result/network_param.
python train.py -d ./Data/ -e experiment_directory/sample_exp --train_file sketch1_randnorm_sorted.npy
python train.py -d ./Data/ -e experiment_directory/sample_pretrain_exp --train_file sketch1_randnorm_sorted.npy -v True -c 180
python plot_interactive_visualize.py -d ./Data/ -e <experiment_directory> --train_file <train_file> --epoch <epoch_num>
python plot_library_visualize.py -d ./Data/ -e <experiment_directory> --train_file <train_file> --epoch <epoch_num>
python make_graph.py -d ./Data/ -e <experiment_directory> --train_file <train_file> --epoch <epoch_num>
Notice that this program need installation of PyGraphViz. Please install it according to your own environment.
If you find our work useful in your research, please consider citing:
@inproceedings{yang2022sketchconcept,
author = {Yuezhi Yang, Hao Pan},
booktitle = {Advances in Neural Information Processing Systems},
title = {Discovering Design Concepts for CAD Sketches},
volume = {35},
year = {2022}
}