CSGNet: Neural Shape Parser for Constructive Solid Geometry

Here we only include the code for 2D CSGNet. Code for 3D is very similar to 2D and is coming soon.

Please use conda env using environment.yml file.

conda env create -f environment.yml -n CSGNet
source activate CSGNet

To train, update config_synthetic.yml with required arguments. Default arguments are already filled. Then run:
```
python train_synthetic.py
```

To test, update config_synthetic.yml with required arguments. Default arguments are already filled. Then run:

# For top-1 testing
python test_synthetic.py

# For beam-search-k testing
python test_synthetic_beamsearch.py

To train a network using RL, fill up configuration in config_cad.yml or keep the default values and then run:
```
python train_cad.py
```
Make sure that you have trained a network used Supervised setting first.
To test the network trained using RL, fill up configuration in config_cad.yml or keep the default values and then run:
```
# for top-1 decoding
python test_cad.py
```
```
# beam search decoding
python test_cad_beamsearch.py
```
For post processing optmization of program expressions (visually guided search), set the flag REFINE=True in the script test_cad_beam_search.py, although it is little slow. For saving visualization of beam search use SAVE_VIZ=True
To optmize some expressions for cad dataset:
```
# To optmize program expressions from top-1 prediction
python refine_cad.py path/to/exp/to/optmize/exp.txt  path/to/directory/to/save/exp/
```
Note that the expression files here should only have 3k expressions corresponding to the 3k test examples from the CAD dataset.
To optmize program expressions from top-1 prediction
```
python refine_cad_beamsearch.py path/to/exp/to/optmize/exp.txt  path/to/directory/to/save/exp/
```
Note that the expression files here should only have 3k x beam_width expressions corresponding to the 3k test examples from the CAD dataset.

To ask questions, please email.

hbcbh1999 / csgnet Goto Github PK