The associated writeup can be found here

• DGCNN Networks are in dgcnn/
• Fusion networks are in fusion/
• geomat.py processes the dataset and facilitates feature extraction for some models due to torch_geometric limitations

Note: Tested on Ubuntu 20.04 w/CUDA 11.3 and Python 3.8.12. If using another CUDA version/OS please see the PyTorch and pytorch-geometric install docs and replace the torch* entries in requirements.txt

python3 -m venv venv
source ./venv/bin/activate
pip install -r requirements.txt

To install torch-geometric you may need the following env variables to include cuda as follows:

export PATH=/usr/local/cuda/bin:$PATH
export CPATH=/usr/local/cuda/include:$CPATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH

The GeoMat dataset can be found here. You must download all 19 zip files (one per class) and extract the folders to data/geomat/raw.

tensorboard --logdir=runs

python -m geomat_classification_v1
python -m geomat_classification_v2
python -m geomat_classification_v3
python -m geomat_classification_v4
python -m deepten

python -m fusion.texture_train
python -m fusion.geometric_train
python -m fusion.fusion_train

To clear data from a model:

rm -rf runs/geomat_classification_v3 && rm data/checkpoints/geomat_classification_v3*

To clear features:

rm -f data/fusion/2d/test/* && rm -f data/fusion/2d/train/*

geomat_classification_v7: (DG-V1), 1205075 params geomat_classification_v1: (DG-V2), 1279251 params geomat_classification_v6: (DG-V3), 1525075 params, 196259539 params (backbone) geomat_classification_v8: (DG-V4), 926675 params, 87585939 params (backbone)

To get number of params: print(sum(dict((p.data_ptr(), p.numel()) for p in model.parameters()).values()))

The ConvNeXt image backbone and corresponding pre-trained weights are taken from here

Implementation of the MUNEGC operator and geometric + fusion networks is adapted from here