In this repository we implement reduced order models for cardiovascular simulations using Graph Neural Networks (GNNs).
Let us first install virtualenv:
pip install virtualenv
Then, from the root of the project:
bash create_venv.sh
This will create a virtual environment gromenv following python packages: matplotlib, vtk, scipy, dgl, torch, sigopt.
To download the data, simply type
bash download_data.sh
in the root directory. This will automatically generate a directory graphs/vtps containing the dataset in vtp format. To inspect these files, use for example Paraview
Within the directory graphs, type
python generate_graph.py $MODELNAME.vtp $MODELNAME.grph
For example,
python generate_graph.py 0063_1001 0063_1001.grph
The graph will be saved in graphs/data.
Within the directory graphs, type
python training.py $MODELNAME
For example,
python training.py 0063_1001
The parameters of the trained model and hyperparameters will be saved in network/models, in a folder named as the date and time when the training was launched.
Within the directory graphs, type
python tester.py $MODELNAME $NETWORKPATH
For example,
python tester.py 0063_1001 models/01.01.1990_00.00.00
This will save comparative plots in the same directory.
In the example, models/01.01.1990_00.00.00 is a model generated after training (see Train a GNN).