Model Mime

M2 (Model Mime) is a generator that, given a dataset of models (that conform a meta-model) and a set of addition edit operations, generates models that are similar to the dataset under consideration.

Requirements 🛠

This generator has been constructed using Python. Thus, you need Python 3.8.X and install the requirements listed in this requirements.txt. I recommend you first generate a virtual environment (with conda) and then install the requirements.

conda create -n <m2_env> python=3.8
conda activate <m2_env>
sudo apt-get install graphviz graphviz-dev
pip install -r requirements.txt

The generator uses PyTorch and PyTorch Geometric. The versions that were used when developing the project were:

• torch-1.11.0+cu102
• torchvision-0.12.0+cu102
• torchaudio-0.11.0
• torch-geometric-2.0.4
• torch-scatter-2.0.9
• torch-sparse-0.6.13
• torch-spline-conv-1.2.1

Feel free to use a more suitable version.

Running the generator 🚀

In the repository you can find a main.py script that is in charge of running everything. To train our generator you can do the following:

python main.py --train 
    --training_dataset <training_dataset>
    --metamodel <metamodel>
    --root_object <root_object>
    --model_path <model_path>

• training_dataset: the folder where the training dataset is located.
• metamodel: the path to the meta-model.
• root_object: the root object of the meta-model (that contains everything).
• model_path: the folder where the trained neural network will be stored.

To generate models using the trained generator:

python main.py --inference
    --metamodel <metamodel>
    --root_object <root_object>
    --model_path <model_path>
    --max_size <max_size>
    --n_samples <n_samples>
    --output_path <output_path>

• max_size: the maximum size of the generated models.
• n_samples: the number of models that will be generated.
• output_path: the folder where the generated models will be placed.