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.

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.

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.