style_editing

INR style editing

The following commands assume that you are executing them from the current directory experiments/style_editing. If you are in the root of the repository, please navigate to the experiments/style_editing directory:

cd experiments/style_editing

Activate the conda environment:

conda activate neural-graphs

Setup

Follow the directions from the INR classification experiment to download the data and preprocess it. The default dataset directory is dataset and it is shared with the inr_classification experiment.

Run the experiment

Now for the fun part! 🚀 To train and evaluate a Neural Graph Transformer (NG-T) model on the MNIST dataset, run the following command:

python main.py model=rtransformer data=mnist

Make sure to check the model configuration in configs/model/rtransformer.yaml and the data configuration in configs/data/mnist.yaml. If you used different paths for the data, you can either overwrite the default paths in configs/data/mnist.yaml or pass the paths as arguments to the command:

python main.py model=rtransformer data=mnist \
  data.dataset_dir=<your-dataset-dir> data.splits_path=<your-splits-path> \
  data.statistics_path=<your-statistics-path>

Training a different model is as simple as changing the model argument! For example, you can train and evaluate a Neural Graph Graph Neural Network (NG-GNN) on MNIST using the following command:

python main.py model=pna data=mnist

Run experiments with scripts

You can also run the experiments using the scripts provided in the scripts directory. For example, to train and evaluate a Neural Graph Transformer (NG-T) model on the MNIST dataset, run the following command:

./scripts/mnist_dilation_rt.sh

This script will run the experiment for 3 different seeds.