Experiments for General E(2)-Equivariant Steerable CNNs

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Paper | Library

Getting Started - Environment

First, you can set up a Conda environment containing some packages required

conda create --name e2exp python=3.6
source activate e2exp

conda install -y pytorch=1.3 torchvision cudatoolkit=10.0 -c pytorch
conda install -y -c conda-forge matplotlib
conda install -y scipy=1.5 pandas scikit-learn=0.23
conda install -y -c anaconda sqlite

Now, we add the e2cnn library. Since the environment has Python 3.6, we clone the legacy_py3.6 branch.

NOTE: make sure you are in the ./experiments/ folder before running the following commands.

mkdir tmp_e2cnn
cd tmp_e2cnn
git clone --single-branch --branch legacy_py3.6 https://github.com/QUVA-Lab/e2cnn
mv e2cnn/e2cnn ../e2cnn
cd ..
rm -rf tmp_e2cnn

If you use Python 3.7 or higher, you can install the library just using

pip install e2cnn

These commands are already included in the file setting_up_env.sh, so you can also just run

cd experiments
./setting_up_env.sh

Getting Started - Datasets

To automatically download the MNIST variants datasets, you can run the following commands (assuming you are in the ./experiments/ folder):

cd datasets
./download_mnist.sh

source activate e2exp

cd mnist_rot
python convert.py

cd ../mnist_fliprot
python convert.py

cd ../mnist12k
python convert.py

Getting Started - Experiments

All the experiments can be run automatically through the following few scripts (assuming you are in the ./experiments/ folder).

To run all the model benchmarking experiments on transformed MNIST datasets:

./mnist_bench.sh

To run the MNIST experiments with group restriction:

./mnist_restrict.sh

To run the competitive MNIST experiments:

./mnist_final.sh

To run the CIFAR10 and the CIFAR100 experiments:

./cifar_experiments.sh

To run the experiments on the full STL10 dataset:

./stl10_experiments.sh

To run the data ablation study on STL10

./stl10_ablation.sh

You can find more details about the single experiments in each bash file.

Experiments' logs and results are stored in a new ./results folder. A summary of all experiments can be printed with the print_results.py script.

Cite

The development of the library and the experiments was part of the work done for our paper General E(2)-Equivariant Steerable CNNs. Please cite this work if you use our code:

@inproceedings{e3cnn,
    title={{General E(2)-Equivariant Steerable CNNs}},
    author={Weiler, Maurice and Cesa, Gabriele},
    booktitle={Conference on Neural Information Processing Systems (NeurIPS)},
    year={2019},
}

Feel free to contact us.

License

This code and the e2cnn library are distributed under BSD Clear license. See LICENSE file.