The mann-pytorch project is a PyTorch implementation of the Mode-Adaptive Neural Networks (MANN) architecture, originally proposed in H. Zhang, S. Starke, T. Komura, and J. Saito, “Mode-adaptive neural networks for quadruped motion control,” ACM Trans. Graph., vol. 37, no. 4, pp. 1–11, 2018.
We cannot guarantee stable API
Install python3, pip and venv via:
sudo apt-get install python3-pip python3-venvClone and install the repo in a virtual environment:
python3 -m venv mann-pytorch-env
source mann-pytorch-env/bin/activate
git clone https://github.com/ami-iit/mann-pytorch.git
cd mann-pytorch
pip install -r requirements.txt
pip install .You can execute a sample training script by:
cd mann-pytorch/scripts
python3 training.pyThe training data will be periodically stored in a dedicated mann-pytorch/models/storage_<training_start_time> folder. You can also monitor the training progress by:
cd mann-pytorch/models/storage_<training_start_time>
python3 -m tensorboard.main --logdir=logsYou can execute a sample testing script by:
cd mann-pytorch/scripts
python3 testing.pyThe average loss of the learned model on the testing dataset will be printed. Moreover, you will be able to inspect the learned model performances by comparing the ground truth and the predicted output on each instance of the testing dataset.
mann-pytorch is an open-source project, for which contributions are welcome. Open an issue with your feature request. Then, you can also proceed with a Pull-Request!