This is the code repository for the experiments of the paper "A new benchmark for group distribution shifts in hand grasp regression for object manipulation. Can meta-learning raise the bar?" accepted at the DistShift workshop of NeurIPS 2022.
First, create the anaconda environment with conda create -f environment.yaml (or alternatively install all dependencies in a virtualenv).
Then, install pytorch for your CUDA version in this created conda env (1.12.1 is recommended),
such as: conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge.
Download our learn2learn fork, switch to the
branch derivative_order_annealing and in your conda environment you just created, run python setup.py build && python setup.py install in the root of learn2learn.
Download the DexYCB dataset and extract it in the location of your
choice, then update this location in the field dataset_path of all dexycb-related config files
(i.e. conf/experiment/anil_cnn_dexycb.yaml).
Optionally download the ShapeNetCore dataset for the ObMan dataset and update the shapenet_root field in conf/config.yaml.
You can get an overview of all the parameters with ./train_test.py -h (refer to Hydra's documentation for more details).
Run ./train_test.py experiment=<name_of_experiment>; see the list of YAML files in
conf/experiments for experiment names (ommit the .yaml extension).
Use the option test_mode=true to test, and don't forget to specify a trained model with
experiment.saved_model=<path>.
Run the procrustes_analysis.py script.
./analyse_grads.py experiment=anil_cnn_dexycb experiment.hold_out=9 experiment.saved_model=models/handonly/anil_9.tar analyse_tasks=100 hand_only=true
Make sure that experiment.hold_out matches your trained model.