ProtoTrees

Refactored version of https://github.com/M-Nauta/ProtoTree and parts of https://github.com/cfchen-duke/ProtoPNet to make them more modular and easier to use. This will probably be turned into a Python package and moved to a new repository.

Setup

For all datasets

1. Create a Python >=3.10 environment.
2. Install requirements from requirements.txt and requirements-download.txt (e.g. pip install -r requirements.txt).
3. Install Graphviz. With the current code you need to be able to call dot from the terminal.
4. You can train the tree model and see its performance on the test set with python train_prototree.py.

For CUB dataset

1. Run cub_download.py.
2. Run cub_preprocess.py.
3. (Optional, but recommended) Download a ResNet50 pretrained on iNaturalist2017 (filename on Google Drive: BBN.iNaturalist2017.res50.180epoch.best_model.pth) and place it in the folder src/features/state_dicts.

For development

Currently, all these steps are only done manually on a development machine. We should set up a pipeline that does these things automatically and reproducibly.

1. Install requirements from requirements-dev.txt.
2. You can lint the code with black src tests.
3. You can check types with MYPYPATH=src mypy src tests --explicit-package-bases --check-untyped-defs. Note that it will be helpful to run mypy --install-types beforehand.
4. You can run tests with the command PYTHONPATH=src pytest.