Visual novelty detector for the Polycraft domain.
This repository utilizes Git submodules so utilize git clone --recurse-submodules instead of git clone when first downloading the repository. If this command was forgotten when cloning, run the following from inside the repository:
git submodule update --init --recursive
The following instructions should be done with Python 3.10. If you do not have Pipenv installed, run the following:
pip install pipenv
The dependencies can be installed within a Pipenv with the following commands:
pipenv install --categories "packages torch_cpu"
PyTorch may require different versions depending on the machine it is running on. The default command is for non-CUDA machines while swapping torch_cpu for torch_cu116 installs PyTorch for CUDA 11.6. If a non-default version of PyTorch is required then generate the appropriate Pip command on the PyTorch website then run it within the Pipenv by prepending pipenv run to it.
To install pretrained models run:
pipenv run python scripts/download_models.py
The following instructions are not recommended unless you are unable to install Python 3.10 with Pipenv on the computer.
For this installation Pytorch will be installed in the Conda environment using the appropriate command according to the Pytorch website. For example:
conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch
Then the Pipenv must be configured for the Conda environment before install:
pipenv --python=$(conda run which python) --site-packages
pipenv install
To run unit tests, run the following command:
pipenv run python -m pytest
The root folder should only contain the project README and configuration files.
Saved copies of Pytorch models whose Tensorboard data is stored in the runs folder.
Python module for Polycraft novelty detection. All Python code for the repository should be within this folder and be accessible through the Python module interface.
Tensorboard data for the Pytorch models saved in the models folder.
Git submodules used as dependencies for the project.
Automated Pytest tests for polycraft_nov_det, divided by test subject.
Model training for the autoencoder can be done by executing the module. To view available configurations run:
pipenv run python -m polycraft_nov_det -h
The remaining model training and testing code is found in polycraft_nov_det/baselines and can be run via the following command while in the repository root folder:
pipenv run python polycraft_nov_det/baselines/<method>.py