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
If you do not have Pipenv installed, run the following:
pip install pipenv
The Pipenv dependencies can be installed within a Pipenv with the following commands:
pipenv install
Pytorch requires different versions depending on the machine it is running on. Therefore it is not included in the Pipenv by default. To install Pytorch, generate the appropriate Pip command on the Pytorch website then run it within the Pipenv by prepending pipenv run to it. For example:
pipenv run pip install torch==1.7.1+cpu torchvision==0.8.2+cpu torchaudio===0.7.2 -f https://download.pytorch.org/whl/torch_stable.html
The following instructions are not recommended unless you are unable to install a Python version compatible with the Pipenv.
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.