A self-supervised feature extraction trained on the single image.
An example use in a preprint:
- Piotr Migdał, Bartłomiej Olechno, Błażej Podgórski, Level generation and style enhancement - deep learning for game development overview, arXiv:2107.07397.
The preferred way to install is to use a conda environment.
git clone git@github.com:Bartolo1024/Pixel2Vec.git
cd Pixel2Vec
conda env create -f .\environment.yaml
conda activate pixel2vec
Code supports Apple M1 GPU out of the box as torch.device('mps'). On macOS, install it with:
conda env create -f .\environment-m1.yaml
To train the SOTA model on the minesweeper image run command:\
python -m pixel2vec.train --experiment-file experiments/minesweeper.yaml
Note: on Windows 10 you want to set in experiments/[name].yaml file num_workers: 0, otherwise it will be super slow, vide pytorch/pytorch#12831.
Example inputs are placed in subfolders in the data directory.
Each data has its own directory, e.g. data/minesweeper, which contains one or more files.
The model was trained on the given Simcity image. All feature vectors were projected into 3d space, and presented as the RGB image. The result is shown on the bottom.
This project is supported by the EU R&D grant POIR.01.02.00-00-0052/19 for project GearShift – building the engine of the behavior of wheeled motor vehicles and map generation based on artificial intelligence algorithms implemented on the Unreal Engine platform – GAMEINN.