neurartist 0.1

Ready-to-use artistic deep learning algorithms

Neurartist

A ready-to-use implementation of various Artistic Deep Learning Algorithms.

• Image Style Transfer Using Convolutional Neural Networks, Gatys et. al, 2016

Installation

# It is recommended to install torch/torchvision manually before this command, according to your hardware configuration (see below)
pip install neurartist

Please note that the use of a GPU is recommended, as CNN computations are pretty slow on a CPU.

NB for GPU users: pip ships torch/torchvision with the Cuda Toolkit 9.0. If you use a more recent version of the Cuda Toolkit, see the PyTorch website for instructions on PyTorch installation with another version of the toolkit.

Usage

Console entrypoint

# Then see the builtin help for usage details
neurartist --help

Library

import neurartist

To be added.

Development

Anaconda is strongly recommended:

conda create python=3.7 --name neurartist_env
conda activate neurartist_env

# with gpu
conda install pytorch torchvision cudatoolkit=<your cudatoolkit version> -c pytorch
conda install --file requirements.txt

# with cpu
conda install pytorch-cpu torchvision-cpu -c pytorch
conda install --file requirements.txt

You can then run the main entrypoint directly using:

python -m neurartist --help

Or build and install the wheel file with the --editable flag.

TODO

• Documentation.

• Implement the remaining parts of the jupyter notebook.

• Semantic segmentation as described in this article as to limit spillovers: different approach than guided gram matrices, but same idea of using spatial guidance channels that describe a semantic segmentation of our images.

• More deep-artistic algorithms.