pystiche 1.0.1

Description:

pystiche 1.0.1

package


citation


code


tests


docs






pystiche
pystiche (pronounced
/ˈpaɪˈstiʃ/ ) is a
framework for
Neural Style Transfer (NST)
built upon PyTorch. The name of the project is a pun on
pastiche meaning:

A pastiche is a work of visual art […] that imitates the style or character of
the work of one or more other artists. Unlike parody, pastiche celebrates, rather
than mocks, the work it imitates.


pystiche has similar goals as Deep Learning (DL) frameworks such as PyTorch:


Accessibility
Starting off with NST can be quite overwhelming due to the sheer amount of
techniques one has to know and be able to deploy. pystiche aims to provide an
easy-to-use interface that reduces the necessary prior knowledge about NST and DL
to a minimum.




Reproducibility
Implementing NST from scratch is not only inconvenient but also error-prone.
pystiche aims to provide reusable tools that let developers focus on their
ideas rather than worrying about bugs in everything around it.






Installation
pystiche is a proper Python package and can be installed with pip. The latest
release can be installed with
pip install pystiche


Usage
pystiche makes it easy to define the optimization criterion for an NST task fully
compatible with PyTorch. For example, the banner above was generated with the following
criterion:
from pystiche import enc, loss

mle = enc.vgg19_multi_layer_encoder()

perceptual_loss = loss.PerceptualLoss(
content_loss=loss.FeatureReconstructionLoss(
mle.extract_encoder("relu4_2")
),
style_loss=loss.MultiLayerEncodingLoss(
mle,
("relu1_1", "relu2_1", "relu3_1", "relu4_1", "relu5_1"),
lambda encoder, layer_weight: ops.GramOLoss(
encoder, score_weight=layer_weight
),
score_weight=1e3,
),
)
For the full example, head over to the example
NST with pystiche.


Documentation
For

detailed installation instructions,
a gallery of usage examples,
the API reference,
the contributing guidelines,

or anything else, head over to the documentation.


Citation
If you use this software, please cite it as
@Article{ML2020,
author = {Meier, Philip and Lohweg, Volker},
journal = {Journal of Open Source Software {JOSS}},
title = {pystiche: A Framework for Neural Style Transfer},
year = {2020},
doi = {10.21105/joss.02761},
}