Artist Style Transfer Via Quadratic Potential

栏目: IT技术 · 发布时间: 4年前

内容简介：In this paper we address the problem of artist style transfer where the painting style of a given artist is applied on a real world photograph. We train our neural networks in adversarial setting via recently introduced quadratic potential divergence for s

Artist Style Transfer Via Quadratic Potential

Rahul Bhalley and Jianlin Su

arXiv paper

Abstract

In this paper we address the problem of artist style transfer where the painting style of a given artist is applied on a real world photograph. We train our neural networks in adversarial setting via recently introduced quadratic potential divergence for stable learning process. To further improve the quality of generated artist stylized images we also integrate some of the recently introduced deep learning techniques in our method. To our best knowledge this is the first attempt towards artist style transfer via quadratic potential divergence. We provide some stylized image samples in the supplementary material. The source code for experimentation was written in PyTorch and is available online in my GitHub repository .

If you find our work, or this repository helpful, please consider citing our work with the following BibTex:

@article{bhalley2019artist,
  title={Artist Style Transfer Via Quadratic Potential},
  author={Bhalley, Rahul and Su, Jianlin},
  journal={arXiv preprint arXiv:1902.11108},
  year={2019}
}

NOTE: Pre-trained models are available in `Google Drive` . Please download it in the root directory of this repository.

Prerequisites

This code was tested in following environment setting:

Python (version >= 3.6.0)
PyTorch (version >= 1.0.0)
Torchvision (version = 0.2.1)

Usage

First clone this repository:

git clone https://github.com/rahulbhalley/cyclegan-qp.git

Getting Datasets

Enter into the cyclegan-qp directory via terminal.

cd cyclegan-qp

To download the datasets (for instance, ukiyoe2photo ) run:

bash download_dataset.sh ukiyoe2photo

Now ukiyoe2photo dataset will be downloaded and unzipped in cyclegan-qp/datasets/ukiyoe2photo directory.

Training & Inference

To train the network set TRAIN = True in config.py and for inference set it to False . Then one may only need to execute the following command in terminal.

python main.py

Configurations

Following is a list of configurable variables (in config.py ) to perform experiments with different settings.

Data

DATASET_DIR - name of directory containing dataset. Default: "datasets" .
DATASET_NAME - name of dataset to use. Default: "vangogh2photo" .
LOAD_DIM - sets the size of images to load. Default: 286 .
CROP_DIM - square crops the images from center. Default: 256 .
CKPT_DIR - name of directory to save checkpoints in. Default: "checkpoints" .
SAMPLE_DIR - directory name where inferred samples will be saved. Default: "samples" .

Quadratic Potential

LAMBDA - see equation (1) in paper . Default: 10.0 .
NORM - see equation (2) in paper . Possible values: "l1" , "l2" . Default: "l1" .

CycleGAN-QP

CYC_WEIGHT - cycle consistency weight. Default: 10.0 .
ID_WEIGHT - identity weight. Default: 0.5 .

Network

N_CHANNELS - number of channels of images in dataset. Set to 3 for RGB and 1 for grayscale. Default: 3 .
UPSAMPLE - set True to use ( Odena et al., 2016 ) technique but False to use vanilla transpose convolution layers in generator networks. Default: True .

Training

RANDOM_SEED - random seed to reproduce the experiments. Default: 12345 .
BATCH_SIZE - batch size for training. Default: 4 .
LR - learning rate. Default: 2e-4 .
BETA1 - hyper-parameter of Adam optimizer. Default: 0.5 .
BETA2 - hyper-parameter of Adam optimizer. Default: 0.999 .
BEGIN_ITER - if 0 the train begins from start but when set to > 0 then training continues from BEGIN_ITER th checkpoint. Default: 0 .
END_ITER - number of iteration for training. Default: 15000 .
TRAIN - set True for training CycleGAN-QP but False to perform inference (for more inference configurations see next subsection). Default: True .

Inference

INFER_ITER - performs inference by loading parameters from this checkpoint. Default: 15000 .
INFER_STYLE - style to be transferred on images. Possible values: "ce" , "mo" , "uk" , "vg" . Default: "vg" .
IMG_NAME - name of image to be performed inference on. Default: "image.jpg" .
IN_IMG_DIR - name of directory containing IMG_NAME . Default: "images" .
OUT_STY_DIR - name of directory to save inferred IMG_NAME . Default: "sty" .
OUT_REC_DIR - name of directory to save recovered (original) IMG_NAME . Default: "rec" .
IMG_SIZE - set None to infer with the original sized IMG_NAME or set some integral value to infer with IMG_SIZE . Default: None .

Logs

ITERS_PER_LOG - iterations duration at which screen logs should be made. Default: 100
ITERS_PER_CKPT - iterations duration at which checkpoints should be saved. Default: 1000