quantative_style

This is the demo code repository for the WACV 2020 paper "Improving style tranfer with calibrated metrics"

https://arxiv.org/pdf/1910.09447.pdf

Requirements

• Matlab R2015a for Ubuntu 14.04
• Python 3
• PyTorch 0.2.0
• torchvision 0.2.0
• R (for calibrating base EC statistics)

Style and Content Images

Style Images

50 style images presented in the paper are collected from previous work and online sources. Download from the link

See 50 styles from the following links:

50styles_1.pdf 50styles_2.pdf

Content Images

We use 200 test images and their ground truth contour annotations from the BSDS500 dataset. Download all test content images, annotations, and examples of evaluated style transferred images used in the paper:

https://www.dropbox.com/s/nflmpwisbpr1ebw/BSDS500.zip?dl=0

Unzip downloaded file as "./BSDS500" under the home directory of this repo.

Experiment procedure

For each configuration of one style transfer method, we randomly sample 300 combination of "style loss weight"-"content ID" -"style ID" and generate their corresponding style transfer images. These images are used for the following quantitative evaluation of style transfer methods.

Base C statistics

We use an off-the-shelf contour detection method by Arbelaezet al. [1],which estimates the probabiliy of boundaries (Pb) given an image.

The following example evaluate images generated from Universal Style transfer method:

1. open Matlab and change directory to ./wacv2020testscript
2. run evaluation_streamline.m, this will generate Pb maps and evaluation results from 300 style transfer images generated with Universal Style Transfer.
3. retreive evaluation results from ./BSDS500/ucm2/SampleTests/Universal folder.

Breakdown Pb evaluation results for base C statistics:

1. Each folder in ./BSDS500/ucm2/SampleTests represents evaluations on a set style transfer images generated by one configuration of a style transfer method.
2. Take Universal Style Transfer for example,

• PbRsts stores every style transfer images' Pb detection result as a .mat file.
• round1_eval stores Pb precision recall curves for evaluated images, see column names in this function for details.
• round1_eval_sum summarizes Pb evaluation results by style loss weights, all folder includes all 300 images.
  • eval_bdry.txt,eval_bdry_img.txt, eval_bdry_thr.txt stores intermediate results.
  • Universal_everyImgAUC.mat stores base C statistics for all evaluated style transfer images. Each row shows weight, content id, style id, and the base C statistics for one image. Note this repo reports Pb precision-recall curve AUC as the base C statistics, the max F-1 score, which is the final reported score in our paper, can also be generated using this codebase, see this function for details.

Base E statistics

We calculate the minus log KL distance between feature statistics of symthesized image and style image. For the evaluation of KL distance, We also use the PCA to reduce the rank of feature maps. See our paper for the details. The KL formula is based on Multivariate normal distributions of https://en.wikipedia.org/wiki/Kullback–Leibler_divergence To generate the E statistics of smaple images, please run the E_base.ipynb step by step

python E_base.ipynb

The related files and folders for E_base.ipynb:

1. the style images are in style_dir = ./50styles
2. the reference images( or content images) for PCA basis are in Reference_dir = ./content/
3. the sample(or synthesized) images are in source_dir = ./sample/ and are listed in sample.txt
4. the generate E values of 5 layers of each sample are listed in EValue[32, 48, 128, 256, 256].txt
5. the pre-trained VGG model is saved at model_dir = ./Models/ . The model can be downloaded from https://www.dropbox.com/s/xc78chba9ffs82a/vgg_conv.pth?dl=0

Calibrating E statistic and Calibrating C statistic

In section 4.1 of our paper, the admissible models for calibrating E statistic and calibrating C statistic are:

E: 0.7468E1+0.2557E2+2.3768*E3

C: 12.1835*C+0.2979 *E1

TODO: upload calibration code

References [1] P. Arbelaez, M. Maire, C. Fowlkes, and J. Malik. Con-tour detection and hierarchical image segmentation.IEEEtransactions on pattern analysis and machine intelligence,33(5):898–916, 2011.

Citation

Please cite our paper for any purpose of usage.

@article{yeh2019improving,
  title={Improving Style Transfer with Calibrated Metrics},
  author={Yeh, Mao-Chuang and Tang, Shuai and Bhattad, Anand and Zou, Chuhang and Forsyth, David},
  journal={arXiv preprint arXiv:1910.09447},
  year={2019}
}