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Measures and metrics for image2image tasks. PyTorch.

Project description

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PyTorch Image Quality (PIQ) is not endorsed by Facebook, Inc.;

PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

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PyTorch Image Quality (PIQ) is a collection of measures and metrics for image quality assessment. PIQ helps you to concentrate on your experiments without the boilerplate code. The library contains a set of measures and metrics that is continually getting extended. For measures/metrics that can be used as loss functions, corresponding PyTorch modules are implemented.

We provide:

  • Unified interface, which is easy to use and extend.

  • Written on pure PyTorch with bare minima of additional dependencies.

  • Extensive user input validation. You code will not crash in the middle of the training.

  • Fast (GPU computations available) and reliable.

  • Most metrics can be backpropagated for model optimization.

  • Supports python 3.6-3.8.

PIQ was initially named PhotoSynthesis.Metrics.

Installation

PyTorch Image Quality (PIQ) can be installed using pip, conda or git.

If you use pip, you can install it with:

$ pip install piq

If you use conda, you can install it with:

$ conda install piq -c photosynthesis-team -c conda-forge -c PyTorch

If you want to use the latest features straight from the master, clone PIQ repo:

git clone https://github.com/photosynthesis-team/piq.git
cd piq
python setup.py install

Documentation

The full documentation is available at https://piq.readthedocs.io.

Usage Examples

Image-Based metrics

The group of metrics (such as PSNR, SSIM, BRISQUE) takes an image or a pair of images as input to compute a distance between them. We have a functional interface, which returns a metric value, and a class interface, which allows to use any metric as a loss function.

import torch
from piq import ssim, SSIMLoss

x = torch.rand(4, 3, 256, 256, requires_grad=True)
y = torch.rand(4, 3, 256, 256)

ssim_index: torch.Tensor = ssim(x, y, data_range=1.)

loss = SSIMLoss(data_range=1.)
output: torch.Tensor = loss(x, y)
output.backward()

For a full list of examples, see image metrics examples.

Distribution-Based metrics

The group of metrics (such as IS, FID, KID) takes a list of image features to compute the distance between distributions. Image features can be extracted by some feature extractor network separately or by using the compute_feats method of a class.

Note:

compute_feats consumes a data loader of a predefined format.

import torch
from torch.utils.data import DataLoader
from piq import FID

first_dl, second_dl = DataLoader(), DataLoader()
fid_metric = FID()
first_feats = fid_metric.compute_feats(first_dl)
second_feats = fid_metric.compute_feats(second_dl)
fid: torch.Tensor = fid_metric(first_feats, second_feats)

If you already have image features, use the class interface for score computation:

import torch
from piq import FID

x_feats = torch.rand(10000, 1024)
y_feats = torch.rand(10000, 1024)
msid_metric = MSID()
msid: torch.Tensor = msid_metric(x_feats, y_feats)

For a full list of examples, see feature metrics examples.

List of metrics

Full-Reference (FR)

Acronym

Year

Metric

PSNR

-

Peak Signal-to-Noise Ratio

SSIM

2003

Structural Similarity

MS-SSIM

2004

Multi-Scale Structural Similarity

IW-SSIM

2011

Information Content Weighted Structural Similarity Index

VIFp

2004

Visual Information Fidelity

FSIM

2011

Feature Similarity Index Measure

SR-SIM

2012

Spectral Residual Based Similarity

GMSD

2013

Gradient Magnitude Similarity Deviation

VSI

2014

Visual Saliency-induced Index

DSS

2015

DCT Subband Similarity Index

-

2016

Content Score

-

2016

Style Score

HaarPSI

2016

Haar Perceptual Similarity Index

MDSI

2016

Mean Deviation Similarity Index

MS-GMSD

2017

Multi-Scale Gradient Magnitude Similarity Deviation

LPIPS

2018

Learned Perceptual Image Patch Similarity

PieAPP

2018

Perceptual Image-Error Assessment through Pairwise Preference

DISTS

2020

Deep Image Structure and Texture Similarity

No-Reference (NR)

Acronym

Year

Metric

TV

1937

Total Variation

BRISQUE

2012

Blind/Referenceless Image Spatial Quality Evaluator

Distribution-Based (DB)

Acronym

Year

Metric

IS

2016

Inception Score

FID

2017

Frechet Inception Distance

GS

2018

Geometry Score

KID

2018

Kernel Inception Distance

MSID

2019

Multi-Scale Intrinsic Distance

PR

2019

Improved Precision and Recall

Benchmark

As part of our library we provide code to benchmark all metrics on a set of common Mean Opinon Scores databases. Currently we support TID2013, KADID10k and PIPAL. You need to download them separately and provide path to images as an argument to the script.

Here is an example how to evaluate SSIM and MS-SSIM metrics on TID2013 dataset:

python3 tests/results_benchmark.py --dataset tid2013 --metrics SSIM MS-SSIM --path ~/datasets/tid2013 --batch_size 16

Below we provide a comparison between Spearman’s Rank Correlation cCoefficient (SRCC) values obtained with PIQ and reported in surveys. Closer SRCC values indicate the higher degree of agreement between results of computations on given datasets. We do not report Kendall rank correlation coefficient (KRCC) as it is highly correlated with SRCC and provides limited additional information. We do not report Pearson linear correlation coefficient (PLCC) as it’s highly dependent on fitting method and is biased towards simple examples.

For metrics that can take greyscale or colour images, c means chromatic version.

TID2013

KADID10k

PIPAL

Source

PIQ / Reference

PIQ / Reference

PIQ / Reference

PSNR

0.69 / 0.69 TID2013

0.68 / -

0.41 / 0.41 PIPAL

SSIM

0.72 / 0.64 TID2013

0.72 / 0.72 KADID10k

0.50 / 0.53 PIPAL

MS-SSIM

0.80 / 0.79 TID2013

0.80 / 0.80 KADID10k

0.55 / 0.46 PIPAL

IW-SSIM

0.78 / 0.78 Eval2019

0.85 / 0.85 KADID10k

0.60 / -

VIFp

0.61 / 0.61 TID2013

0.65 / 0.65 KADID10k

0.50 / -

FSIM

0.80 / 0.80 TID2013

0.83 / 0.83 KADID10k

0.59 / 0.60 PIPAL

FSIMc

0.85 / 0.85 TID2013

0.85 / 0.85 KADID10k

0.59 / -

SR-SIM

0.81 / 0.81 Eval2019

0.84 / 0.84 KADID10k

0.57 / -

SR-SIMc

0.87 / -

0.87 / -

0.57 / -

GMSD

0.80 / 0.80 MS-GMSD

0.85 / 0.85 KADID10k

0.58 / -

VSI

0.90 / 0.90 Eval2019

0.88 / 0.86 KADID10k

0.54 / -

DSS

0.79 / 0.79 Eval2019

0.86 / 0.86 KADID10k

0.63 / -

Content

0.71 / -

0.72 / -

0.45 / -

Style

0.54 / -

0.65 / -

0.34 / -

HaarPSI

0.87 / 0.87 HaarPSI

0.89 / 0.89 KADID10k

0.59 / -

MDSI

0.89 / 0.89 MDSI

0.89 / 0.89 KADID10k

0.59 / -

MS-GMSD

0.81 / 0.81 MS-GMSD

0.85 / -

0.59 / -

MS-GMSDc

0.89 / 0.89 MS-GMSD

0.87 / -

0.59 / -

LPIPS-VGG

0.67 / 0.67 DISTS

0.72 / -

0.57 / 0.58 PIPAL

PieAPP

0.84 / 0.88 DISTS

0.87 / -

0.70 / 0.71 PIPAL

DISTS

0.81 / 0.83 DISTS

0.88 / -

0.62 / 0.66 PIPAL

BRISQUE

0.37 / 0.84 Eval2019

0.33 / 0.53 KADID10k

0.21 / -

IS

0.26 / -

0.25 / -

0.09 / -

FID

0.67 / -

0.66 / -

0.18 / -

KID

0.42 / -

0.66 / -

0.12 / -

MSID

0.21 / -

0.32 / -

0.01 / -

GS

0.37 / -

0.37 / -

0.02 / -

Unlike FR and NR IQMs, designed to compute an image-wise distance, the DB metrics compare distributions of sets of images. To address these problems, we adopt a different way of computing the DB IQMs proposed in https://arxiv.org/abs/2203.07809. Instead of extracting features from the whole images, we crop them into overlapping tiles of size 96 × 96 with stride = 32. This pre-processing allows us to treat each pair of images as a pair of distributions of tiles, enabling further comparison. The other stages of computing the DB IQMs are kept intact.

Assertions

In PIQ we use assertions to raise meaningful messages when some component doesn’t receive an input of the expected type. This makes prototyping and debugging easier, but it might hurt the performance. To disable all checks, use the Python -O flag: python -O your_script.py

Roadmap

See the open issues for a list of proposed features and known issues.

Contributing

If you would like to help develop this library, you’ll find more information in our contribution guide.

Citation

If you use PIQ in your project, please, cite it as follows.

@misc{piq,
  title={{PyTorch Image Quality}: Metrics and Measure for Image Quality Assessment},
  url={https://github.com/photosynthesis-team/piq},
  note={Open-source software available at https://github.com/photosynthesis-team/piq},
  author={Sergey Kastryulin and Dzhamil Zakirov and Denis Prokopenko},
  year={2019},
}

Contacts

Sergey Kastryulin - @snk4tr - snk4tr@gmail.com

Jamil Zakirov - @zakajd - djamilzak@gmail.com

Denis Prokopenko - @denproc - d.prokopenko@outlook.com

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