piqa 1.1.8

PyTorch Image Quality Assessment

PIQA is not endorsed by Facebook, Inc.; PyTorch, the PyTorch logo and any related marks are trademarks of Facebook, Inc.

PyTorch Image Quality Assessment

The piqa package is a collection of measures and metrics for image quality assessment in various image processing tasks such as denoising, super-resolution, image interpolation, etc. It relies only on PyTorch and takes advantage of its efficiency and automatic differentiation.

PIQA is directly inspired from the piq project, but focuses on the conciseness, readability and understandability of its (sub-)modules, such that anyone can easily reuse and/or adapt them to its needs.

However, conciseness should never be at the expense of efficiency; PIQA's implementations are up to 3 times faster than those of other IQA PyTorch packages like kornia, piq and IQA-pytorch.

PIQA should be pronounced pika (like Pikachu ⚡️)

Installation

The piqa package is available on PyPI, which means it is installable with pip:

pip install piqa

Alternatively, if you need the latest features, you can install it using

pip install git+https://github.com/francois-rozet/piqa

or copy the package directly to your project, with

git clone https://github.com/francois-rozet/piqa
cp -R piqa/piqa <path/to/project>/piqa

Documentation

The documentation of this package is generated automatically using pdoc.

Getting started

In piqa, each metric is associated to a class, child of torch.nn.Module, which has to be instantiated to evaluate the metric.

import torch

# PSNR
from piqa import PSNR

x = torch.rand(5, 3, 256, 256)
y = torch.rand(5, 3, 256, 256)

psnr = PSNR()
l = psnr(x, y)

# SSIM
from piqa import SSIM

x = torch.rand(5, 3, 256, 256, requires_grad=True).cuda()
y = torch.rand(5, 3, 256, 256).cuda()

ssim = SSIM().cuda()
l = 1 - ssim(x, y)
l.backward()

Like torch.nn built-in components, these classes are based on functional definitions of the metrics, which are less user-friendly, but more versatile.

import torch

from piqa.ssim import ssim
from piqa.utils.functional import gaussian_kernel

x = torch.rand(5, 3, 256, 256)
y = torch.rand(5, 3, 256, 256)

kernel = gaussian_kernel(11, sigma=1.5).repeat(3, 1, 1)

l = ssim(x, y, kernel=kernel, channel_avg=False)

Metrics

Acronym	Class	Range	Objective	Year	Metric
TV	TV	[0, ∞]	/	1937	Total Variation
PSNR	PSNR	[0, ∞]	max	/	Peak Signal-to-Noise Ratio
SSIM	SSIM	[0, 1]	max	2004	Structural Similarity
MS-SSIM	MS_SSIM	[0, 1]	max	2004	Multi-Scale Structural Similarity
LPIPS	LPIPS	[0, ∞]	min	2018	Learned Perceptual Image Patch Similarity
GMSD	GMSD	[0, ∞]	min	2013	Gradient Magnitude Similarity Deviation
MS-GMSD	MS_GMSD	[0, ∞]	min	2017	Multi-Scale Gradient Magnitude Similarity Deviation
MDSI	MDSI	[0, ∞]	min	2016	Mean Deviation Similarity Index
HaarPSI	HaarPSI	[0, 1]	max	2018	Haar Perceptual Similarity Index
VSI	VSI	[0, 1]	max	2014	Visual Saliency-based Index
FSIM	FSIM	[0, 1]	max	2011	Feature Similarity

JIT

Most functional components of piqa support PyTorch's JIT, i.e. TorchScript, which is a way to create serializable and optimizable functions from PyTorch code.

By default, jitting is disabled for those components. To enable it, the PIQA_JIT environment variable has to be set to 1. To do so temporarily,

• UNIX-like bash

export PIQA_JIT=1

• Windows cmd

set PIQA_JIT=1

• Microsoft PowerShell

$env:PIQA_JIT=1

Assert

PIQA uses type assertions to raise meaningful messages when an object-oriented component doesn't receive an input of the expected type. This feature eases a lot early prototyping and debugging, but it might hurt a little the performances.

If you need the absolute best performances, the assertions can be disabled with the Python flag -O. For example,

python -O your_awesome_code_using_piqa.py

Alternatively, you can disable PIQA's type assertions within your code with

from piqa.utils import set_debug
set_debug(False)