piqa 1.3.2

Description:

piqa 1.3.2

PyTorch Image Quality Assessment
PIQA is a collection of PyTorch metrics for image quality assessment in various image processing tasks such as generation, denoising, super-resolution, interpolation, etc. It focuses on the efficiency, conciseness and understandability of its (sub-)modules, such that anyone can easily reuse and/or adapt them to its needs.

PIQA should be pronounced pika (like Pikachu ⚡️)

Installation
The piqa package is available on PyPI, which means it is installable via pip.
pip install piqa

Alternatively, if you need the latest features, you can install it from the repository.
pip install git+https://github.com/francois-rozet/piqa

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. All metrics are differentiable and support CPU and GPU (CUDA).
import torch
import piqa

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

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

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

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

kernel = gaussian_kernel(11, sigma=1.5).repeat(3, 1, 1)
ss, cs = ssim(x, y, kernel=kernel)

For more information, check out the documentation at piqa.readthedocs.io.
Available metrics



Class
Range
Objective
Year
Metric




TV
[0, ∞]
/
1937
Total Variation


PSNR
[0, ∞]
max
/
Peak Signal-to-Noise Ratio


SSIM
[0, 1]
max
2004
Structural Similarity


MS_SSIM
[0, 1]
max
2004
Multi-Scale Structural Similarity


LPIPS
[0, ∞]
min
2018
Learned Perceptual Image Patch Similarity


GMSD
[0, ∞]
min
2013
Gradient Magnitude Similarity Deviation


MS_GMSD
[0, ∞]
min
2017
Multi-Scale Gradient Magnitude Similarity Deviation


MDSI
[0, ∞]
min
2016
Mean Deviation Similarity Index


HaarPSI
[0, 1]
max
2018
Haar Perceptual Similarity Index


VSI
[0, 1]
max
2014
Visual Saliency-based Index


FSIM
[0, 1]
max
2011
Feature Similarity


FID
[0, ∞]
min
2017
Fréchet Inception Distance



Tracing
All metrics of piqa support PyTorch's tracing, which optimizes their execution, especially on GPU.
ssim = piqa.SSIM().cuda()
ssim_traced = torch.jit.trace(ssim, (x, y))

l = 1 - ssim_traced(x, y) # should be faster ¯\_(ツ)_/¯

Assert
PIQA uses type assertions to raise meaningful messages when a metric 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
piqa.utils.set_debug(False)

Contributing
If you have a question, an issue or would like to contribute, please read our contributing guidelines.