PIQA

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.

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 API or the repository.