PyTorch Image Quality Assessment
Project description
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.
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