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 ⚡️)
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
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.
| 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 |
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)_/¯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)If you have a question, an issue or would like to contribute, please read our contributing guidelines.
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