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pytorch-msssim

Differentiable Multi-Scale Structural Similarity (SSIM) index

This small utiliy provides a differentiable MS-SSIM implementation for PyTorch based on Po Hsun Su's implementation of SSIM @ https://github.com/Po-Hsun-Su/pytorch-ssim. At the moment only the product method for MS-SSIM is supported.

Installation

Master branch now only supports PyTorch 0.4 or higher. All development occurs in the dev branch (git checkout dev after cloning the repository to get the latest development version).

To install the current version of pytorch_mssim:

  1. Clone this repo.
  2. Go to the repo directory.
  3. Run python setup.py install

or

  1. Clone this repo.
  2. Copy "pytorch_msssim" folder in your project.

To install a version of of pytorch_mssim that runs in PyTorch 0.3.1 or lower use the tag checkpoint-0.3. To do so, run the following commands after cloning the repository:

git fetch --all --tags
git checkout tags/checkpoint-0.3

Example

Basic usage

import pytorch_msssim
import torch

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
m = pytorch_msssim.MSSSIM()

img1 = torch.rand(1, 1, 256, 256)
img2 = torch.rand(1, 1, 256, 256)

print(pytorch_msssim.msssim(img1, img2))
print(m(img1, img2))

Training

For a detailed example on how to use msssim for optimization, take a look at the file max_ssim.py.

Stability and normalization

MS-SSIM is a particularly unstable metric when used for some architectures and may result in NaN values early on during the training. The msssim method provides a normalize attribute to help in these cases. There are three possible values. We recommend using the value normalize="relu" when training.

  • None : no normalization method is used and should be used for evaluation
  • "relu" : the ssimand mc values of each level during the calculation are rectified using a relu ensuring that negative values are zeroed
  • "simple" : the ssimresult of each iteration is averaged with 1 for an expected lower bound of 0.5 - should ONLY be used for the initial iterations of your training or when averaging below 0.6 normalized score

Currently and due to backward compability, a value of True will equal the "simple" normalization.

Reference

https://ece.uwaterloo.ca/~z70wang/research/ssim/

https://github.com/Po-Hsun-Su/pytorch-ssim

Thanks to z70wang for proposing MS-SSIM and providing the initial implementation, and Po-Hsun-Su for the initial differentiable SSIM implementation for Pytorch.