paddle-msssim 0.0.2

Fast and differentiable MS-SSIM and SSIM for paddle.

Paddle MS-SSIM

Introduction

• Fast and differentiable MS-SSIM and SSIM for Paddle.

  

• Structural Similarity (SSIM):

  

• Multi-Scale Structural Similarity (MS-SSIM):

  

Installation

• via pip

  $ pip install paddle-msssim
  

• via sources

  $ git clone https://github.com/AgentMaker/Paddle-MSSSIM
  
  $ cd Paddle-MSSSIM
  
  $ python setup.py install
  

Requirement

• paddlepaddle / paddlepaddle-gpu >= 2.0.0

Usage

• Basic Usage

  from paddle_msssim import ssim, ms_ssim, SSIM, MS_SSIM
  # X: (N,3,H,W) a batch of non-negative RGB images (0~255)
  # Y: (N,3,H,W)  
  
  # calculate ssim & ms-ssim for each image
  ssim_val = ssim( X, Y, data_range=255, size_average=False) # return (N,)
  ms_ssim_val = ms_ssim( X, Y, data_range=255, size_average=False ) #(N,)
  
  # set 'size_average=True' to get a scalar value as loss. see tests/tests_loss.py for more details
  ssim_loss = 1 - ssim( X, Y, data_range=255, size_average=True) # return a scalar
  ms_ssim_loss = 1 - ms_ssim( X, Y, data_range=255, size_average=True )
  
  # reuse the gaussian kernel with SSIM & MS_SSIM. 
  ssim_module = SSIM(data_range=255, size_average=True, channel=3) # channel=1 for grayscale images
  ms_ssim_module = MS_SSIM(data_range=255, size_average=True, channel=3)
  
  ssim_loss = 1 - ssim_module(X, Y)
  ms_ssim_loss = 1 - ms_ssim_module(X, Y)
  

• Normalized input

  '''
  If you need to calculate MS-SSIM/SSIM on normalized images
  Please denormalize them to the range of [0, 1] or [0, 255] first
  '''
  # X: (N,3,H,W) a batch of normalized images (-1 ~ 1)
  # Y: (N,3,H,W)  
  X = (X + 1) / 2  # [-1, 1] => [0, 1]
  Y = (Y + 1) / 2  
  ms_ssim_val = ms_ssim( X, Y, data_range=1, size_average=False ) #(N,)
  

• Enable nonnegative_ssim

  • For ssim, it is recommended to set nonnegative_ssim=True to avoid negative results. However, this option is set to False by default to keep it consistent with tensorflow and skimage.

  • For ms-ssim, there is no nonnegative_ssim option and the ssim reponses is forced to be non-negative to avoid NaN results.

Test

• Switch to the tests dir

  $ cd ./tests
  

• Benchmark

  $ python comparisons_skimage_tf_torch.py
  

    outputs(AMD Ryzen 4600H): 
  
    ===================================
                Test SSIM
    ===================================
    ====> Single Image
    Repeat 10 times
    sigma=0.0 ssim_skimage=1.000000 (247.7732 ms), ssim_tf=1.000000 (277.2696 ms), ssim_paddle=1.000000 (179.4677 ms), ssim_torch=1.000000 (183.6994 ms)
    sigma=10.0 ssim_skimage=0.932399 (226.1620 ms), ssim_tf=0.932640 (257.2435 ms), ssim_paddle=0.932636 (163.2263 ms), ssim_torch=0.932400 (179.1418 ms)
    sigma=20.0 ssim_skimage=0.786023 (224.1826 ms), ssim_tf=0.786032 (279.2126 ms), ssim_paddle=0.786017 (158.3070 ms), ssim_torch=0.786027 (180.0890 ms)
    sigma=30.0 ssim_skimage=0.637174 (237.5582 ms), ssim_tf=0.637183 (267.6092 ms), ssim_paddle=0.637165 (167.9277 ms), ssim_torch=0.637178 (181.7910 ms)
    sigma=40.0 ssim_skimage=0.515865 (221.0388 ms), ssim_tf=0.515876 (264.3230 ms), ssim_paddle=0.515857 (170.7676 ms), ssim_torch=0.515869 (189.0941 ms)
    sigma=50.0 ssim_skimage=0.422551 (222.6846 ms), ssim_tf=0.422558 (273.1971 ms), ssim_paddle=0.422542 (168.3579 ms), ssim_torch=0.422554 (176.7442 ms)
    sigma=60.0 ssim_skimage=0.351337 (215.1536 ms), ssim_tf=0.351340 (270.5560 ms), ssim_paddle=0.351325 (164.3315 ms), ssim_torch=0.351340 (194.6781 ms)
    sigma=70.0 ssim_skimage=0.295752 (210.0273 ms), ssim_tf=0.295756 (272.1814 ms), ssim_paddle=0.295744 (169.3864 ms), ssim_torch=0.295755 (178.9230 ms)
    sigma=80.0 ssim_skimage=0.253164 (239.2978 ms), ssim_tf=0.253169 (260.8894 ms), ssim_paddle=0.253157 (184.7061 ms), ssim_torch=0.253166 (181.4640 ms)
    sigma=90.0 ssim_skimage=0.219240 (224.7329 ms), ssim_tf=0.219245 (270.3727 ms), ssim_paddle=0.219235 (172.3580 ms), ssim_torch=0.219242 (180.5838 ms)
    sigma=100.0 ssim_skimage=0.192630 (238.8582 ms), ssim_tf=0.192634 (261.4317 ms), ssim_paddle=0.192624 (166.0294 ms), ssim_torch=0.192632 (175.7241 ms)
    Pass!
    ====> Batch
    Pass!
  
  
    ===================================
                Test MS-SSIM
    ===================================
    ====> Single Image
    Repeat 10 times
    sigma=0.0 msssim_tf=1.000000 (534.9398 ms), msssim_paddle=1.000000 (231.7381 ms), msssim_torch=1.000000 (257.3238 ms)
    sigma=10.0 msssim_tf=0.991148 (525.1758 ms), msssim_paddle=0.991147 (213.8527 ms), msssim_torch=0.991101 (243.9299 ms)
    sigma=20.0 msssim_tf=0.967450 (523.3070 ms), msssim_paddle=0.967447 (217.2415 ms), msssim_torch=0.967441 (253.1073 ms)
    sigma=30.0 msssim_tf=0.934692 (538.5145 ms), msssim_paddle=0.934687 (215.2203 ms), msssim_torch=0.934692 (242.5429 ms)
    sigma=40.0 msssim_tf=0.897363 (558.0346 ms), msssim_paddle=0.897357 (219.1107 ms), msssim_torch=0.897362 (249.1027 ms)
    sigma=50.0 msssim_tf=0.859276 (524.8582 ms), msssim_paddle=0.859267 (232.4189 ms), msssim_torch=0.859275 (263.1328 ms)
    sigma=60.0 msssim_tf=0.820967 (512.8726 ms), msssim_paddle=0.820958 (223.7422 ms), msssim_torch=0.820965 (251.9713 ms)
    sigma=70.0 msssim_tf=0.784204 (529.6149 ms), msssim_paddle=0.784194 (213.1742 ms), msssim_torch=0.784203 (244.9676 ms)
    sigma=80.0 msssim_tf=0.748574 (545.3014 ms), msssim_paddle=0.748563 (222.8581 ms), msssim_torch=0.748572 (261.0413 ms)
    sigma=90.0 msssim_tf=0.715980 (538.3886 ms), msssim_paddle=0.715968 (214.4464 ms), msssim_torch=0.715977 (282.6247 ms)
    sigma=100.0 msssim_tf=0.683882 (540.9150 ms), msssim_paddle=0.683870 (218.5596 ms), msssim_torch=0.683880 (244.1856 ms)
    Pass
    ====> Batch
    Pass
  

Example

• Image comparison

  SSIM = 1.0000	SSIM = 0.4225	SSIM = 0.1924

• As a loss function

  • switch to the examples/as_loss dir

    $ cd ./examples/as_loss
    

  • run the example script as_loss.py

    $ python as_loss.py
    

      Initial ssim: 0.9937540888786316
      step: 1 ssim_loss: 0.993843
      step: 2 ssim_loss: 0.993934
      step: 3 ssim_loss: 0.994021
      step: 4 ssim_loss: 0.994106
      step: 5 ssim_loss: 0.994190
      ...
      step: 81 ssim_loss: 0.999762
      step: 82 ssim_loss: 0.999785
      step: 83 ssim_loss: 0.999862
      step: 84 ssim_loss: 0.999874
      step: 85 ssim_loss: 0.999884
      step: 86 ssim_loss: 0.999892
      step: 87 ssim_loss: 0.999912
    

  • result

    Input	Output

  • See examples/as_loss/as_loss.py for more details

• Auto Encoder

  • See examples/auto_encoder for more details

References

• SSIM Research

• MS-SSIM Paper

• Matlab Code

• Pytorch Code

• TensorFlow Code

Thanks

• This repo is based on Pytorch MS-SSIM developed by @VainF.

• Thanks to the above project and its developers.