Package: paddle-msssim
Paddle MS-SSIMIntroductionFast and differentiable MS-SSIM and SSIM for Paddle.Structural Similarity (SSIM):Multi-Scale Structural Similarity (MS-SSIM):Installationvia pip$pipinstallpaddle-msssimvia sources$gitclonehttps://github.com/AgentMaker/Paddle-MSSSIM

$cdPaddle-MSSSIM

$pythonsetup.pyinstallRequirementpaddlepaddle / paddlepaddle-gpu >= 2.0.0UsageBasic Usagefrompaddle_msssimimportssim,ms_ssim,SSIM,MS_SSIM# X: (N,3,H,W) a batch of non-negative RGB images (0~255)# Y: (N,3,H,W)# calculate ssim & ms-ssim for each imagessim_val=ssim(X,Y,data_range=255,size_average=False)# return (N,)ms_ssim_val=ms_ssim(X,Y,data_range=255,size_average=False)#(N,)# set 'size_average=True' to get a scalar value as loss. see tests/tests_loss.py for more detailsssim_loss=1-ssim(X,Y,data_range=255,size_average=True)# return a scalarms_ssim_loss=1-ms_ssim(X,Y,data_range=255,size_average=True)# reuse the gaussian kernel with SSIM & MS_SSIM.ssim_module=SSIM(data_range=255,size_average=True,channel=3)# channel=1 for grayscale imagesms_ssim_module=MS_SSIM(data_range=255,size_average=True,channel=3)ssim_loss=1-ssim_module(X,Y)ms_ssim_loss=1-ms_ssim_module(X,Y)Normalized input'''If you need to calculate MS-SSIM/SSIM on normalized imagesPlease denormalize them to the range of [0, 1] or [0, 255] first'''# X: (N,3,H,W) a batch of normalized images (-1 ~ 1)# Y: (N,3,H,W)X=(X+1)/2# [-1, 1] => [0, 1]Y=(Y+1)/2ms_ssim_val=ms_ssim(X,Y,data_range=1,size_average=False)#(N,)Enable nonnegative_ssimFor ssim, it is recommended to setnonnegative_ssim=Trueto avoid negative results. However, this option is set toFalseby default to keep it consistent with tensorflow and skimage.For ms-ssim, there is no nonnegative_ssim option and the ssim reponses is forced to be non-negative to avoid NaN results.TestSwitch to the tests dir$cd./testsBenchmark$pythoncomparisons_skimage_tf_torch.pyoutputs(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
  PassExampleImage comparisonSSIM = 1.0000SSIM = 0.4225SSIM = 0.1924As a loss functionswitch to the examples/as_loss dir$cd./examples/as_lossrun the example script as_loss.py$pythonas_loss.pyInitial 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: 0.999912resultInputOutputSeeexamples/as_loss/as_loss.pyfor more detailsAuto EncoderSeeexamples/auto_encoderfor more detailsReferencesSSIM ResearchMS-SSIM PaperMatlab CodePytorch CodeTensorFlow CodeThanksThis repo is based onPytorch MS-SSIMdeveloped by@VainF.Thanks to the above project and its developers.