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        "author": "Gongfan Fang",
        "author_email": "fgfvain97@zju.edu.cn",
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            "Programming Language :: Python :: 3"
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        "description": "# Pytorch MS-SSIM\n\nFast and differentiable MS-SSIM and SSIM for pytorch 1.0+\n\nAll calculations will be on the same device as inputs.\n\n# update\n_2019.6.17_  \nNow it is faster than compare_ssim thanks to [One-sixth's contribution](https://github.com/VainF/pytorch-msssim/issues/3)\n\n# Install\n```bash\npython setup.py install\n```\nor\n```bash\npip install pytorch-msssim\n```\n\n# Example\n\n```python\nfrom pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM\n# X: (N,3,H,W) a batch of RGB images with values ranging from 0 to 255.\n# Y: (N,3,H,W)  \nssim_val = ssim( X, Y, data_range=255, size_average=False) # return (N,)\nms_ssim_val = ms_ssim( X, Y, data_range=255, size_average=False ) #(N,)\n\n# or set 'size_average=True' to get a scalar value as loss.\nssim_loss = ssim( X, Y, data_range=255, size_average=True) # return a scalar value\nms_ssim_loss = ms_ssim( X, Y, data_range=255, size_average=True )\n\n# or reuse windows with SSIM & MS_SSIM. \nssim_module = SSIM(win_size=11, win_sigma=1.5, data_range=255, size_average=True, channel=3)\nms_ssim_module = MS_SSIM(win_size=11, win_sigma=1.5, data_range=255, size_average=True, channel=3)\n\nssim_loss = ssim_module(X, Y)\nms_ssim_loss = ms_ssim_module(X, Y)\n```\n\n# Tests\n\nCompared with [skimage.measure.compare_ssim](https://scikit-image.org/docs/dev/api/skimage.measure.html#skimage.measure.compare_ssim) on CPU.\n\nThe outputs:\n```\nDownloading test image...\n====> Single Image\nsigma=0.000000 compare_ssim=1.000000 (417.248964 ms) ssim_torch=1.000000 (257.593870 ms)\nsigma=1.000000 compare_ssim=0.991320 (326.905012 ms) ssim_torch=0.991320 (135.488033 ms)\nsigma=2.000000 compare_ssim=0.966521 (485.862017 ms) ssim_torch=0.966520 (237.199068 ms)\nsigma=3.000000 compare_ssim=0.928799 (323.492050 ms) ssim_torch=0.928797 (148.905993 ms)\nsigma=4.000000 compare_ssim=0.882271 (290.801048 ms) ssim_torch=0.882267 (146.914005 ms)\nsigma=5.000000 compare_ssim=0.831310 (282.787085 ms) ssim_torch=0.831306 (148.653984 ms)\nsigma=6.000000 compare_ssim=0.778222 (308.619022 ms) ssim_torch=0.778217 (147.915840 ms)\nsigma=7.000000 compare_ssim=0.726444 (290.637970 ms) ssim_torch=0.726438 (133.754253 ms)\nsigma=8.000000 compare_ssim=0.676345 (294.582129 ms) ssim_torch=0.676339 (144.154072 ms)\nsigma=9.000000 compare_ssim=0.629922 (300.610065 ms) ssim_torch=0.629916 (141.150951 ms)\nPass\n====> Batch\nPass\n```\n\n# An autoencoder trained with MS_SSIM\n\n![results](https://github.com/VainF/Images/blob/master/pytorch_msssim/ae_ms_ssim.jpg)\n*left: original image, right: reconstructed image*\n\n# References\n\n[https://github.com/jorge-pessoa/pytorch-msssim](https://github.com/jorge-pessoa/pytorch-msssim)  \n[https://ece.uwaterloo.ca/~z70wang/research/ssim/](https://ece.uwaterloo.ca/~z70wang/research/ssim/)  \n[https://ece.uwaterloo.ca/~z70wang/publications/msssim.pdf](https://ece.uwaterloo.ca/~z70wang/publications/msssim.pdf)  \n[Matlab Code](https://ece.uwaterloo.ca/~z70wang/research/iwssim/)  \n\n\n",
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