3jSSKJr SSKrSSKJr SSKJr SS Sjjr"SS\R5rg) ) annotationsN)nn)metricsc[R"XX#XF5nSU- nUS:XaUR5nU$US:XaUR5nU$US:XaU$[ S5e)aCompute a loss based on the SSIM measurement. The loss, or the Structural dissimilarity (DSSIM) is described as: .. math:: \text{loss}(x, y) = \frac{1 - \text{SSIM}(x, y)}{2} See :meth:`~kornia.losses.ssim` for details about SSIM. Args: img1: the first input image with shape :math:`(B, C, D, H, W)`. img2: the second input image with shape :math:`(B, C, D, H, W)`. window_size: the size of the gaussian kernel to smooth the images. max_val: the dynamic range of the images. eps: Small value for numerically stability when dividing. reduction : Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, ``'mean'``: the sum of the output will be divided by the number of elements in the output, ``'sum'``: the output will be summed. padding: ``'same'`` | ``'valid'``. Whether to only use the "valid" convolution area to compute SSIM to match the MATLAB implementation of original SSIM paper. Returns: The loss based on the ssim index. Examples: >>> input1 = torch.rand(1, 4, 5, 5, 5) >>> input2 = torch.rand(1, 4, 5, 5, 5) >>> loss = ssim3d_loss(input1, input2, 5) ?meansumnonezInvalid reduction option.)rssim3drr NotImplementedError) img1img2 window_sizemax_valeps reductionpaddingssim_maplosss N/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/losses/ssim3d.py ssim3d_lossrszT%^^D c[H >DFyy{ K e xxz K f   K""=>>cV^\rSrSrSrSSU4SjjjrSSjrSrU=r$) SSIM3DLossUaiCreate a criterion that computes a loss based on the SSIM measurement. The loss, or the Structural dissimilarity (DSSIM) is described as: .. math:: \text{loss}(x, y) = \frac{1 - \text{SSIM}(x, y)}{2} See :meth:`~kornia.losses.ssim_loss` for details about SSIM. Args: window_size: the size of the gaussian kernel to smooth the images. max_val: the dynamic range of the images. eps: Small value for numerically stability when dividing. reduction : Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, ``'mean'``: the sum of the output will be divided by the number of elements in the output, ``'sum'``: the output will be summed. padding: ``'same'`` | ``'valid'``. Whether to only use the "valid" convolution area to compute SSIM to match the MATLAB implementation of original SSIM paper. Returns: The loss based on the ssim index. Examples: >>> input1 = torch.rand(1, 4, 5, 5, 5) >>> input2 = torch.rand(1, 4, 5, 5, 5) >>> criterion = SSIM3DLoss(5) >>> loss = criterion(input1, input2) c^>[TU]5 XlX lX0lX@lXPlgN)super__init__rrrrr)selfrrrrr __class__s rrSSIM3DLoss.__init__vs*  +% '# rc [XURURURURUR 5$r)rrrrrr)r r rs rforwardSSIM3DLoss.forwards34t'7'7txxQUQ_Q_aeamamnnr)rrrrrrg-q=rsame) rintrfloatrr)rstrrr*returnNone)r torch.Tensorrr-r+r-) __name__ __module__ __qualname____firstlineno____doc__rr$__static_attributes__ __classcell__)r!s@rrrUsVBsy$$).$;@$UX$lo$ $$oorrr&)r r-rr-rr(rr)rr)rr*rr*r+r-) __future__rtorchrkorniarrModulerrrr:s$# 8 8 88 8  8  888v,o,or