o oif@sPddlmZddlmZddlmZmZ    ddddZGdddeZdS)) annotations)metrics)ModuleTensor?-q=meansameimg1rimg2 window_sizeintmax_valfloateps reductionstrpaddingreturnc CsZt||||||}d|}|dkr|}|S|dkr"|}|S|dkr) |Std)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) rrsumnonezInvalid reduction option.)rssim3drrNotImplementedError) r r r rrrrssim_maplossrH/home/ubuntu/.local/lib/python3.10/site-packages/kornia/losses/ssim3d.py ssim3d_losss*rcs0eZdZdZ ddfdd ZdddZZS) SSIM3DLossaCreate 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) rrrr r r rrrrrrrNonecs,t||_||_||_||_||_dSN)super__init__r rrrr)selfr rrrr __class__rrr"ts  zSSIM3DLoss.__init__r rr cCst|||j|j|j|j|jSr )rr rrrr)r#r r rrrforward~szSSIM3DLoss.forwardrrrr ) r r rrrrrrrrrr)r rr rrr)__name__ __module__ __qualname____doc__r"r& __classcell__rrr$rrSs ! rNr')r rr rr r rrrrrrrrrr) __future__rkorniar kornia.corerrrrrrrrs   ;