o oi@sXddlmZddlmZmZddlZddlmmZ ddlmZGdddej Z dS)) annotations)OptionalSequenceN)nncsVeZdZdZ      d'd(fdd Z d)d*dd Z d)d*d!d"Zd+d%d&ZZS), MS_SSIMLossaCreates a criterion that computes MSSIM + L1 loss. According to [1], we compute the MS_SSIM + L1 loss as follows: .. math:: \text{loss}(x, y) = \alpha \cdot \mathcal{L_{MSSIM}}(x,y)+(1 - \alpha) \cdot G_\alpha \cdot \mathcal{L_1}(x,y) Where: - :math:`\alpha` is the weight parameter. - :math:`x` and :math:`y` are the reconstructed and true reference images. - :math:`\mathcal{L_{MSSIM}}` is the MS-SSIM loss. - :math:`G_\alpha` is the sigma values for computing multi-scale SSIM. - :math:`\mathcal{L_1}` is the L1 loss. Reference: [1]: https://research.nvidia.com/sites/default/files/pubs/2017-03_Loss-Functions-for/NN_ImgProc.pdf#page11 Args: sigmas: gaussian sigma values. data_range: the range of the images. K: k values. alpha : specifies the alpha value compensation: specifies the scaling coefficient. 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. Returns: The computed loss. Shape: - Input1: :math:`(N, C, H, W)`. - Input2: :math:`(N, C, H, W)`. - Output: :math:`(N, H, W)` or scalar if reduction is set to ``'mean'`` or ``'sum'``. Examples: >>> input1 = torch.rand(1, 3, 5, 5) >>> input2 = torch.rand(1, 3, 5, 5) >>> criterion = kornia.losses.MS_SSIMLoss() >>> loss = criterion(input1, input2) g??g@g@g @rg{Gz?gQ?皙?i@meansigmasSequence[float] data_rangefloatKtuple[float, float]alpha compensation reductionstrreturnNonec s t||_|d|d|_|d|d|_td|d|_||_||_||_ td|dd}t dt |d||f}t |D]@\} } ||| |d| ddddddf<||| |d| ddddddf<||| |d| ddddddf<qG|d|dS)Nr_g_masks)super__init__DRC1C2intpadrrrtorchzeroslen enumerate_fspecial_gauss_2dregister_buffer) selfr rrrrr filter_sizeg_masksidxsigma __class__I/home/ubuntu/.local/lib/python3.10/site-packages/kornia/losses/ms_ssim.pyr Ks ((*zMS_SSIMLoss.__init__Nsizer$r0deviceOptional[torch.device]dtypeOptional[torch.dtype] torch.TensorcCsNtj|||d}||d8}t|d d|d}||}|dS)aCreate 1-D gauss kernel. Args: size: the size of gauss kernel. sigma: sigma of normal distribution. device: device to store the result on. dtype: dtype of the result. Returns: 1D kernel (size). )r6r8rr)r&arangeexpsumreshape)r,r5r0r6r8coordsgr3r3r4_fspecial_gauss_1dis    zMS_SSIMLoss._fspecial_gauss_1dcCs|||||}t||S)a Create 2-D gauss kernel. Args: size: the size of gauss kernel. sigma: sigma of normal distribution. device: device to store the result on. dtype: dtype of the result. Returns: 2D kernel (size x size). )rAr&outer)r,r5r0r6r8 gaussian_vecr3r3r4r*~s zMS_SSIMLoss._fspecial_gauss_2dimg1img2cCstt|tjstdt|t|tjstdt|t|jt|jks7tdt|dt|dtj tj|j }|jd}t j ||||j d}t j ||||j d}||}||}||} t j |||||j d|} t j |||||j d|} t j |||||j d| } d| |j|||j} d| |j| | |j}| d d d d d d d f| d d d d d d d f| d d dd d d d f}|jd d }d ||}t j||dd}t j ||| d ||j dd }|j|d |j||j}|j|}|jdkrt|}|S|jdkr)t|}|S|jdkr2 |Std|j)zCompute MS_SSIM loss. Args: img1: the predicted image with shape :math:`(B, C, H, W)`. img2: the target image with a shape of :math:`(B, C, H, W)`. Returns: Estimated MS-SSIM_L1 loss. z&Input type is not a torch.Tensor. Got z'Output type is not a torch.Tensor. Got z!Input shapes should be same. Got z and .)groupspaddingrNrr)dimnone)rr r=zInvalid reduction mode: ) isinstancer&Tensor TypeErrortyper(shape ValueErrorjitannotaterFconv2dr%r"r#prodl1_lossr rr!rrr=NotImplementedError)r,rDrEr.CHmuxmuymux2muy2muxysigmax2sigmay2sigmaxylccslMPIcs loss_ms_ssimloss_l1 gaussian_l1lossr3r3r4forwardsF   T  $     zMS_SSIMLoss.forward)rrr r r r )r rrrrrrrrrrrrr)NN) r5r$r0rr6r7r8r9rr:)rDr:rEr:rr:) __name__ __module__ __qualname____doc__r rAr*rk __classcell__r3r3r1r4rs.  r) __future__rtypingrrr&torch.nn.functionalr functionalrUModulerr3r3r3r4s