3jpSSKJr SSKJrJr SSKrSSKJs Jr SSKJr "SS\R5r g)) annotations)OptionalSequenceN)nnc^\rSrSrSrSS U4SjjjrS S SjjrS S SjjrS SjrSr U=r $) MS_SSIMLossaCreates a criterion that computes MSSIM + L1 loss. According to [1], we compute the MS_SSIM + L1 loss as follows: .. math:: \text{loss}(x, y) = \alpha \cdot \mathcal{L_{MSSIM}}(x,y)+(1 - \alpha) \cdot G_\alpha \cdot \mathcal{L_1}(x,y) Where: - :math:`\alpha` is the weight parameter. - :math:`x` and :math:`y` are the reconstructed and true reference images. - :math:`\mathcal{L_{MSSIM}}` is the MS-SSIM loss. - :math:`G_\alpha` is the sigma values for computing multi-scale SSIM. - :math:`\mathcal{L_1}` is the L1 loss. Reference: [1]: https://research.nvidia.com/sites/default/files/pubs/2017-03_Loss-Functions-for/NN_ImgProc.pdf#page11 Args: sigmas: gaussian sigma values. data_range: the range of the images. K: k values. alpha : specifies the alpha value compensation: specifies the scaling coefficient. reduction : Specifies the reduction to apply to the output: ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied, ``'mean'``: the sum of the output will be divided by the number of elements in the output, ``'sum'``: the output will be summed. Returns: The computed loss. Shape: - Input1: :math:`(N, C, H, W)`. - Input2: :math:`(N, C, H, W)`. - Output: :math:`(N, H, W)` or scalar if reduction is set to ``'mean'`` or ``'sum'``. Examples: >>> input1 = torch.rand(1, 3, 5, 5) >>> input2 = torch.rand(1, 3, 5, 5) >>> criterion = kornia.losses.MS_SSIMLoss() >>> loss = criterion(input1, input2) cH>[T U]5 X lUSU-S-UlUSU-S-Ul[ SUS-5UlX@lXPlX`l [ SUS-S-5n[R"S[U5-SXw45n[U5HkupURXz5USU -S-SSS2SS24'URXz5USU -S-SSS2SS24'URXz5USU -S-SSS2SS24'Mm URSU5 g)Nr_g_masks)super__init__DRC1C2intpadalpha compensation reductiontorchzeroslen enumerate_fspecial_gauss_2dregister_buffer) selfsigmas data_rangeKrrr filter_sizeg_masksidxsigma __class__s O/home/wildlama/miniconda3/lib/python3.13/site-packages/kornia/losses/ms_ssim.pyrMS_SSIMLoss.__init__Ks0 #A$+1A$+1q6":~&! #/'!fRj.1,- ++q3v;;LM$F+JC,0,C,CK,WGAGaKAq( ),0,C,CK,WGAGaKAq( ),0,C,CK,WGAGaKAq( ), Z1c[R"XUS9nXQS--n[R"US-*SUS--- 5nXfR5-nUR S5$)zCreate 1-D gauss kernel. Args: size: the size of gauss kernel. sigma: sigma of normal distribution. device: device to store the result on. dtype: dtype of the result. Returns: 1D kernel (size). )devicedtyper r )rarangeexpsumreshape)r!sizer(r.r/coordsgs r*_fspecial_gauss_1dMS_SSIMLoss._fspecial_gauss_1dis\d?!) II la%(l3 4 UUW yy}r,cRURXX45n[R"XU5$)zCreate 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). )r7router)r!r4r(r.r/ gaussian_vecs r*rMS_SSIMLoss._fspecial_gauss_2d~s%..tFJ {{<66r,c[U[R5(d[S[ U535e[U[R5(d[S[ U535e[ UR 5[ UR 5:Xd$[S[ U5S[ U5S35e[RR[RUR5nUR Sn[R"XX@RS9n[R"X#X@RS9nXU-nXf-nXV-n [R"X-X4URS9U- n [R"X"-X4URS9U- n [R"X-X4URS9U - n SU -UR-Xx-UR-- n SU -UR-X-UR-- nU S S 2S S S 2S S 24U S S 2S S S 2S S 24-U S S 2SS S 2S S 24-nUR!S S 9nS UU-- n[R""XSS9n[R"UX4*S X@RS9R%S 5nUR&U-S UR&- U-UR(- -nUR*U-nUR,S:Xa[R$"U5nU$UR,S:Xa[R."U5nU$UR,S:XaU$[1SUR,35e)zCompute MS_SSIM loss. Args: img1: the predicted image with shape :math:`(B, C, H, W)`. img2: the target image with a shape of :math:`(B, C, H, W)`. Returns: Estimated MS-SSIM_L1 loss. z&Input type is not a torch.Tensor. Got z'Output type is not a torch.Tensor. Got z!Input shapes should be same. Got z and .)groupspaddingr Nr r )dimnone)rmeanr2zInvalid reduction mode: ) isinstancerTensor TypeErrortypershape ValueErrorjitannotaterFconv2drrrprodl1_lossrErrrrr2NotImplementedError)r!img1img2r&CHmuxmuymux2muy2muxysigmax2sigmay2sigmaxylccslMPIcs loss_ms_ssimloss_l1 gaussian_l1losss r*forwardMS_SSIMLoss.forwards$ --DT$ZLQR R$ --Ed4j\RS S4::#djj/1@d ERVW[R\Q]]^_` ` % 2 25<< O**R.hhtRBhhtRByyy((4;DHHMPTT((4;DHHMPTT((4;DHHMPTT$h T[477%: ;'kDGG#(9DGG(C D 2q! _r!RA+ .Ar1aK @ww1w~29} ))D&9hhw b((SXXYZ[ zzL(A Nk+IDGG+SS  4' >>V #::d#D ^^u $99T?D  ^^v %  &(@@P&QR Rr,)rrrrrrr))g??g@g@g @rh)g{Gz?gQ?g?gi@rE)r"zSequence[float]r#floatr$ztuple[float, float]rrirrirstrreturnNone)NN) r4rr(rir.zOptional[torch.device]r/zOptional[torch.dtype]rk torch.Tensor)rSrmrTrmrkrm) __name__ __module__ __qualname____firstlineno____doc__rr7rrf__static_attributes__ __classcell__)r)s@r*rrs*\#<!-#222  2  2  22 22>nr %/EUj ,nr77 %7/E7Uj7 7$<<r,r) __future__rtypingrrrtorch.nn.functionalr functionalrNModulerr,r*r{s+$#%  n"))nr,