9i SSKJr SSKJr SSKrSSKJrJr SSK J r SSK J r J r JrJr "S S\R,R.5r"SS\R,R.5rS\R4S\R4S\R4S\R44SjrS\R4S\R4S\R44Sjrg!\as \R""\R$"\55r\ "S\R*"\SS5\R*"\SS 5\R*"\SS 5/S S 9r SSK J r J r JrJr GNf=f)N)Tuple) custom_bwd custom_fwd)load)ailut_cbackwardailut_cforward lut_cbackward lut_cforward cudaops_ailutAilutzcsrc/ailut_transform.cppzcsrc/ailut_transform_cpu.cppzcsrc/ailut_transform_cuda.cuT)namesourcesverbosec\rSrSr\\"\RS9S\RS\RS\R4Sj55r \\ S\RS\ \R4Sj55r S r g ) LUTTransformFunction cast_inputsimglutreturncUR5nUR5nUR5S:XdS5eUR5S;dS5eURURS5URS5URS5URS545n[ XU5 UR X5 U$) NConly support 2D image with batch and channel dimensions (4D tensor)=only support 3D lookup table with batch dimension (5D tensor)r) contiguous ndimension new_zerossizer save_for_backward)ctxrroutputs a/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/modelscope/ops/ailut/pyinterfaces.pyforwardLUTTransformFunction.forwardsnnnn~~1$ R Q R$~~3& L K L& XXa[#((1+sxx{CHHQK @BSv& c' grad_outputcUR5nURup#[R"U5n[R"U5n[ XX4U5 XE4$N)r! saved_tensorstorch zeros_liker )r&r,rrgrad_imggrad_luts r(backwardLUTTransformFunction.backward/sT",,. $$##C(##C(kx@!!r+N)__name__ __module__ __qualname____firstlineno__ staticmethodrr0float32Tensorr)rrr4__static_attributes__r6r+r(rrs}EMM*%,,U\\ell+$ "5<< "E%,,4G " "r+rc \rSrSr\\"\RS9S\RS\RS\RS\R4Sj55r \\ S\RS\ \R4S j55r S rg ) AiLUTTransformFunction>rrrverticesrcUR5nUR5nUR5nUR5S:XdS5eUR5S;dS5eUR5S:XdS5eURURS5URS5URS 5URS545n[ XX45 UR XU5 U$) Nrrrrr zKonly support 1D vertices list with batch and channel dimensions (3D tensor)rrr)r!r"r#r$rr%)r&rrrBr's r(r)AiLUTTransformFunction.forward@s nnnn&&(~~1$ R Q R$~~3& L K L&""$) Z Y Z) XXa[#((1+sxx{CHHQK @Bs2 c1 r+r,c UR5nURup#n[R"U5n[R"U5n[R"U5n[ XX4XVU5 XVU4$r.)r!r/r0r1r)r&r,rrrBr2r3grad_vers r(r4AiLUTTransformFunction.backwardXsp",,.  ..(##C(##C(##H- #  "8++r+r6N)r7r8r9r:r;rr0r<r=tensorr)rrr4r>r6r+r(r@r@>sEMM*%,,U\\,,+0<<+, ,5<< ,E%,,4G , ,r+r@rrrBrc.[RXU5$)aAdaptive Interval 3D Lookup Table Transform (AiLUT-Transform). Args: img (torch.Tensor): input image of shape (b, 3, h, w). lut (torch.Tensor): output values of the 3D LUT, shape (b, 3, d, d, d). vertices (torch.Tensor): sampling coordinates along each dimension of the 3D LUT, shape (b, 3, d). Returns: torch.Tensor: transformed image of shape (b, 3, h, w). )r@apply)rrrBs r(ailut_transformrKjs " ' '( ;;r+c,[RX5$)zStandard 3D Lookup Table Transform. Args: img (torch.Tensor): input image of shape (b, 3, h, w). lut (torch.Tensor): output values of the 3D LUT, shape (b, 3, d, d, d). Returns: torch.Tensor: transformed image of shape (b, 3, h, w). )rrJ)rrs r( lut_transformrMys % %c //r+)os.pathpathosptypingrr0torch.cuda.amprrtorch.utils.cpp_extensionrr rrr r ImportErrorabspathdirname__file__CUR_DIRjoinautogradFunctionrr@r=rKrMr6r+r(r\s' 1*--- ""5>>22""J),U^^44),X <