mirror of https://github.com/vladmandic/human
8034 lines
1.6 MiB
8034 lines
1.6 MiB
/*
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Human
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homepage: <https://github.com/vladmandic/human>
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author: <https://github.com/vladmandic>'
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*/
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Actual: ${r}.
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Actual: ${r}.
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with dtype ${a.dtype}. `)}),n.length===1)return Bn(n[0]);let s=n,r={axis:t};return U.runKernel(yi,s,r)}var St=G({concat_:lD});function uD(e){let n={x:F(e,"x","sigmoid","float32")};return U.runKernel(go,n)}var _n=G({sigmoid_:uD});function cD(e,t,n){let s=F(e,"x","slice","string_or_numeric");if(s.rank===0)throw new Error("Slicing scalar is not possible");let r={x:s},a={begin:t,size:n};return U.runKernel(Gi,r,a)}var Pe=G({slice_:cD});function dD(e){let n={x:F(e,"x","tanh","float32")};return U.runKernel(wo,n)}var Bu=G({tanh_:dD});function pD(e,t,n,s,r,a){let o=F(e,"forgetBias","basicLSTMCell"),i=F(t,"lstmKernel","basicLSTMCell"),l=F(n,"lstmBias","basicLSTMCell"),c=F(s,"data","basicLSTMCell"),u=F(r,"c","basicLSTMCell"),d=F(a,"h","basicLSTMCell"),p=St([c,d],1),h=je(p,i),f=ue(h,l),m=f.shape[0],g=f.shape[1]/4,y=[m,g],x=Pe(f,[0,0],y),A=Pe(f,[0,g],y),b=Pe(f,[0,g*2],y),w=Pe(f,[0,g*3],y),C=ue(L(_n(x),Bu(A)),L(u,_n(ue(o,b)))),S=L(Bu(C),_n(w));return[C,S]}var hD=G({basicLSTMCell_:pD});function 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${c.rank}.`),u!=null&&M(u.rank===2||u.rank===1,()=>`Error in batchNorm2D: offset must be rank 2 or rank 1 but got rank ${u.rank}.`),Wu(o,i,l,u,c,a)}var mv=G({batchNorm2d_:yD});function AD(e,t,n,s,r,a){let o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),c;r!=null&&(c=F(r,"scale","batchNorm"));let u;return s!=null&&(u=F(s,"offset","batchNorm")),M(o.rank===3,()=>`Error in batchNorm3D: x must be rank 3 but got rank ${o.rank}.`),M(i.rank===3||i.rank===1,()=>`Error in batchNorm3D: mean must be rank 3 or rank 1 but got rank ${i.rank}.`),M(l.rank===3||l.rank===1,()=>`Error in batchNorm3D: variance must be rank 3 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===3||c.rank===1,()=>`Error in batchNorm3D: scale must be rank 3 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===3||u.rank===1,()=>`Error in batchNorm3D: offset must be rank 3 or rank 1 but got rank ${u.rank}.`),Wu(o,i,l,u,c,a)}var gv=G({batchNorm3d_:AD});function xD(e,t,n,s,r,a){let o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),c;r!=null&&(c=F(r,"scale","batchNorm"));let u;return s!=null&&(u=F(s,"offset","batchNorm")),M(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),M(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),M(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),Wu(o,i,l,u,c,a)}var yv=G({batchNorm4d_:xD});function bD(e,t,n){let s=F(e,"x","bincount"),r=F(t,"weights","bincount");M(s.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${s.dtype}`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(r.size===s.size||r.size===0,()=>`Error in bincount: weights must have the same size as input or0-length, but got input shape: ${s.shape}, weights shape: ${r.shape}.`);let a={x:s,weights:r},o={size:n};return U.runKernel(Rh,a,o)}var sy=G({bincount_:bD});function vD(e,t){let n=F(e,"s0","broadcastArgs","int32"),s=F(t,"s1","broadcastArgs","int32");if(n.rank!==1)throw new Error(`broadcastArgs(): first input must be a vector (rank=1). Has rank ${n.rank}`);if(s.rank!==1)throw new Error(`broadcastArgs(): second input must be a vector (rank=1). 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Got strides ${n} and dilations '${a}'`);let p={x:c,filter:l},h={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},f=U.runKernel(za,p,h);return u?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Po=G({conv2d_:ED});function RD(e,t,n,s,r="NWC",a=1,o){let i=F(e,"x","conv1d"),l=F(t,"filter","conv1d"),c=i,u=!1;i.rank===2&&(u=!0,c=H(i,[1,i.shape[0],i.shape[1]])),M(c.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${c.rank}.`),M(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),Wn("conv1d",s,o),M(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${c.shape[2]}) must match input depth for filter ${l.shape[1]}.`),M(_r(n,a),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${n} and dilation '${a}'`),M(r==="NWC",()=>`Error in conv1d: got dataFormat of ${r} but only NWC is currently supported.`);let d=H(l,[1,l.shape[0],l.shape[1],l.shape[2]]),p=H(c,[c.shape[0],1,c.shape[1],c.shape[2]]),g=Po(p,d,[1,n],s,"NHWC",[1,a],o);return u?H(g,[g.shape[2],g.shape[3]]):H(g,[g.shape[0],g.shape[2],g.shape[3]])}var ry=G({conv1d_:RD});function _D(e,t,n,s,r,a="NHWC",o){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let i=e,l=t,c=!1;t.rank===3&&(c=!0,l=H(t,[1,t.shape[0],t.shape[1],t.shape[2]]),i=[1,e[0],e[1],e[2]]),M(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),M(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),M(n.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${n.rank}`);let u=a==="NHWC"?i[3]:i[1],d=a==="NHWC"?l.shape[3]:l.shape[1];M(u===n.shape[2],()=>`Error in conv2dDerInput: depth of input (${u}) must match input depth for filter ${n.shape[2]}.`),M(d===n.shape[3],()=>`Error in conv2dDerInput: depth of output (${d}) must match output depth for filter ${n.shape[3]}.`),Wn("conv2dDerInput",r,o);let p={dy:l,filter:n},h={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,inputShape:i},f=U.runKernel(La,p,h);return c?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var ay=G({conv2DBackpropInput_:_D});function DD(e,t,n,s,r,a){let o=F(e,"x","conv2dTranspose"),i=F(t,"filter","conv2dTranspose");return ay(n,o,i,s,r,"NHWC",a)}var oy=G({conv2dTranspose_:DD});function $D(e,t,n,s,r="NDHWC",a=[1,1,1]){let o=F(e,"x","conv3d"),i=F(t,"filter","conv3d"),l=o,c=!1;o.rank===4&&(c=!0,l=H(o,[1,o.shape[0],o.shape[1],o.shape[2],o.shape[3]])),M(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),M(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),M(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),M(_r(n,a),()=>`Error in conv3D: Either strides or dilations must be 1. Got strides ${n} and dilations '${a}'`),M(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let u={x:l,filter:i},d={strides:n,pad:s,dataFormat:r,dilations:a},p=U.runKernel(pd,u,d);return c?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var iy=G({conv3d_:$D});function FD(e,t,n,s,r){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=H(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);let l=a[4],c=o.shape[4];M(a.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`),M(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),M(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),M(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),M(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:o,filter:n},d={pad:r,strides:s,inputShape:a},p=U.runKernel(Fh,u,d);return i?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var kv=G({conv3DBackpropInput_:FD});function PD(e,t,n,s,r){let a=F(e,"x","conv3dTranspose"),o=F(t,"filter","conv3dTranspose");return kv(n,a,o,s,r)}var Sv=G({conv3dTranspose_:PD});function OD(e){let n={x:F(e,"x","cos","float32")};return U.runKernel(Ba,n)}var Af=G({cos_:OD});function MD(e){let n={x:F(e,"x","cosh","float32")};return U.runKernel(Wa,n)}var ly=G({cosh_:MD});function zD(e,t=0,n=!1,s=!1){let a={x:F(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:s};return U.runKernel(Ai,a,o)}var uy=G({cumsum_:zD});function LD(e,t,n,s=!1){let r=F(e,"x","denseBincount"),a=F(t,"weights","denseBincount");M(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),M(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(a.size===r.size||a.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);let o={x:r,weights:a},i={size:n,binaryOutput:s};return U.runKernel(Ph,o,i)}var Iv=G({denseBincount_:LD});function BD(e,t,n="NHWC"){let s=F(e,"x","depthToSpace","float32"),r=n==="NHWC"?s.shape[1]:s.shape[2],a=n==="NHWC"?s.shape[2]:s.shape[3],o=n==="NHWC"?s.shape[3]:s.shape[1];M(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),M(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${r} and ${t} for depthToSpace with input shape
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${s.shape}`),M(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${a} and ${t} for depthToSpace with input shape
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${s.shape}`),M(o%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${o} for depthToSpace with input shape ${s.shape}`);let i={x:s},l={blockSize:t,dataFormat:n};return U.runKernel(bi,i,l)}var Cv=G({depthToSpace_:BD});function WD(e,t,n,s,r="NHWC",a=[1,1],o){let i=F(e,"x","depthwiseConv2d","float32"),l=F(t,"filter","depthwiseConv2d","float32"),c=i,u=!1;i.rank===3&&(u=!0,c=H(i,[1,i.shape[0],i.shape[1],i.shape[2]])),M(c.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),M(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),M(c.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),Wn("depthwiseConv2d",s,o);let d={x:c,filter:l},p={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},h=U.runKernel(Va,d,p);return u?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Wd=G({depthwiseConv2d_:WD});function VD(e){let n={x:F(e,"x","diag")};return U.runKernel(zh,n)}var UD=G({diag_:VD});function GD(e,t,n,s,r=[1,1],a="NHWC"){let o=F(e,"x","dilation2d"),i=F(t,"filter","dilation2d");M(o.rank===3||o.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${o.rank}.`),M(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),M(a==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);let l=o,c=!1;o.rank===3&&(l=H(o,[1,o.shape[0],o.shape[1],o.shape[2]]),c=!0);let u={x:l,filter:i},d={strides:n,pad:s,dilations:r},p=U.runKernel(hd,u,d);return c?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Tv=G({dilation2d_:GD});function HD(e,t){let n=F(e,"a","equal","string_or_numeric"),s=F(t,"b","equal","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return U.runKernel(vi,r)}var Ps=G({equal_:HD});function jD(e,t,n){let s=F(t,"a","where"),r=F(n,"b","where"),a=F(e,"condition","where","bool"),o=At(At(a.shape,s.shape),r.shape),i=Bd(a,o),l=Bd(s,o),c=Bd(r,o),u={condition:i,t:l,e:c};return U.runKernel(Ui,u)}var Vn=G({where_:jD});function qD(e){let n={x:F(e,"x","zerosLike")};return U.runKernel(Qi,n)}var nt=G({zerosLike_:qD});function XD(e,t){let n=F(e,"a","div"),s=F(t,"b","div");[n,s]=Ot(n,s);let r=de(n,s),a=nt(r),o=Ps(s,a);return Vn(o,a,r)}var Nv=G({divNoNan_:XD});function KD(e,t){let n=F(e,"t1","dot"),s=F(t,"t2","dot");M((n.rank===1||n.rank===2)&&(s.rank===1||s.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${n.rank} and ${s.rank}.`);let r=n.rank===1?n.size:n.shape[1],a=s.rank===1?s.size:s.shape[0];if(M(r===a,()=>`Error in dot: inner dimensions of inputs must match, but got ${r} and ${a}.`),n.rank===1&&s.rank===1){let o=H(n,[1,-1]),i=H(s,[-1,1]),l=je(o,i);return H(l,[])}else if(n.rank===1&&s.rank===2){let o=H(n,[1,-1]),i=H(s,[s.shape[0],s.shape[1]]),l=je(o,i);return H(l,[l.size])}else if(n.rank===2&&s.rank===1){let o=H(s,[-1,1]),i=je(n,o);return H(i,[i.size])}else{let o=H(s,[s.shape[0],s.shape[1]]);return je(n,o)}}var ZD=G({dot_:KD});function YD(e,...t){let n=t.map((r,a)=>F(r,`tensors${a}`,"einsum")),s={equation:e};return U.runKernel(fd,n,s)}var Ev=G({einsum_:YD});function JD(e){let n={x:F(e,"x","elu","float32")};return U.runKernel(Ga,n)}var Vd=G({elu_:JD});function QD(e){let t=F(e,"x","erf");M(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=me(t,"float32"));let n={x:t};return U.runKernel(yu,n)}var Rv=G({erf_:QD});function e$(e){let n={x:F(e,"x","exp")};return U.runKernel(Ha,n)}var Os=G({exp_:e$});function t$(e,t=0){let n=F(e,"x","expandDims","string_or_numeric");M(t<=n.rank,()=>"Axis must be <= rank of the tensor");let s={input:n},r={dim:t};return U.runKernel(wi,s,r)}var Zt=G({expandDims_:t$});function n$(e){let n={x:F(e,"x","expm1")};return U.runKernel(ki,n)}var _v=G({expm1_:n$});function s$(e,t){let n=F(e,"x","tile","string_or_numeric");M(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let s={x:n},r={reps:t};return U.runKernel(Yr,s,r)}var Js=G({tile_:s$});function r$(e,t,n,s="float32"){t==null&&(t=e);let r=ze([e,t],s),a=e<=t?e:t;for(let i=0;i<a;++i)r.set(1,i,i);let o=H(r.toTensor(),[e,t]);if(n==null)return o;if(n.length===1)return Js(Zt(o,0),[n[0],1,1]);if(n.length===2)return Js(Zt(Zt(o,0),0),[n[0],n[1],1,1]);if(n.length===3)return Js(Zt(Zt(Zt(o,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}var cy=G({eye_:r$});function Uu(e,t,n){let s={shape:e,value:t,dtype:n};return U.runKernel(Au,{},s)}function a$(e){let n={x:F(e,"x","floor","float32")};return U.runKernel(ja,n)}var Ud=G({floor_:a$});function o$(e,t,n=0,s=0){let r=F(e,"x","gather"),a=F(t,"indices","gather","int32"),o={x:r,indices:a},i={axis:n,batchDims:s};return U.runKernel(Ii,o,i)}var Gu=G({gather_:o$});function i$(e,t){let n=F(e,"a","greater","string_or_numeric"),s=F(t,"b","greater","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return U.runKernel(Ti,r)}var xs=G({greater_:i$});function l$(e,t){let n=F(e,"a","greaterEqual","string_or_numeric"),s=F(t,"b","greaterEqual","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return U.runKernel(Ka,r)}var dl=G({greaterEqual_:l$});function u$(e){let n={input:F(e,"input","imag")};return U.runKernel(md,n)}var xf=G({imag_:u$});function c$(e){let n={x:F(e,"x","isFinite")};return U.runKernel(xu,n)}var d$=G({isFinite_:c$});function p$(e){let n={x:F(e,"x","isInf")};return U.runKernel(bu,n)}var h$=G({isInf_:p$});function f$(e){let n={x:F(e,"x","isNaN")};return U.runKernel(vu,n)}var Dv=G({isNaN_:f$});function m$(e,t=.2){let s={x:F(e,"x","leakyRelu")},r={alpha:t};return U.runKernel(Ya,s,r)}var bf=G({leakyRelu_:m$});function g$(e,t){let n=F(e,"a","less","string_or_numeric"),s=F(t,"b","less","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return U.runKernel(Ni,r)}var dy=G({less_:g$});function y$(e,t){let n=F(e,"a","lessEqual","string_or_numeric"),s=F(t,"b","lessEqual","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return U.runKernel(Ei,r)}var pl=G({lessEqual_:y$});function $v(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let s={start:e,stop:t,num:n};return U.runKernel(Gh,{},s)}function A$(e,t=5,n=1,s=1,r=.5){let a=F(e,"x","localResponseNormalization");M(a.rank===4||a.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
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rank ${a.rank}.`),M(ru(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let o=a,i=!1;a.rank===3&&(i=!0,o=H(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let l={x:o},c={depthRadius:t,bias:n,alpha:s,beta:r},u=U.runKernel(yd,l,c);return i?H(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Fv=G({localResponseNormalization_:A$});function x$(e){let n={x:F(e,"x","log","float32")};return U.runKernel(Ja,n)}var Ms=G({log_:x$});function b$(e){let n={x:F(e,"x","log1p")};return U.runKernel(wu,n)}var vf=G({log1p_:b$});function v$(e){return M(_a(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let s=F(t,"x","tf.grad","string_or_numeric"),r=n!=null?F(n,"dy","tf.grad"):null;return U.tidy(()=>{let{value:a,grads:o}=U.gradients(()=>e(s),[s],r);return r!=null&&Mn(a.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),wf(o),o[0]})}}function w$(e){return M(_a(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{M(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let s=Od(t,"args","tf.grads","string_or_numeric"),r=n!=null?F(n,"dy","tf.grads"):null;return U.tidy(()=>{let{value:a,grads:o}=U.gradients(()=>e(...s),s,r);return r!=null&&Mn(a.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),wf(o),o})}}function k$(e){return M(_a(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{M(t instanceof et,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),M(n==null||n instanceof et,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:s,value:r}=U.gradients(()=>e(t),[t],n);return wf(s),{grad:s[0],value:r}}}function S$(e){return M(_a(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{M(Array.isArray(t)&&t.every(r=>r instanceof et),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),M(n==null||n instanceof et,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let s=U.gradients(()=>e(...t),t,n);return n!=null&&Mn(s.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),wf(s.grads),s}}function Pv(e,t){M(_a(e),()=>"The f passed in variableGrads(f) must be a function"),M(t==null||Array.isArray(t)&&t.every(c=>c instanceof Fd),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let c in U.registeredVariables)t.push(U.registeredVariables[c])}let s=n?t.filter(c=>!c.trainable):null,r=t.length;t=t.filter(c=>c.trainable),M(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let a=!0,{value:o,grads:i}=U.gradients(e,t,null,a);M(i.some(c=>c!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),M(o.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${o.rank} tensor`);let l={};return t.forEach((c,u)=>{i[u]!=null&&(l[c.name]=i[u])}),s!=null&&s.forEach(c=>l[c.name]=null),{value:o,grads:l}}function Dr(e){return U.customGrad(e)}function wf(e){if(e.filter(n=>n==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
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the f you passed encloses all operations that lead from x to y.`)}function I$(e){let n={x:F(e,"x","neg")};return U.runKernel(_i,n)}var zt=G({neg_:I$});function C$(e){let n={x:F(e,"x","softplus")};return U.runKernel(_u,n)}var Hu=G({softplus_:C$});function T$(e){let t=F(e,"x","logSigmoid");return Dr(s=>({value:zt(Hu(zt(s))),gradFunc:o=>L(o,_n(zt(s)))}))(t)}var N$=G({logSigmoid_:T$});function E$(e,t=null,n=!1){let r={x:F(e,"x","max")},a={reductionIndices:t,keepDims:n};return U.runKernel(Qa,r,a)}var An=G({max_:E$});function R$(e,t){let n=F(e,"a","sub"),s=F(t,"b","sub");[n,s]=Ot(n,s);let r={a:n,b:s};return U.runKernel(vo,r)}var he=G({sub_:R$});function _$(e,t=null,n=!1){let s=F(e,"x","sum");s.dtype==="bool"&&(s=me(s,"int32"));let r={x:s},a={axis:t,keepDims:n};return U.runKernel(Ao,r,a)}var we=G({sum_:_$});function D$(e,t=-1){let n=F(e,"logits","logSoftmax");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. 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DO=G({transform_:_O});function $O(e,t,n){M(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),M(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let s=F(e,"a","bandPart");M(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);let r=s.shape,[a,o]=s.shape.slice(-2);if(!(t<=a))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);if(!(n<=o))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${o}).`);t<0&&(t=a),n<0&&(n=o);let i=H(Xu(0,a,1,"int32"),[-1,1]),l=Xu(0,o,1,"int32"),c=he(i,l),u=hr(pl(c,Ce(+t,"int32")),dl(c,Ce(-n,"int32"))),d=Gt([a,o],s.dtype);return H(an(os(H(s,[-1,a,o])).map(p=>Vn(u,p,d))),r)}var FO=G({bandPart_:$O});function PO(e){let t;if(Array.isArray(e)){t=!1,M(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let a=1;a<e.length;++a)M(e[a].shape[0]===r,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`)}else t=!0,e=Yt(e,e.shape[0],0).map(r=>rt(r,[0]));M(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],s=e;for(let r=0;r<e.length;++r)n.push(U.tidy(()=>{let a=s[r];if(r>0)for(let o=0;o<r;++o){let i=L(we(L(n[o],a)),n[o]);a=he(a,i)}return de(a,_y(a,"euclidean"))}));return t?an(n,0):n}var OO=G({gramSchmidt_:PO});function MO(e,t=!1){if(M(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return hw(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,c)=>l*c),s=os(H(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],a=[];s.forEach(l=>{let[c,u]=hw(l,t);r.push(c),a.push(u)});let o=H(an(r,0),e.shape),i=H(an(a,0),e.shape);return[o,i]}}function hw(e,t=!1){return U.tidy(()=>{M(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],s=e.shape[1],r=cy(n),a=Bn(e),o=fr([[1]],[1,1]),i=Bn(o),l=n>=s?s:n;for(let c=0;c<l;++c){let u=a,d=i,p=r;[i,a,r]=U.tidy(()=>{let h=Pe(a,[c,c],[n-c,1]),f=_y(h),m=Pe(a,[c,c],[1,1]),g=Vn(xs(m,0),fr([[-1]]),fr([[1]])),y=he(m,L(g,f)),x=de(h,y);x.shape[0]===1?i=Bn(o):i=St([o,Pe(x,[1,0],[x.shape[0]-1,x.shape[1]])],0);let A=zt(de(je(g,y),f)),b=Pe(a,[c,0],[n-c,s]),w=L(A,i),C=tt(i);if(c===0)a=he(b,je(w,je(C,b)));else{let R=he(b,je(w,je(C,b)));a=St([Pe(a,[0,0],[c,s]),R],0)}let S=tt(w),E=Pe(r,[0,c],[n,r.shape[1]-c]);if(c===0)r=he(E,je(je(E,i),S));else{let R=he(E,je(je(E,i),S));r=St([Pe(r,[0,0],[n,c]),R],1)}return[i,a,r]}),ne([u,d,p])}return!t&&n>s&&(r=Pe(r,[0,0],[n,s]),a=Pe(a,[0,0],[s,s])),[r,a]})}var zO=G({qr_:MO}),is=(e=>(e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS",e))(is||{});function LO(e,t,n=is.SUM_BY_NONZERO_WEIGHTS){let s=F(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=F(t,"weights","computeWeightedLoss"));let a=r==null?s:L(s,r);if(n===is.NONE)return a;if(n===is.SUM)return we(a);if(n===is.MEAN){if(r==null)return Ut(a);{let o=s.size/r.size,i=de(we(a),we(r));return o>1?de(i,Ce(o)):i}}if(n===is.SUM_BY_NONZERO_WEIGHTS){if(r==null)return de(we(a),Ce(s.size));{let o=L(r,bs(s.shape)),i=me(we(ju(o,Ce(0))),"float32");return de(we(a),i)}}throw Error(`Unknown reduction: ${n}`)}var ta=G({computeWeightedLoss_:LO});function BO(e,t,n,s=is.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","absoluteDifference"),a=F(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=F(n,"weights","absoluteDifference")),Mn(r.shape,a.shape,"Error in absoluteDifference: ");let i=rn(he(r,a));return ta(i,o,s)}var WO=G({absoluteDifference_:BO});function VO(e,t,n,s,r=is.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","cosineDistance"),o=F(t,"predictions","cosineDistance"),i=null;s!=null&&(i=F(s,"weights","cosineDistance")),Mn(a.shape,o.shape,"Error in cosineDistance: ");let l=Ce(1),c=he(l,we(L(a,o),n,!0));return ta(c,i,r)}var UO=G({cosineDistance_:VO});function GO(e,t,n,s=is.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","hingeLoss"),a=F(t,"predictions","hingeLoss"),o=null;n!=null&&(o=F(n,"weights","hingeLoss")),Mn(r.shape,a.shape,"Error in hingeLoss: ");let i=Ce(1);r=he(L(Ce(2),r),i);let l=$r(he(i,L(r,a)));return ta(l,o,s)}var HO=G({hingeLoss_:GO});function jO(e,t,n,s=1,r=is.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","huberLoss"),o=F(t,"predictions","huberLoss"),i=null;n!=null&&(i=F(n,"weights","huberLoss")),Mn(a.shape,o.shape,"Error in huberLoss: ");let l=Ce(s),c=rn(he(o,a)),u=Gd(c,l),d=he(c,u),p=ue(L(Ce(.5),xt(u)),L(l,d));return ta(p,i,r)}var qO=G({huberLoss_:jO});function XO(e,t,n,s=1e-7,r=is.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","logLoss"),o=F(t,"predictions","logLoss"),i=null;n!=null&&(i=F(n,"weights","logLoss")),Mn(a.shape,o.shape,"Error in logLoss: ");let l=Ce(1),c=Ce(s),u=zt(L(a,Ms(ue(o,c)))),d=L(he(l,a),Ms(ue(he(l,o),c))),p=he(u,d);return ta(p,i,r)}var KO=G({logLoss_:XO});function ZO(e,t,n,s=is.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","meanSquaredError"),a=F(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=F(n,"weights","meanSquaredError")),Mn(r.shape,a.shape,"Error in meanSquaredError: ");let i=Ny(r,a);return ta(i,o,s)}var YO=G({meanSquaredError_:ZO});function JO(e,t){let n=F(e,"labels","sigmoidCrossEntropyWithLogits"),s=F(t,"logits","sigmoidCrossEntropyWithLogits");Mn(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=$r(s),a=L(s,n),o=vf(Os(zt(rn(s))));return ue(he(r,a),o)}function QO(e,t,n,s=0,r=is.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"multiClassLabels","sigmoidCrossEntropy"),o=F(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=F(n,"weights","sigmoidCrossEntropy")),Mn(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let c=Ce(s),u=Ce(1),d=Ce(.5);a=ue(L(a,he(u,c)),L(d,c))}let l=JO(a,o);return ta(l,i,r)}var eM=G({sigmoidCrossEntropy_:QO});function tM(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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${r.shape}`);if(a.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);if(o.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let l={indices:r,values:a,denseShape:o,defaultValue:i},c=U.runKernel(bd,l);return{outputIndices:c[0],outputValues:c[1],emptyRowIndicator:c[2],reverseIndexMap:c[3]}}var aM=G({sparseFillEmptyRows_:rM});function oM(e,t,n){let s=F(e,"inputIndices","sparseReshape","int32"),r=F(t,"inputShape","sparseReshape","int32"),a=F(n,"newShape","sparseReshape","int32");if(s.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
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${s.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(a.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);let o={inputIndices:s,inputShape:r,newShape:a},i=U.runKernel(Du,o);return{outputIndices:i[0],outputShape:i[1]}}var iM=G({sparseReshape_:oM});function lM(e,t,n){let s=F(e,"data","sparseSegmentMean"),r=F(t,"indices","sparseSegmentMean","int32"),a=F(n,"segmentIds","sparseSegmentMean","int32");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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|
${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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|
${a.shape}`);let o={data:s,indices:r,segmentIds:a};return U.runKernel(vd,o)}var uM=G({sparseSegmentMean_:lM});function cM(e,t,n){let s=F(e,"data","sparseSegmentSum"),r=F(t,"indices","sparseSegmentSum","int32"),a=F(n,"segmentIds","sparseSegmentSum","int32");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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${a.shape}`);let o={data:s,indices:r,segmentIds:a};return U.runKernel(wd,o)}var dM=G({sparseSegmentSum_:cM});function pM(e,t,n,s,r,a,o,i){let l=F(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let c=F(t,"dataSplits","stringNGrams");if(c.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let u={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:o,preserveShortSequences:i},d={data:l,dataSplits:c},p=U.runKernel(Sd,d,u);return{nGrams:p[0],nGramsSplits:p[1]}}var hM=G({stringNGrams_:pM});function fM(e,t,n=!0){let s=F(e,"input","stringSplit","string"),r=F(t,"delimiter","stringSplit","string");if(s.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let a={skipEmpty:n},o={input:s,delimiter:r},i=U.runKernel(Jh,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var mM=G({stringSplit_:fM});function gM(e,t){let n=F(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return U.runKernel(Qh,r,s)}var yM=G({stringToHashBucketFast_:gM}),AM={fft:Ef,ifft:qd,rfft:Rf,irfft:Ty},xM={hammingWindow:qP,hannWindow:iw,frame:lw,stft:YP},Se={flipLeftRight:tO,grayscaleToRGB:sO,resizeNearestNeighbor:TO,resizeBilinear:IO,rotateWithOffset:aO,cropAndResize:QP,nonMaxSuppression:iO,nonMaxSuppressionAsync:mO,nonMaxSuppressionWithScore:yO,nonMaxSuppressionWithScoreAsync:xO,nonMaxSuppressionPadded:vO,nonMaxSuppressionPaddedAsync:kO,threshold:RO,transform:DO},fw={bandPart:FO,gramSchmidt:OO,qr:zO},bM={absoluteDifference:WO,computeWeightedLoss:ta,cosineDistance:UO,hingeLoss:HO,huberLoss:qO,logLoss:KO,meanSquaredError:YO,sigmoidCrossEntropy:eM,softmaxCrossEntropy:sM},Kd={sparseFillEmptyRows:aM,sparseReshape:iM,sparseSegmentMean:uM,sparseSegmentSum:dM},Of={stringNGrams:hM,stringSplit:mM,stringToHashBucketFast:yM},na=class extends Y3{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:r[o.name]}));this.applyGradients(a)}else 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indices.shape[0] = ${e}`}function ez(e,t){return`indices(${e}, 0) is invalid: ${t} < 0`}function tz(e,t,n){return`indices(${e}, 0) is invalid: ${t} >= ${n}`}function nz(e,t){return`only one output dimension may be -1, not both ${e} and ${t}`}function sz(e,t){return`size ${e} must be non-negative, not ${t}`}function rz(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function az(e,t){let n=It(e),s=It(t);return`Input to reshape is a SparseTensor with ${n}
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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},s=0;for(let a of this.layers)for(let o of a.weights){if(n[o.originalName]!=null)throw new q(`Duplicate weight name: ${o.originalName}`);n[o.originalName]=o,s++}let r=[];for(let a in e){let o=a;if(n[a]==null){let i=a.split("/");o=i.slice(0,-2).concat([i[i.length-1]]).join("/")}if(n[o]!=null)r.push([n[o],e[a]]);else if(t)throw new q(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new q(`${a.length} of ${s} weights are not set: ${a}`)}o1(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${m1}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=f1(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return X(()=>{e=Tt(e);let n=new kl;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return ap(this.outputs,n,t)})}computeMask(e,t){return X(()=>{e=Tt(e);let n;return t==null?n=yl(null,e.length):n=Tt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=nm(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],c=i.name+"_0_0";n[c]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Uf);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],x=`${m.name}_${g}_${y}`,A=n[x];u.push(A)}let d=c.computeOutputShape(ls(u)),p=nm(d),h=c.inboundNodes.indexOf(l);for(let f=0;f<p.length;f++){let m=`${c.name}_${h}_${f}`;n[m]=p[f]}}}let r=[],a=[];for(let o=0;o<this.outputLayers.length;o++){let i=this.outputLayers[o],l=this.outputLayersNodeIndices[o],c=this.outputLayersTensorIndices[o],u=`${i.name}_${l}_${c}`;a.push(u)}for(let o=0;o<a.length;o++){let i=a[o];Fr(i in n),r.push(n[i])}return ls(r)}runInternalGraph(e,t){t==null&&(t=yl(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],c=e[i],u=t[i];n[l.id]=[c,u]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Uf);for(let i of s){let l=this.nodesByDepth[i];for(let c of l){let u=c.outboundLayer,d=c.inputTensors,p=c.outputTensors,h=new Array;for(let f of d)f.id in n&&h.push(n[f.id]);if(h.length===d.length){let f={},m,g,y,x;if(c.callArgs!=null&&(f=c.callArgs),h.length===1){let[A,b]=h[0];f.mask==null&&(f.mask=b),y=Tt(u.call(A,f)),x=Tt(u.computeMask(A,b)),m=[A],g=[b]}else m=h.map(A=>A[0]),g=h.map(A=>A[1]),f.mask==null&&(f.mask=g),y=Tt(u.call(m,f)),x=Tt(u.computeMask(m,g));if(u.activityRegularizer)throw new Le("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let A=0;A<p.length;++A){let b=p[A],w=y[A],C=x[A];n[b.id]=[w,C]}}}}let r=[],a=[],o=[];for(let i of this.outputs){Fr(i.id in n,`Could not compute output ${i.name} : ${i.id}`);let[l,c]=n[i.id];o.push(l.shape),r.push(l),a.push(c)}return[r,a,o]}buildNodeConversionMap(e){let t={},n;for(let s of this.layers){n=s instanceof Mr?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Mr.nodeKey(s,r);this.containerNodes.has(a)&&(t[a]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new q(`Was asked to retrieve layer at index ${t}, but model only has ${this.layers.length} layer(s).`);return this.layers[t]}else if(e==null)throw new q("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new q(`No such layer: ${e}`)}calculateLosses(){return X(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Mr.nodeKey(t,n);this.containerNodes.has(s)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let a of this.layers){let o=a.getClassName(),i=a.getConfig(),l=[];for(let u=0;u<a.inboundNodes.length;u++){let d=a.inboundNodes[u],p=Mr.nodeKey(a,u),h={};if(this.containerNodes.has(p)){if(d.callArgs)try{JSON.stringify(d.callArgs),h=d.callArgs}catch(f){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${d.callArgs}. 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t={};return t.className="linear",t.config={},k1(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},k1(t)}else return e instanceof cs?e:k1(e)}function S1(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var Lk=class extends ce.Serializable{},ip=class extends Lk{constructor(e){super();S1(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return X(()=>{let t=Gt([1]);return this.hasL1&&(t=ue(t,we(L(this.l1,rn(e))))),this.hasL2&&(t=ue(t,we(L(this.l2,tp(e))))),H(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};ip.className="L1L2";ce.registerClass(ip);function dV(e){return S1(e),new ip({l1:e!=null?e.l1:null,l2:0})}function pV(e){return S1(e),new ip({l2:e!=null?e.l2:null,l1:0})}var Bk={l1l2:"L1L2"};function bt(e){return Ly(e)}function 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e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};E1.className="ThresholdedReLU";ce.registerClass(E1);var R1=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new w1().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Ve(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};R1.className="Softmax";ce.registerClass(R1);function nc(e,t,n){if(typeof e=="number")return yl(e,t);if(e.length!==t)throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let s=0;s<t;++s){let r=e[s];if(!DB(r))throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function vr(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function zr(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Vo([n-t,0]);else if(s==="same")e=e*t;else throw new q(`Unsupport padding mode: ${s}.`);return e}function _1(e,t){return X(()=>(Ht(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function Vk(e,t){return X(()=>(Ht(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function hV(e,t,n,s=1,r="valid",a,o=1){return X(()=>{if(a==null&&(a=mr()),Ht(a),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=tt(e,[0,2,1])),r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=ry(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=Ar(i,n)),i})}function Uk(e,t,n,s=[1,1],r="valid",a,o,i=null){return X(()=>{if(a==null&&(a=mr()),Ht(a),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=_1(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=zo.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=tt(l,[0,3,1,2])),l})}function fV(e,t,n,s=[1,1,1],r="valid",a,o){return X(()=>{if(a==null&&(a=mr()),Ht(a),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=Vk(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=iy(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Ar(i,n)),a==="channelsFirst"&&(i=tt(i,[0,4,1,2,3])),i})}var D1=class extends st{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",D1.verifyArgs(t),this.rank=e,xn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Le(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=nc(t.kernelSize,e,"kernelSize"),this.strides=nc(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Bs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ht(this.dataFormat),this.activation=Ho(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=$t(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=un(t.biasConstraint),this.biasRegularizer=Ft(t.biasRegularizer),this.activityRegularizer=Ft(t.activityRegularizer),this.dilationRate=nc(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new q(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Fr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Wy(e.kernelSize,"number",1,3))throw new q(`BaseConv expects config.kernelSize to be number or number[] with length 1, 2, or 3, but received ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Go(this.activation),useBias:this.useBias,biasInitializer:Lt(this.biasInitializer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),biasConstraint:ln(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},lp=class extends D1{constructor(e,t){super(e,t);this.kernel=null,lp.verifyArgs(t),this.filters=t.filters,xn(this.filters,"filters"),this.kernelInitializer=$t(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=un(t.kernelConstraint),this.kernelRegularizer=Ft(t.kernelRegularizer)}build(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],s=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return X(()=>{e=Ve(e);let n,s=this.bias==null?null:this.bias.read(),r=$w(this.activation.getClassName());if(r!=null&&this.rank===2)n=Uk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=hV(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Uk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=fV(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Le("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ft(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=vr(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:Lt(this.kernelInitializer),kernelRegularizer:bt(this.kernelRegularizer),kernelConstraint:ln(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Gk=class extends lp{constructor(e){super(2,e);Gk.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Wy(e.kernelSize,"number",1,2))throw new q(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},fm=Gk;fm.className="Conv2D";ce.registerClass(fm);var Hk=class extends lp{constructor(e){super(3,e);Hk.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!(Array.isArray(e.kernelSize)&&(e.kernelSize.length===1||e.kernelSize.length===3)))throw new q(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},mm=Hk;mm.className="Conv3D";ce.registerClass(mm);var $1=class extends fm{constructor(e){super(e);if(this.inputSpec=[new Jt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Jt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return X(()=>{let n=Ve(e);if(n.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o;this.dataFormat==="channelsFirst"?(a=2,o=3):(a=1,o=2);let i=s[a],l=s[o],c=this.kernelSize[0],u=this.kernelSize[1],d=this.strides[0],p=this.strides[1],h=zr(i,d,c,this.padding),f=zr(l,p,u,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=tt(n,[0,2,3,1]));let g=oy(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=tt(g,[0,3,1,2])),this.bias!=null&&(g=Ar(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ft(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=zr(t[s],i,a,this.padding),t[r]=zr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};$1.className="Conv2DTranspose";ce.registerClass($1);var F1=class extends mm{constructor(e){super(e);if(this.inputSpec=[new Jt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new q(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=ft(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Jt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return X(()=>{let n=Ve(e);if(n.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let s=n.shape,r=s[0],a,o,i;this.dataFormat==="channelsFirst"?(i=2,a=3,o=4):(i=1,a=2,o=3);let l=s[i],c=s[a],u=s[o],d=this.kernelSize[0],p=this.kernelSize[1],h=this.kernelSize[2],f=this.strides[0],m=this.strides[1],g=this.strides[2],y=zr(l,f,d,this.padding),x=zr(c,m,p,this.padding),A=zr(u,g,h,this.padding),b=[r,y,x,A,this.filters];this.dataFormat!=="channelsLast"&&(n=tt(n,[0,2,3,4,1]));let w=Sv(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=tt(w,[0,4,1,2,3])),this.bias!==null&&(w=Ar(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=ft(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],u=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=zr(t[s],c,o,this.padding),t[r]=zr(t[r],u,i,this.padding),t[a]=zr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};F1.className="Conv3DTranspose";ce.registerClass(F1);var jk=class extends lp{constructor(e,t){super(e,t);if(this.DEFAULT_DEPTHWISE_INITIALIZER="glorotUniform",this.DEFAULT_POINTWISE_INITIALIZER="glorotUniform",this.depthwiseKernel=null,this.pointwiseKernel=null,t.filters==null)throw new q("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new q("Fields kernelInitializer, kernelRegularizer and kernelConstraint are invalid for SeparableConv2D. Use depthwiseInitializer, depthwiseRegularizer, depthwiseConstraint, pointwiseInitializer, pointwiseRegularizer and pointwiseConstraint instead.");if(t.padding!=null&&t.padding!=="same"&&t.padding!=="valid")throw new q(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=$t(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ft(t.depthwiseRegularizer),this.depthwiseConstraint=un(t.depthwiseConstraint),this.pointwiseInitializer=$t(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ft(t.pointwiseRegularizer),this.pointwiseConstraint=un(t.pointwiseConstraint)}build(e){if(e=ft(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${this.rank}D should have rank ${this.rank+2}, but received input shape: ${JSON.stringify(e)}`);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],s=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let o=0;o<this.rank;++o)r.push(1);r.push(n*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",s,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,a,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,a,this.biasConstraint):this.bias=null,this.inputSpec=[new Jt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return X(()=>{e=Ve(e);let n;if(this.rank===1)throw new Le("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=tt(e,[0,2,3,1])),n=Hv(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ar(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=tt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Lt(this.depthwiseInitializer),e.pointwiseInitializer=Lt(this.pointwiseInitializer),e.depthwiseRegularizer=bt(this.depthwiseRegularizer),e.pointwiseRegularizer=bt(this.pointwiseRegularizer),e.depthwiseConstraint=ln(this.depthwiseConstraint),e.pointwiseConstraint=ln(this.pointwiseConstraint),e}};jk.className="SeparableConv";var P1=class extends jk{constructor(e){super(2,e)}};P1.className="SeparableConv2D";ce.registerClass(P1);var qk=class extends lp{constructor(e){super(1,e);qk.verifyArgs(e),this.inputSpec=[{ndim:3}]}getConfig(){let e=super.getConfig();return delete e.rank,delete e.dataFormat,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Wy(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},O1=qk;O1.className="Conv1D";ce.registerClass(O1);var M1=class extends st{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return X(()=>{if(e=Ve(e),this.dataFormat==="channelsLast"){let n=Hf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Hf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Hf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Hf(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};M1.className="Cropping2D";ce.registerClass(M1);var z1=class extends st{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,EB(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return X(()=>{let n=Ve(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=tt(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?Se.resizeNearestNeighbor(n,[r,a]):Se.resizeBilinear(n,[r,a]);return tt(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?Se.resizeNearestNeighbor(n,[r,a]):Se.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};z1.className="UpSampling2D";ce.registerClass(z1);function mV(e,t,n=[1,1],s="valid",r,a){return X(()=>{r==null&&(r=mr()),Ht(r);let o=_1(e,r);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Wd(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=tt(o,[0,3,1,2])),o})}var L1=class extends D1{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=$t(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=un(e.depthwiseConstraint),this.depthwiseRegularizer=Ft(e.depthwiseRegularizer)}build(e){if(e=ft(e),e.length<4)throw new q(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new q(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],s=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",s,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return X(()=>{e=Ve(e);let n=mV(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ar(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=vr(t,this.kernelSize[0],this.padding,this.strides[0]),a=vr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Lt(this.depthwiseInitializer),e.depthwiseRegularizer=bt(this.depthwiseRegularizer),e.depthwiseConstraint=ln(this.depthwiseRegularizer),e}};L1.className="DepthwiseConv2D";ce.registerClass(L1);function Xk(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new q("When inputs is an array, neither initialState or constants should be provided");s!=null&&(n=e.slice(e.length-s,e.length),e=e.slice(0,e.length-s)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(a){return a==null||Array.isArray(a)?a:[a]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function Kk(e,t,n,s=!1,r,a,o=!1,i=!1){return X(()=>{let l=t.shape.length;if(l<3)throw new q(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(yr(2,l));if(t=tt(t,c),a!=null)throw new Le("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=me(me(r,"bool"),"float32"),r.rank===l-1&&(r=Zt(r,-1)),r=tt(r,c)),s&&(t=Ls(t,0),r!=null&&(r=Ls(r,0)));let u=[],d,p=n,h=t.shape[0],f=os(t),m;r!=null&&(m=os(r));for(let y=0;y<h;++y){let x=f[y],A=X(()=>e(x,p));if(r==null)d=A[0],p=A[1];else{let b=X(()=>{let w=m[y],C=he(zs(w),w),S=ue(L(A[0],w),L(p[0],C)),E=p.map((R,P)=>ue(L(A[1][P],w),L(R,C)));return{output:S,newStates:E}});d=b.output,p=b.newStates}i&&u.push(d)}let g;return i&&(g=an(u,1)),[d,g,p]})}var Zk=class extends st{constructor(e){super(e);let t;if(e.cell==null)throw new q("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Am({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Jt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return yr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){r1(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],s;if(this.returnSequences?s=[e[0],e[1],n]:s=[e[0],n],this.returnState){let r=[];for(let a of t)r.push([e[0],a]);return[s].concat(r)}else return s}computeMask(e,t){return X(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let s=this.states.map(r=>null);return[n].concat(s)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Le("Constants support is not implemented in RNN yet.");r1(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Jt({shape:[n,null,...s]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new Le("Constants support is not implemented in RNN yet.");this.cell.build(r);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!v.arraysEqual(this.stateSpec.map(o=>o.shape[o.shape.length-1]),a))throw new q(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=a.map(o=>new Jt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){X(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Gt([n,s])):this.states_=[Gt([n,this.cell.stateSize])];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Gt([n,s])):this.states_[0]=Gt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):ne(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!v.arraysEqual(r.shape,o))throw new q(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${r.shape}`);this.states_[s]=r}}this.states_=this.states_.map(s=>yn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=Xk(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Jt({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof xr){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return X(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Ve(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==a)throw new q(`RNN Layer has ${a} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=Kk((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],u=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?u:c;return this.returnState?[p].concat(d):p})}getInitialState(e){return X(()=>{let t=Gt(e.shape);return t=we(t,[1,2]),t=ep(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Ky(t,[1,n]):t):this.cell.stateSize>1?[Ky(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===Zk.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=br(s,n);return new e(Object.assign(t,{cell:r}))}},oa=Zk;oa.className="RNN";ce.registerClass(oa);var up=class extends st{},gm=class extends up{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,xn(this.units,"units"),this.activation=Ho(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=un(e.kernelConstraint),this.recurrentConstraint=un(e.recurrentConstraint),this.biasConstraint=un(e.biasConstraint),this.dropout=Ju([1,Vo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ju([1,Vo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e),this.kernel=this.addWeight("kernel",[e[e.length-1],this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return X(()=>{if(e=e,e.length!==2)throw new q(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let s=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=jo({ones:()=>zs(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=jo({ones:()=>zs(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=Pr(L(e,a),this.kernel.read()):r=Pr(e,this.kernel.read()),this.bias!=null&&(r=Ar(r,this.bias.read())),o!=null&&(n=L(n,o));let i=ue(r,Pr(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Go(this.activation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),recurrentInitializer:Lt(this.recurrentInitializer),biasInitializer:Lt(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};gm.className="SimpleRNNCell";ce.registerClass(gm);var B1=class extends oa{constructor(e){e.cell=new gm(e);super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};B1.className="SimpleRNN";ce.registerClass(B1);var ym=class extends up{constructor(e){super(e);if(this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.resetAfter)throw new q("GRUCell does not support reset_after parameter set to true.");this.units=e.units,xn(this.units,"units"),this.activation=Ho(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ho(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=un(e.kernelConstraint),this.recurrentConstraint=un(e.recurrentConstraint),this.biasConstraint=un(e.biasConstraint),this.dropout=Ju([1,Vo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ju([1,Vo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=ft(e);let t=e[e.length-1];this.kernel=this.addWeight("kernel",[t,this.units*3],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*3],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias?this.bias=this.addWeight("bias",[this.units*3],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return X(()=>{if(e=e,e.length!==2)throw new q(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,s=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=jo({ones:()=>zs(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=jo({ones:()=>zs(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let c=Pr(e,this.kernel.read());this.useBias&&(c=Ar(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,a[0]));let u=this.recurrentKernel.read(),[d,p]=Yt(u,[2*this.units,this.units],u.rank-1),h=Pr(s,d),[f,m,g]=Yt(c,3,c.rank-1),[y,x]=Yt(h,2,h.rank-1);o=this.recurrentActivation.apply(ue(f,y)),i=this.recurrentActivation.apply(ue(m,x));let A=Pr(L(i,s),p);l=this.activation.apply(ue(g,A));let b=ue(L(o,s),L(ue(1,zt(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Go(this.activation),recurrentActivation:Go(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),recurrentInitializer:Lt(this.recurrentInitializer),biasInitializer:Lt(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};ym.className="GRUCell";ce.registerClass(ym);var W1=class extends oa{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new ym(e);super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};W1.className="GRU";ce.registerClass(W1);var cp=class extends up{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_RECURRENT_ACTIVATION="hardSigmoid",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,xn(this.units,"units"),this.activation=Ho(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ho(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=$t(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=un(e.kernelConstraint),this.recurrentConstraint=un(e.recurrentConstraint),this.biasConstraint=un(e.biasConstraint),this.dropout=Ju([1,Vo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ju([1,Vo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ft(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends nr{apply(o,i){let l=r.apply([a]),c=new qf().apply([a]),u=r.apply([a*2]);return Vw(Vw(l,c),u)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return X(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new q(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let s=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=jo({ones:()=>zs(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=jo({ones:()=>zs(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,c,u;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let d=Pr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,o[0])),d=ue(d,Pr(s,this.recurrentKernel.read())),this.useBias&&(d=Ar(d,this.bias.read()));let[p,h,f,m]=Yt(d,4,d.rank-1);i=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(h),c=ue(L(l,r),L(i,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let g=L(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Go(this.activation),recurrentActivation:Go(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),recurrentInitializer:Lt(this.recurrentInitializer),biasInitializer:Lt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:bt(this.kernelRegularizer),recurrentRegularizer:bt(this.recurrentRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),recurrentConstraint:ln(this.recurrentConstraint),biasConstraint:ln(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};cp.className="LSTMCell";ce.registerClass(cp);var V1=class extends oa{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new cp(e);super(e)}call(e,t){return X(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};V1.className="LSTM";ce.registerClass(V1);var Am=class extends up{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return X(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){r1(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{bl(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(br(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return a1(e)}setWeights(e){let t=[];for(let n of this.cells){let s=n.weights.length,r=e.splice(s);for(let a=0;a<n.weights.length;++a)t.push([n.weights[a],r[a]])}o1(t)}};Am.className="StackedRNNCells";ce.registerClass(Am);function jo(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):Gw(t(),n),i=()=>np(o,t,s);return!r||r<=1?yn(i().clone()):Array(r).fill(void 0).map(i).map(c=>yn(c.clone()))}var Yk=class extends oa{constructor(e){if(e.unroll)throw new Le("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Le("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Jt({ndim:5})]}call(e,t){return X(()=>{if(this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new q("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return X(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=Gt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){X(()=>{if(!this.stateful)throw new sa("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)];if(n[0]==null)throw new q("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Gt(r)):this.states_=[Gt(r)];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Gt(r)):this.states_[0]=Gt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):ne(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!v.arraysEqual(i.shape,l))throw new q(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>yn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],c=e[i?4:3],u=vr(l,s[0],r,a[0],o[0]),d=vr(c,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,u,d]:[u,d,n]]}};Yk.className="ConvRNN2D";var xm=class extends cp{constructor(e){let{filters:t,kernelSize:n,strides:s,padding:r,dataFormat:a,dilationRate:o}=e;super({...e,units:t});this.filters=t,xn(this.filters,"filters"),this.kernelSize=nc(n,2,"kernelSize"),this.kernelSize.forEach(i=>xn(i,"kernelSize")),this.strides=nc(s||1,2,"strides"),this.strides.forEach(i=>xn(i,"strides")),this.padding=r||"valid",Bs(this.padding),this.dataFormat=a||"channelsLast",Ht(this.dataFormat),this.dilationRate=nc(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>xn(i,"dilationRate"))}build(e){var t;e=ft(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;i=new(t=class extends nr{apply(u,d){let p=l.apply([c]),h=bs([c]),f=l.apply([c*2]);return Xy([p,h,f])}},t.className="CustomInit",t)}else i=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,i,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return X(()=>{if(e.length!==3)throw new q(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=jo({ones:()=>zs(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(W,Q,te)=>!Q||!Q[te]?W:L(Q[te],W),c=l(s,i,0),u=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=jo({ones:()=>zs(r),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),y=l(r,h,3),x=3,[A,b,w,C]=Yt(this.kernel.read(),o,x),[S,E,R,P]=this.useBias?Yt(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,A,S,this.padding),u=this.inputConv(u,b,E,this.padding),d=this.inputConv(d,w,R,this.padding),p=this.inputConv(p,C,P,this.padding);let[_,$,T,O]=Yt(this.recurrentKernel.read(),o,x);f=this.recurrentConv(f,_),m=this.recurrentConv(m,$),g=this.recurrentConv(g,T),y=this.recurrentConv(y,O);let V=this.recurrentActivation.apply(ue(c,f)),K=this.recurrentActivation.apply(ue(u,m)),z=ue(L(K,a),L(V,this.activation.apply(ue(d,g)))),j=L(this.recurrentActivation.apply(ue(p,y)),this.activation.apply(z));return[j,j,z]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=Po(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ar(r,n,this.dataFormat):r}recurrentConv(e,t){return Po(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};xm.className="ConvLSTM2DCell";ce.registerClass(xm);var U1=class extends Yk{constructor(e){let t=new xm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};U1.className="ConvLSTM2D";ce.registerClass(U1);var bm=class extends st{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let s=0;s<this.noiseShape.length;++s)n.push(this.noiseShape[s]==null?t[s]:this.noiseShape[s]);return n}call(e,t){return X(()=>{this.invokeCallHook(e,t);let n=Ve(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return np(()=>Gw(n,this.rate,r,this.seed),()=>n,s)}return e})}getConfig(){let e={rate:this.rate,noiseShape:this.noiseShape,seed:this.seed},t=super.getConfig();return Object.assign(e,t),e}dispose(){return super.dispose()}};bm.className="Dropout";ce.registerClass(bm);var G1=class extends bm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};G1.className="SpatialDropout1D";ce.registerClass(G1);var H1=class extends st{constructor(e){super(e);if(this.activation=null,this.useBias=!0,this.kernel=null,this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",e.batchInputShape==null&&e.inputShape==null&&e.inputDim!=null){let t=null;e.batchSize!=null&&(t=e.batchSize),this.batchInputShape=[t,e.inputDim]}this.units=e.units,xn(this.units,"units"),this.activation=Ho(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=$t(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=$t(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=un(e.kernelConstraint),this.biasConstraint=un(e.biasConstraint),this.kernelRegularizer=Ft(e.kernelRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.activityRegularizer=Ft(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=ft(e);let t=e[e.length-1];this.kernel==null&&(this.kernel=this.addWeight("kernel",[t,this.units],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.units],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint))),this.inputSpec=[{minNDim:2,axes:{[-1]:t}}],this.built=!0}computeOutputShape(e){e=ft(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return X(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=$w(this.activation.getClassName()),r;return s!=null?r=Pr(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Pr(n,this.kernel.read()),this.bias!=null&&(r=Ar(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Go(this.activation),useBias:this.useBias,kernelInitializer:Lt(this.kernelInitializer),biasInitializer:Lt(this.biasInitializer),kernelRegularizer:bt(this.kernelRegularizer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),kernelConstraint:ln(this.kernelConstraint),biasConstraint:ln(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};H1.className="Dense";ce.registerClass(H1);var j1=class extends st{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=ft(e);for(let t of e.slice(1))if(t==null)throw new q(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). 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X(()=>(e=Ve(e),$B(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};X1.className="RepeatVector";ce.registerClass(X1);var K1=class extends st{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",s=t.slice(),r=1,a=null;for(let i=0;i<s.length;++i){let l=s[i];if(this.isUnknown(l))if(a===null)a=i;else throw new q("Can only specifiy one unknown dimension.");else r*=l}let o=Wo(e);if(a!==null){if(r===0||o%r!=0)throw new q(n);s[a]=o/r}else if(o!==r)throw new q(n);return s}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return X(()=>{this.invokeCallHook(e,t);let 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i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=we(L(e,t),a[0]):i=we(L(tt(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,c=a[1]===t.shape.length-1;i=je(e,t,l,c)}if(o>0){let l;s>r?l=s+r-3:l=s-1;let c=[];for(let u=l;u<l+o;++u)c.push(u);i=rt(i,c)}return i.shape.length===1&&(i=Zt(i,1)),i})}var aA=class extends Il{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Le("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new q(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new q(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} 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st{constructor(e){super(e);this.supportsMasking=!0,this.stddev=e.stddev}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={stddev:this.stddev};return Object.assign(t,e),t}call(e,t){return X(()=>{this.invokeCallHook(e,t);let n=Ve(e);return np(()=>ue(jf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};oA.className="GaussianNoise";ce.registerClass(oA);var iA=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return X(()=>{this.invokeCallHook(e,t);let n=Ve(e);return this.rate>0&&this.rate<1?np(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return L(n,jf(n.shape,1,r))},()=>n,t.training||!1):n})}};iA.className="GaussianDropout";ce.registerClass(iA);var lA=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return 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e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Lt(this.betaInitializer),gammaInitializer:Lt(this.gammaInitializer),movingMeanInitializer:Lt(this.movingMeanInitializer),movingVarianceInitializer:Lt(this.movingVarianceInitializer),betaRegularizer:bt(this.betaRegularizer),gammaRegularizer:bt(this.gammaRegularizer),betaConstraint:ln(this.betaConstraint),gammaConstraint:ln(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};uA.className="BatchNormalization";ce.registerClass(uA);var cA=class extends st{constructor(e){e==null&&(e={});super(e);if(this.axis=e.axis==null?-1:e.axis,typeof this.axis=="number"){if(!Number.isInteger(this.axis))throw new Error(`Expected axis to be an integer, but received ${this.axis}`)}else if(Array.isArray(this.axis)){for(let t of this.axis)if(!Number.isInteger(t))throw new Error(`Expected axis to be an array of integers, but received ${JSON.stringify(this.axis)}`)}else throw new Error(`Expected axis to be an integer or an array of integers, but received ${JSON.stringify(this.axis)}`);this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=$t(e.betaInitializer||"zeros"),this.gammaInitializer=$t(e.gammaInitializer||"ones"),this.betaRegularizer=Ft(e.betaRegularizer),this.gammaRegularizer=Ft(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=ft(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==Bo(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),s=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,s):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,s):this.beta=null,this.built=!0}call(e,t){let n=Ve(e),s=n.shape,r=s.length;return X(()=>{let a=!0,{mean:o,variance:i}=If(n,this.axis,a),l=yl(1,r);for(let f of this.axis)l[f]=s[f];let c=f=>f!=null&&f.shape.length!==r?H(f,l):f,u=c(this.gamma.read()),d=c(this.beta.read()),p=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(p.push(s[f]),h.push(1)):(p.push(1),h.push(s[f]));return o=Js(o,p),i=Js(i,p),u=Js(u,h),d=Js(d,h),pp(n,o,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Lt(this.betaInitializer),gammaInitializer:Lt(this.gammaInitializer),betaRegularizer:bt(this.betaRegularizer),gammaRegularizer:bt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};cA.className="LayerNormalization";ce.registerClass(cA);function bV(e,t,n){return X(()=>{if(e.rank!==4)throw new q(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new q("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=mr()),n!=="channelsLast"&&n!=="channelsFirst")throw new q(`Unknown data format: ${n}. 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length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Jt({ndim:4})]}computeOutputShape(e){e=ft(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return X(()=>bV(Ve(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};dA.className="ZeroPadding2D";ce.registerClass(dA);function vm(e,t,n,s,r,a){return X(()=>{Ht(r),Mw(a),Bs(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=mr()),a==null&&(a="max"),e=_1(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Sf(e,t,n,i):o=gf(e,t,n,i),r==="channelsFirst"&&(o=tt(o,[0,3,1,2])),o})}function Jk(e,t,n,s,r,a){return X(()=>{Ht(r),Mw(a),Bs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=mr()),a==null&&(a="max"),e=Vk(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=gy(e,t,n,i):o=ny(e,t,n,i),r==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var Qk=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(xn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);xn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Bs(this.padding),this.inputSpec=[new Jt({ndim:3})]}computeOutputShape(e){e=ft(e);let t=vr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return X(()=>{this.invokeCallHook(e,t),e=ep(Ve(e),2);let n=this.poolingFunction(Ve(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return rt(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},pA=class extends Qk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),Bs(s),vm(e,t,n,s,r,"max")}};pA.className="MaxPooling1D";ce.registerClass(pA);var hA=class extends Qk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),Bs(s),vm(e,t,n,s,r,"avg")}};hA.className="AveragePooling1D";ce.registerClass(hA);var eS=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];xn(this.poolSize,"poolSize"),xn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),Bs(this.padding),this.inputSpec=[new Jt({ndim:4})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=vr(t,this.poolSize[0],this.padding,this.strides[0]),n=vr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return X(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ve(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},fA=class extends eS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),Bs(s),vm(e,t,n,s,r,"max")}};fA.className="MaxPooling2D";ce.registerClass(fA);var mA=class extends eS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),Bs(s),vm(e,t,n,s,r,"avg")}};mA.className="AveragePooling2D";ce.registerClass(mA);var tS=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];xn(this.poolSize,"poolSize"),xn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),Bs(this.padding),this.inputSpec=[new Jt({ndim:5})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=vr(t,this.poolSize[0],this.padding,this.strides[0]),n=vr(n,this.poolSize[1],this.padding,this.strides[1]),s=vr(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return X(()=>(this.invokeCallHook(e,t),this.poolingFunction(Ve(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},gA=class extends tS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),Bs(s),Jk(e,t,n,s,r,"max")}};gA.className="MaxPooling3D";ce.registerClass(gA);var yA=class extends tS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),Bs(s),Jk(e,t,n,s,r,"avg")}};yA.className="AveragePooling3D";ce.registerClass(yA);var nS=class extends st{constructor(e){super(e);this.inputSpec=[new Jt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Le}},AA=class extends nS{constructor(e){super(e||{})}call(e,t){return X(()=>{let n=Ve(e);return Ut(n,1)})}};AA.className="GlobalAveragePooling1D";ce.registerClass(AA);var xA=class extends nS{constructor(e){super(e||{})}call(e,t){return X(()=>{let n=Ve(e);return An(n,1)})}};xA.className="GlobalMaxPooling1D";ce.registerClass(xA);var sS=class extends st{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),this.inputSpec=[new Jt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Le}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},bA=class extends sS{call(e,t){return X(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?Ut(n,[1,2]):Ut(n,[2,3])})}};bA.className="GlobalAveragePooling2D";ce.registerClass(bA);var vA=class extends sS{call(e,t){return X(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?An(n,[1,2]):An(n,[2,3])})}};vA.className="GlobalMaxPooling2D";ce.registerClass(vA);var rS=class extends st{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=br(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},wA=class extends rS{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=ft(e),e.length<3)throw new q(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=ft(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),s=e[1];return[n[0],s].concat(n.slice(1))}call(e,t){return X(()=>(e=Ve(e),Kk((a,o)=>[Ve(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};wA.className="TimeDistributed";ce.registerClass(wA);function vV(e){xl(NB,"BidirectionalMergeMode",e)}var wV="concat",kA=class extends rS{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=br(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=br(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?wV:e.mergeMode,vV(this.mergeMode),e.weights)throw new Le("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,s,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,s=[n]):this.mergeMode==null?s=[n,n.slice()]:s=[n],this.returnState?this.mergeMode==null?s.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):ls(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=Xk(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(u=>new Jt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),o.push(...c)}if(s!=null)throw new Le("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof xr;for(let l of a)if(l instanceof xr!==i)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return X(()=>{let n=t.initialState,s,r;if(n==null)s=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let i=n.slice(0,n.length/2),l=n.slice(n.length/2);s=this.forwardLayer.call(e,Object.assign(t,{initialState:i})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:l}))}let a;this.returnState&&(Array.isArray(s)&&(a=s.slice(1).concat(r.slice(1))),s=s[0],r=r[0]),this.returnSequences&&(r=Ls(r,1));let o;return this.mergeMode==="concat"?o=Xy([s,r]):this.mergeMode==="sum"?o=ue(s,r):this.mergeMode==="ave"?o=L(.5,ue(s,r)):this.mergeMode==="mul"?o=L(s,r):this.mergeMode==null&&(o=[s,r]),this.returnState?this.mergeMode==null?o.concat(a):[o].concat(a):o})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){bl(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),bl(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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TypeError(`Node type ${e.op} is not implemented`)}};function sr(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){v.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let s=0;s<e.length;s++){let r=e[s],a=t[s];v.assert(r<0||a<0||r===a,()=>n+` Shapes ${e} and ${t} must match`)}}}function BS(e){return!(typeof e=="number"||e.some(t=>t<0))}function hp(e,t,n){let s=LA(e,n),r=!BS(s);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${s}`);if(r&&t.forEach(a=>{s=LA(a.shape,s)}),!BS(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function LA(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let s=0;s<e.length;++s){let r=e[s],a=t[s];if(r>=0&&a>=0&&r!==a)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[s]=r>=0?r:a}return n}var 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e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
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because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),sr(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,yn(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,s)=>this.write(n,t[s]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let s=0;s<this.size();s++)e.push(s)}if(e.length===0)return pt([],[0].concat(this.elementShape));let n=this.readMany(e);return sr(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),an(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return pt([],[0].concat(this.elementShape));let t=[];for(let s=0;s<this.size();s++)t.push(s);let n=this.readMany(t);return sr(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),St(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,os(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,s=e.map(i=>(n+=i,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
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tensor.shape[0], but sum of lengths is
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${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,a=[];X(()=>{t=H(t,[1,n,r]);for(let i=0;i<e.length;++i){let l=i===0?0:s[i-1],c=[0,l,0],u=[1,e[i],r];a[i]=H(Pe(t,c,u),this.elementShape)}return a});let o=[];for(let i=0;i<e.length;i++)o[i]=i;this.writeMany(o,a)}},fp=class{constructor(e,t,n,s=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);sr(t,r.shape,"TensorList shape mismatch: "),yn(r)}),this.idTensor=Ce(0),this.maxNumElements=s,yn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new fp([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);sr(e,this.elementShape,"TensorList shape mismatch: ");let s=hp(this.elementShape,this.tensors,e);return X(()=>{let r=this.tensors.map(a=>H(a,s));return an(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=hp(this.elementShape,this.tensors,e),s=this.tensors.pop();return sr(s.shape,e,"TensorList shape mismatch: "),H(s,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(sr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");yn(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);sr(this.tensors[e].shape,t,"TensorList shape mismatch: ");let s=hp(this.elementShape,this.tensors,t);return H(this.tensors[e],s)}setItem(e,t){if(t.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t.dtype}, but list elements ${this.elementDtype}`);if(e<0||this.maxNumElements!==-1&&e>=this.maxNumElements)throw new Error(`Trying to set element ${e} in a list with max ${this.maxNumElements} elements.`);sr(this.elementShape,t.shape,"TensorList shape mismatch: "),yn(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);sr(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let s=hp(this.elementShape,this.tensors,n);return e.length===0?pt([],[0].concat(s)):X(()=>{let r=e.map(a=>H(this.tensors[a],s));return an(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);sr(this.elementShape,t,"TensorList shape mismatch: ");let n=hp(this.elementShape,this.tensors,t);return this.size()===0?pt([],[0].concat(n)):X(()=>{let s=this.tensors.map(r=>H(r,n));return St(s,0)})}};function TG(e,t,n){let s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);sr(r,t,"TensorList shape mismatch: ");let a=os(e);return new fp(a,t,s)}function NG(e,t,n){return new fp([],e,t,n)}function EG(e,t,n,s){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(s!=null&&s!==-1&&r>=s)throw new Error(`Max index must be < array size (${r} vs. ${s})`);let a=new fp([],n,e.dtype,s),o=os(e,0);return t.forEach((i,l)=>{a.setItem(i,o[l])}),a}function RG(e,t,n){let s=0,r=t.map(u=>(s+=u,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
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tensor.shape[0], but sum of lengths is
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${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),o=LA(a,n),i=s===0?0:e.size/s,l=X(()=>{let u=[];e=H(e,[1,s,i]);for(let d=0;d<t.length;++d){let p=d===0?0:r[d-1],h=[0,p,0],f=[1,t[d],i];u[d]=H(Pe(e,h,f),o)}return e.dispose(),u}),c=new fp([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var _G=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let s=k("thenBranch",e,t,n),r=k("elseBranch",e,t,n),a=k("cond",e,t,n),o=k("args",e,t,n);return(await a.data())[0]?n.functionMap[s].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(o,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let s=k("body",e,t,n),r=k("cond",e,t,n),a=k("args",e,t,n),o=await n.functionMap[r].executeFunctionAsync(a,n.tensorArrayMap,n.tensorListMap),i=a.map(u=>u.id),l=await o[0].data();o.forEach(u=>{!u.kept&&i.indexOf(u.id)===-1&&u.dispose()});let c=a;for(;l[0];){let u=c;c=await 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s=k("elementShape",e,t,n),r=k("elementDType",e,t,n),a;e.op==="TensorListReserve"?a="numElements":a="maxNumElements";let o=k(a,e,t,n),i=NG(s,r,o);return n.addTensorList(i),[i.idTensor]}case"TensorListGather":{let s=k("tensorListId",e,t,n),r=k("indices",e,t,n),a=k("elementShape",e,t,n),o=k("elementDType",e,t,n);return[n.getTensorList(s.id).gather(r,o,a)]}case"TensorListStack":{let s=k("tensorListId",e,t,n),r=k("elementShape",e,t,n),a=k("elementDType",e,t,n),o=k("numElements",e,t,n);return[n.getTensorList(s.id).stack(r,a,o)]}case"TensorListFromTensor":{let s=k("tensor",e,t,n),r=k("elementShape",e,t,n),a=k("elementDType",e,t,n),o=TG(s,r,a);return n.addTensorList(o),[o.idTensor]}case"TensorListConcat":{let s=k("tensorListId",e,t,n),r=n.getTensorList(s.id),a=k("dtype",e,t,n),o=k("elementShape",e,t,n);return[r.concat(a,o)]}case"TensorListPushBack":{let s=k("tensorListId",e,t,n),r=k("tensor",e,t,n),a=n.getTensorList(s.id);return a.pushBack(r),[a.idTensor]}case"TensorListPopBack":{let 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implemented`)}},qG=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=Of.stringNGrams(k("data",e,t,n),k("dataSplits",e,t,n),k("separator",e,t,n),k("nGramWidths",e,t,n),k("leftPad",e,t,n),k("rightPad",e,t,n),k("padWidth",e,t,n),k("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=Of.stringSplit(k("input",e,t,n),k("delimiter",e,t,n),k("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[Of.stringToHashBucketFast(k("input",e,t,n),k("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},XG=(e,t,n)=>{switch(e.op){case"Cast":return[me(k("x",e,t,n),k("dtype",e,t,n))];case"ExpandDims":{let s=k("axis",e,t,n);return[Zt(k("x",e,t,n),s)]}case"Squeeze":{let s=k("axis",e,t,n);return[rt(k("x",e,t,n),s)]}case"Reshape":return[H(k("x",e,t,n),k("shape",e,t,n))];case"MirrorPad":return[Wv(k("x",e,t,n),k("padding",e,t,n),k("mode",e,t,n))];case"PadV2":case"Pad":return[Qs(k("x",e,t,n),k("padding",e,t,n),k("constantValue",e,t,n))];case"SpaceToBatchND":{let s=k("blockShape",e,t,n),r=k("paddings",e,t,n);return[Cf(k("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=k("blockShape",e,t,n),r=k("crops",e,t,n);return[yf(k("x",e,t,n),s,r)]}case"DepthToSpace":{let s=k("blockSize",e,t,n),r=k("dataFormat",e,t,n).toUpperCase();return[Cv(k("x",e,t,n),s,r)]}case"BroadcastTo":return[Bd(k("x",e,t,n),k("shape",e,t,n))];case"BroadcastArgs":return[Av(k("s0",e,t,n),k("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function VS(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return X(()=>SG(a,o,i));case"basic_math":return X(()=>IG(a,o,i));case"control":return _G(a,o,i);case"convolution":return X(()=>DG(a,o,i));case"creation":return X(()=>$G(a,o,i));case"dynamic":return FG(a,o,i);case"evaluation":return X(()=>PG(a,o,i));case"image":return X(()=>LG(a,o,i));case"graph":return X(()=>OG(a,o,i));case"logical":return X(()=>BG(a,o,i));case"matrices":return X(()=>WG(a,o,i));case"normalization":return X(()=>VG(a,o,i));case"reduction":return X(()=>UG(a,o,i));case"slice_join":return X(()=>GG(a,o,i));case"sparse":return X(()=>HG(a,o,i));case"spectral":return X(()=>jG(a,o,i));case"string":return X(()=>qG(a,o,i));case"transformation":return X(()=>XG(a,o,i));case"hash_table":return zG(a,o,i,s);case"custom":let l=gS(a.op);if(l&&l.customExecutor)return l.customExecutor(new kG(a,o,i));throw TypeError(`Custom op ${a.op} is not registered.`);default:throw TypeError(`Unknown op '${a.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(e,t,n);return v.isPromise(r)?r.then(a=>[].concat(a)):[].concat(r)}var US=class{constructor(e={},t={},n={},s={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=s,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function GS(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,c=Object.keys(e).map(p=>vs(p)[0]),u=[];s!=null&&(u=s.map(p=>vs(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((HS(p)||QG(p)||eH(p))&&o==null&&(o=p,i=o.children.map(h=>h.name).filter(h=>r.has(h))),r.add(p.name),n[p.name]==null&&c.indexOf(p.name)===-1&&u.indexOf(p.name)===-1){if(p.inputs.length===0){a.push(p.name);continue}p.inputs.forEach(h=>{l.has(h.name)||(l.add(h.name),d.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:a,dynamicNode:o,syncInputs:i}}function KG(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(u=>vs(u)[0]).map(u=>e.nodes[u]),i=e.initNodes;o.forEach(u=>{s.has(u.name)&&a.push(u)}),e.weights.forEach(u=>{s.has(u.name)&&a.push(u)}),i!=null&&i.forEach(u=>{s.has(u.name)&&a.push(u)});let l=new Set,c=[];for(;a.length>0;){let u=a.pop();l.add(u.name),t[u.name]||c.push(u),u.children.forEach(d=>{!l.has(d.name)&&s.has(d.name)&&d.inputs.every(p=>l.has(p.name))&&a.push(d)})}return c}var ZG=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],YG=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],JG=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function HS(e){return ZG.indexOf(e.op)>=0}function QG(e){return YG.indexOf(e.op)>=0}function eH(e){return JG.indexOf(e.op)>=0}var WA=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},this.intermediateTensors={},this.keepTensorForDebug=!1,this._outputs=e.outputs,this._inputs=e.inputs,this._initNodes=e.initNodes,this._signature=e.signature,this._functions=e.functions,e.functions!=null&&Object.keys(e.functions).forEach(n=>{this._functionExecutorMap[n]=new WA(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(s=>s.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),s=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+s.join(this.SEPERATOR)}compile(e,t){let n=GS(e,t,this.weightMap,this._initNodes),{missingInputs:s,dynamicNode:r,syncInputs:a}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(s.length>0){let o=t.map(l=>l.name),i=Object.keys(e);throw new Error(`Cannot compute the outputs [${o}] from the provided inputs [${i}]. Missing the following inputs: [${s}]`)}return KG(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let s=n.map(u=>this.graph.nodes[vs(u)[0]]),r=t.map(u=>vs(u)[0]),a=r.map(u=>this.graph.nodes[u]);this.resetIntermediateTensors(),a.length===0&&(a=this._outputs);let o=this.getCompilationKey(s,a),i=this.compiledMap.get(o);i==null&&(i=this.compile(e,a),this.compiledMap.set(o,i));let l={},c={};return X(()=>{let u=new US(this.weightMap,l,c,this.functionExecutorMap),d={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=vs(f),y=[];y[g]=e[f],d[m]=y});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=VS(m,d,u,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);d[m.name]=g,this.checkTensorForDisposal(m.name,m,d,u,p,r,h)}}return this.parent==null&&u.dispose(p),t.map(f=>Un(f,d,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=nG(i.name,n,s);l!=null&&l.forEach(c=>{if(c&&!c.kept&&!r.has(c.id)){let u=o[c.id];if(u===1){if(!this.keepTensorForDebug)c.dispose();else{let[d,p]=Lr(t.name,s);this.intermediateTensors[d]?this.intermediateTensors[d][p]=c:(this.intermediateTensors[d]=[],this.intermediateTensors[d][p]=c)}delete o[c.id]}else u!=null&&o[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}disposeIntermediateTensors(){!this.intermediateTensors||(Object.keys(this.intermediateTensors).forEach(e=>this.intermediateTensors[e].forEach(t=>t.dispose())),this.disposeTensorsMap())}disposeTensorsMap(){!this.tensorsMap||Object.keys(this.tensorsMap).forEach(e=>{this.tensorsMap[e].forEach(n=>{n&&!n.kept&&!n.isDisposed&&!this.keepIds.has(n.id)&&n.dispose()})})}getIntermediateTensors(){return this.tensorsMap}resetIntermediateTensors(){for(let e in this.intermediateTensors)this.intermediateTensors[e].forEach(t=>t.dispose()),delete this.intermediateTensors[e]}async _executeAsync(e,t,n=!1,s={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));try{this.keepTensorForDebug=Y().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){console.warn(c.message)}this.resetIntermediateTensors();let a=new US(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(c=>Un(c,this.tensorsMap,a)),i=o.map(c=>c.id),l=Object.keys(e).map(c=>e[c].id);return this.keepIds=new Set([...i,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(x=>this.graph.nodes[vs(x)[0]]),o=n.map(x=>vs(x)[0]),i=o.map(x=>this.graph.nodes[x]);i.length===0&&(i=this._outputs);let{usedNodes:l,missingInputs:c,dynamicNode:u,syncInputs:d}=GS(e,i,this.weightMap,this._initNodes),p=[...a,...this.graph.weights,...this._initNodes||[]].map(x=>({node:x,contexts:t.currentContext})),h={...this.weightMap};Object.keys(e).forEach(x=>{let[A,b]=vs(x),w=[];w[b]=e[x],h[A]=w});let f={},m=this.getFrozenTensorIds(h),g={};for(;p.length>0;){let x=this.processStack(a,p,t,h,g,m,o,f,l);await Promise.all(x)}u==null&&!s&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=i.filter(x=>!HS(x)&&!Un(x.name,h,t)).map(x=>x.name);if(y.length>0){let x="";throw u!=null&&(x=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${d}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${c}]. ${x}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let d="";if(u.node.op==="Enter"&&k("isConstant",u.node,s,n)&&([d]=Lr(u.node.name,n)),s[u.node.name]==null){let p=VS(u.node,s,n,this._resourceManager);d||([d]=Lr(u.node.name,n));let h=n.currentContext;v.isPromise(p)?c.push(p.then(f=>(s[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l),f))):(s[d]=p,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l))}else this.processChildNodes(u.node,t,n,s,r,l)}return c}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Lr(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=vs(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${a}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&v.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=vs(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=vs(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},tH=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},nH="?tfjs-format=file",sH="model.json",jS=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new tH}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=rs.browserHTTPRequest(e,this.loadOptions);else{let t=rs.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(rs.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let s=rs.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new WA(OS.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=OS.Instance.transformGraph(e.modelInitializer);this.initializer=new WA(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=rs.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof et)&&!Array.isArray(e))return 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next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;ne(e.value)}}},wH=class extends bn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=this.transform(e.value),s=dr.getTensorsInContainer(n);for(let r of t)dr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},kH=class extends bn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},t7=class extends bn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=await this.transform(e.value),s=dr.getTensorsInContainer(n);for(let r of t)dr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},UA=class extends bn{constructor(){super();this.outputQueue=new QS,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},SH=class extends UA{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await 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${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),ws(async()=>(await n.iterator()).columnMajorBatch(e,t,EH),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,ws(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,ws(async()=>(await t.iterator()).filter(s=>X(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return ws(async()=>(await t.iterator()).map(n=>X(()=>e(n))),this.size)}mapAsync(e){let t=this;return ws(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return ws(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,ws(async()=>{let s=VA(async()=>({value:await t.iterator(),done:!1}));return hH(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,ws(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=aH.alea(t||v.now().toString());return ws(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,ws(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};rc.MAX_BUFFER_SIZE=1e4;function ws(e,t=null){return new class extends rc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function TH(e){return ws(async()=>e7(e),e.length)}function NH(e){if(!sc(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return ws(async()=>{let n=await ZS(e,s=>{if(s instanceof rc)return{value:s.iterator(),recurse:!1};if(sc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return fH(n,GA.SHORTEST)},t)}function EH(e){if(e===null)return null;let t=e[0];return uH(t)?{value:RH(e),recurse:!1}:{value:null,recurse:!0}}function RH(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof et?an(e):pt(e)}var r7=class extends rc{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
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`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Cm='"',mp=Symbol("out"),a7=Symbol("field"),Tm=Symbol("quote"),HA=Symbol("quoteafterquote"),o7=Symbol("quoteinquote"),i7=class extends rc{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new r7(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(v.assert(t.delimiter==null,()=>"Delimiter should not be provided when delimWhitespace is true."),this.delimWhitespace=!0,this.delimiter=" "):this.delimiter=t.delimiter?t.delimiter:","}async columnNames(){return this.columnNamesValidated||await this.setColumnNames(),this.configuredColumnsOnly?Object.keys(this.columnConfigs):this.fullColumnNames}async setColumnNames(){let e=await this.maybeReadHeaderLine();if(!this.fullColumnNames&&!e)throw new Error("Column names must be provided if there is no header line.");this.fullColumnNames&&e&&v.assert(e.length===this.fullColumnNames.length,()=>"The length of provided columnNames ("+this.fullColumnNames.length.toString()+") does not match the length of the header line read from file ("+e.length.toString()+")."),this.fullColumnNames||(this.fullColumnNames=e);let t=this.fullColumnNames.reduce((s,r)=>(s[r]=s[r]+1||1,s),{}),n=Object.keys(t).filter(s=>t[s]>1);if(v.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let s of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(s)===-1)throw new Error('The key "'+s+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let t=await(await this.base.iterator()).next();if(t.done)throw new Error("No data was found for CSV parsing.");let n=t.value;return this.parseRow(n,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},s={};for(let r=0;r<this.fullColumnNames.length;r++){let a=this.fullColumnNames[r],o=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!o)){let i=t[r],l=null;if(i==="")if(o&&o.default!==void 0)l=o.default;else{if(o&&(o.required||o.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);l=void 0}else{let c=Number(i);if(isNaN(c))o&&o.dtype==="bool"?l=this.getBoolean(i):l=i;else if(!o||!o.dtype)l=c;else switch(o.dtype){case"float32":l=c;break;case"int32":l=Math.floor(c);break;case"bool":l=this.getBoolean(i);break;default:l=c}}o&&o.isLabel?s[a]=l:n[a]=l}}return Object.keys(s).length===0?n:{xs:n,ys:s}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],s=0,r=e.length,a=mp;for(let o=0;o<r;o++)switch(a){case mp:switch(e.charAt(o)){case Cm:s=o+1,a=Tm;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=mp;break;default:a=a7,s=o;break}break;case a7:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=mp,s=o+1;break;default:}break;case Tm:switch(e.charAt(o)){case Cm:a=HA;break;default:}break;case HA:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=mp,s=o+1;break;case Cm:a=Tm;break;default:a=o7;break}break;case o7:switch(e.charAt(o)){case Cm:a=Tm;break;default:}break;default:}if(a===HA?n.push(e.substring(s,r-1)):n.push(e.substring(s)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},l7=class extends bn{constructor(e){super();this.microphoneConfig=e,this.isClosed=!1,this.fftSize=e.fftSize||1024;let t=Math.log2(this.fftSize);if(this.fftSize<0||t<4||t>14||!Number.isInteger(t))throw new Error(`Invalid fftSize: it must be a power of 2 between 2 to 4 and 2 to 14, but got ${this.fftSize}`);if(this.numFrames=e.numFramesPerSpectrogram||43,this.sampleRateHz=e.sampleRateHz,this.columnTruncateLength=e.columnTruncateLength||this.fftSize,this.audioTrackConstraints=e.audioTrackConstraints,this.smoothingTimeConstant=e.smoothingTimeConstant||0,this.includeSpectrogram=e.includeSpectrogram!==!1,this.includeWaveform=e.includeWaveform===!0,!this.includeSpectrogram&&!this.includeWaveform)throw new Error("Both includeSpectrogram and includeWaveform are false. At least one type of data should be returned.")}summary(){return"microphone"}static async create(e={}){if(Y().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new l7(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),pt(n,t)}},u7=class extends bn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ct([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=fr([a,r,i,o],[1,4])}else this.cropBox=fr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Y().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new u7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Ys.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return X(()=>{let t=Zt(me(e,"float32"),0),n;n=Se.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return H(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},c7=class{},d7=class extends bn{split(e){return new _H(this,e)}},_H=class extends d7{constructor(e,t){super();this.upstream=e,this.impl=new DH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},DH=class extends UA{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},$H=class extends bn{decodeUTF8(){return new FH(this)}},FH=class extends d7{constructor(e){super();this.upstream=e,this.impl=new PH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},PH=class extends UA{constructor(e){super();if(this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=O5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return Y().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},p7=class extends $H{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(Y().get("IS_BROWSER")?e instanceof File||e instanceof Blob:!1),()=>"FileChunkIterator only supports File, Blob and Uint8Array right now."),this.offset=t.offset||0,this.chunkSize=t.chunkSize||1024*1024}summary(){return`FileChunks ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function OH(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=MH(e));let a=await(n||v.fetch)(s,r);if(a.ok){let o=new Uint8Array(await a.arrayBuffer());return new p7(o,t)}else throw new Error(a.statusText)}var MH=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function h7(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var f7=class extends c7{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(h7(this.input)&&Y().get("IS_NODE")){let e=kh();this.input=e.readFileSync(this.input.substr(7))}return new p7(this.input,this.options)}},m7=class extends c7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return h7(this.url)?new f7(this.url,this.fileOptions).iterator():OH(this.url,this.fileOptions)}};function zH(e,t={}){return new i7(new m7(e),t)}function LH(e){let t=VA(e);return ws(async()=>t)}function BH(e){return ws(async()=>{let t=await e();return VA(()=>t.next())})}async function WH(e,t){return u7.create(e,t)}async function VH(e){return l7.create(e)}var UH="0.0.0";function Ee(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var GH=er.whereImpl,g7=class extends su{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new od(this,as())}nextDataId(){return g7.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,Y().get("IS_NODE")&&N.warn(`
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============================
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Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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============================`));let s={id:this.nextDataId()};return this.data.set(s,{values:e,dtype:n,refCount:1}),s}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,s,r){this.data.set(e,{values:t,dtype:s,refCount:r})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let s=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return N.mergeRealAndImagArrays(s,r)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,n)}makeOutput(e,t,n){let s=this.write(e,t,n);return as().makeTensorFromDataId(s,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){Ee([e],"where");let t=this.readSync(e.dataId);return GH(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},jA=g7;jA.nextDataId=0;var Nm={};Me(Nm,{addImpl:()=>A7,bincountImpl:()=>XA,bincountReduceImpl:()=>x7,ceilImpl:()=>b7,concatImpl:()=>KA,equalImpl:()=>v7,expImpl:()=>k7,expm1Impl:()=>I7,floorImpl:()=>C7,gatherNdImpl:()=>T7,gatherV2Impl:()=>N7,greaterEqualImpl:()=>R7,greaterImpl:()=>E7,lessEqualImpl:()=>D7,lessImpl:()=>_7,linSpaceImpl:()=>$7,logImpl:()=>F7,maxImpl:()=>P7,maximumImpl:()=>O7,minimumImpl:()=>M7,multiplyImpl:()=>ZA,negImpl:()=>z7,notEqualImpl:()=>L7,prodImpl:()=>B7,rangeImpl:()=>JA,rsqrtImpl:()=>W7,sigmoidImpl:()=>Dj,simpleAbsImpl:()=>y7,sliceImpl:()=>_m,sparseFillEmptyRowsImpl:()=>U7,sparseReshapeImpl:()=>G7,sparseSegmentReductionImpl:()=>QA,sqrtImpl:()=>Pj,squaredDifferenceImpl:()=>H7,stridedSliceImpl:()=>j7,stringNGramsImpl:()=>q7,stringSplitImpl:()=>X7,stringToHashBucketFastImpl:()=>K7,subImpl:()=>Z7,tileImpl:()=>Y7,topKImpl:()=>Q7,transposeImpl:()=>YA,uniqueImpl:()=>eI});function y7(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var HH=e=>{let{x:t}=e.inputs,n=e.backend;Ee(t,"abs");let s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return s=y7(r),n.makeOutput(s,t.shape,t.dtype)},jH={kernelName:mi,backendName:"cpu",kernelFunc:HH};function Qt(e){return(t,n,s,r,a)=>{let o=N.assertAndGetBroadcastShape(t,n),i=o.length,l=v.computeStrides(o),c=v.sizeFromShape(o),u=v.getTypedArrayFromDType(a,c),d=t.length,p=n.length,h=v.computeStrides(t),f=v.computeStrides(n),m=N.getBroadcastDims(t,o),g=N.getBroadcastDims(n,o);if(m.length+g.length===0)for(let y=0;y<u.length;++y)u[y]=e(s[y%s.length],r[y%r.length]);else for(let y=0;y<u.length;++y){let x=v.indexToLoc(y,i,l),A=x.slice(-d);m.forEach(S=>A[S]=0);let b=v.locToIndex(A,d,h),w=x.slice(-p);g.forEach(S=>w[S]=0);let C=v.locToIndex(w,p,f);u[y]=e(s[b],r[C])}return[u,o]}}function ks(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=n.makeTensorInfo(s.shape,"complex64"),l=n.data.get(i.dataId);return l.complexTensorInfos={real:n.makeTensorInfo(s.shape,"float32",a),imag:n.makeTensorInfo(r.shape,"float32",o)},i}var qH={kernelName:cd,backendName:"cpu",kernelFunc:ks};function Em(e,t,n="float32"){if(n==="complex64"){let r=Em(e,t,"float32"),a=Em(e,t,"float32");return ks({inputs:{real:r,imag:a},backend:e})}let s=v.makeZerosTypedArray(v.sizeFromShape(t),n);return e.makeTensorInfo(t,n,s)}function Br(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var XH={kernelName:Za,backendName:"cpu",kernelFunc:Br};function Cl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.real,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var KH={kernelName:xd,backendName:"cpu",kernelFunc:Cl};function qo(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Br({inputs:{x:r},backend:n});let o=Em(n,r.shape,r.dtype),i=qo({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=ks({inputs:{real:i,imag:o},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=Cl({inputs:{input:r},backend:n}),i=qo({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Br({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32"){let o=n.data.get(r.dataId).values,i=Int32Array.from(o);return n.makeTensorInfo(r.shape,"int32",i)}if(a==="bool"){let o=n.data.get(r.dataId).values,i=v.toTypedArray([0],r.dtype),[l,c]=Qt((u,d)=>u!==d?1:0)(r.shape,[],o,i,"bool");return n.makeTensorInfo(c,"bool",l)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var ZH={kernelName:Oa,backendName:"cpu",kernelFunc:qo};function vn(e,t,n,s){return n==null?({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;Ee([o,i],e);let 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a=0;a<e.length+1;a++)if(a===e.length||t.indexOf(e[a])!==-1){let o=e.subarray(r,a);(!n||o.length!==0)&&s.push(o),r=a+1}}function X7(e,t,n){let s=e.length,r=[],a=0,o=0,i=new Array(s);for(let p=0;p<s;++p){let h=r.length;Wj(e[p],t,n,r);let f=r.length-h;i[p]=f,a+=f,o=Math.max(o,f)}let l=v.getArrayFromDType("int32",a*2),c=new Array(a),u=[s,o],d=0;for(let p=0;p<s;++p)for(let h=0;h<i[p];++h)l[d*2]=p,l[d*2+1]=h,c[d]=r[d],++d;return[l,c,u]}function K7(e,t){let n=v.getArrayFromDType("int32",e.length);for(let s=0;s<e.length;++s)n[s]=v.fingerPrint64(e[s]).modulo(t).getLowBitsUnsigned();return n}var Z7=Qt((e,t)=>e-t),Vj=qA((e,t,n,s)=>({real:e-n,imag:t-s})),ex=vn(vo,Z7,Vj),Uj={kernelName:vo,backendName:"cpu",kernelFunc:ex};function Y7(e,t){let n=new Array(e.rank);for(let r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];let s=ze(n,e.dtype);for(let r=0;r<s.values.length;++r){let a=s.indexToLoc(r),o=new Array(e.rank);for(let l=0;l<o.length;l++)o[l]=a[l]%e.shape[l];let i=e.locToIndex(o);s.values[r]=e.values[i]}return s}var yp=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function J7(e,t,n=0,s=e.length-1){for(;s>n;){if(s-n>600){let i=s-n+1,l=t-n+1,c=Math.log(i),u=.5*Math.exp(2*c/3),d=.5*Math.sqrt(c*u*(i-u)/i)*Math.sign(l-i/2),p=Math.max(n,Math.floor(t-l*u/i+d)),h=Math.min(s,Math.floor(t+(i-l)*u/i+d));J7(e,t,p,h)}let r=e[t],a=n,o=s;for(v.swap(e,n,t),yp(e[s],r)>0&&v.swap(e,n,s);a<o;){for(v.swap(e,a,o),a++,o--;yp(e[a],r)<0;)a=a+1;for(;yp(e[o],r)>0;)o=o-1}yp(e[n],r)===0?v.swap(e,n,o):(o=o+1,v.swap(e,o,s)),o<=t&&(n=o+1),t<=o&&(s=o-1)}}function Q7(e,t,n,s,r){let a=t[t.length-1],[o,i]=[e.length/a,a],l=v.getTypedArrayFromDType(n,o*s),c=v.getTypedArrayFromDType("int32",o*s);for(let d=0;d<o;d++){let p=d*i,h=e.subarray(p,p+i),f=new Array(h.length);h.forEach((x,A)=>f[A]={value:x,index:A}),s<f.length&&(J7(f,s),f=f.slice(0,s)),r&&f.sort(yp);let m=d*s,g=l.subarray(m,m+s),y=c.subarray(m,m+s);for(let x=0;x<s;x++)g[x]=f[x].value,y[x]=f[x].index}let u=t.slice();return u[u.length-1]=s,[ze(u,n,l),ze(u,"int32",c)]}function eI(e,t,n,s){let r=v.parseAxisParam(t,n)[0],a=[1,n[0],1];for(let f=0;f<r;f++)a[0]*=n[f];a[1]=n[r];for(let f=r+1;f<n.length;f++)a[2]*=n[f];let o={},i=new Int32Array(n[r]),l=new sn(a,s,e),c=[],u=a[0]===1&&a[2]===1;for(let f=0;f<n[r];f++){let m;if(u)m=e[f].toString();else{let g=[];for(let y=0;y<a[0];y++)for(let x=0;x<a[2];x++)g.push(l.get(y,f,x));m=g.join(",")}if(o[m]!==void 0)i[f]=o[m];else{let g=Object.keys(o).length;o[m]=g,i[f]=g,c.push(f)}}let d=a.slice();d[1]=Object.keys(o).length;let p=new sn(d,s);c.forEach((f,m)=>{for(let g=0;g<a[0];g++)for(let y=0;y<a[2];y++)p.set(l.get(g,f,y),g,m,y)});let h=n.slice();return h[r]=d[1],{outputValues:p.values,outputShape:h,indices:i}}var Gj="0.0.0";ul("cpu",()=>new jA,1);var tI=mt(Ga,e=>e>=0?e:Math.exp(e)-1),Hj={kernelName:Ga,backendName:"cpu",kernelFunc:tI};function nI(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;Ee([r],"leakyRelu");let 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Jj={kernelName:Pa,backendName:"cpu",kernelFunc:oI};function Qj(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s,p,h,f,m=[];p=oI({inputs:{a:r,b:a},attrs:{transposeA:l,transposeB:c},backend:n}),o&&(h=gp({inputs:{a:p,b:o},backend:n}),m.push(p),p=h),u&&(f=tx(n,p,u,i,d),m.push(p),p=f);for(let y of m)n.disposeIntermediateTensorInfo(y);return p}var eq={kernelName:Io,backendName:"cpu",kernelFunc:Qj},tq=mt(iu,e=>Math.acos(e)),nq={kernelName:iu,backendName:"cpu",kernelFunc:tq},sq=mt(lu,e=>Math.acosh(e)),rq={kernelName:lu,backendName:"cpu",kernelFunc:sq};function aq(e){let{inputs:t,backend:n}=e,s=t;Ee(t,"addN");let r=s.map(i=>n.data.get(i.dataId).values),a=ze(s[0].shape,s[0].dtype),o=a.values;for(let i=0;i<s.length;i++){let l=r[i];for(let c=0;c<o.length;c++)o[c]+=l[c]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var oq={kernelName:Da,backendName:"cpu",kernelFunc:aq};function 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oe=Z*l-y,re=oe;for(;re<0;)re+=d;let ge=Math.min(r.inWidth,f+oe),ve=ae+Z*E,Re=x,Te=0,Fe=0;for(let ot=V;ot<K;ot+=c){let Je=_+ot*s[1];for(let Ze=Q;Ze<te;Ze+=u){let gt=Je+Ze*s[2];for(let it=re;it<ge;it+=d){let yt=gt+it*s[3],_t=e[yt+$];if(a==="max"&&_t>Re?Re=_t:a==="avg"&&(Te+=_t,Fe++),isNaN(Re))break}if(isNaN(Re))break}if(isNaN(Re))break}let Ue=ve+$;b[Ue]=a==="avg"?Te/Fe:Re}}}}return A}function Cq(e,t){let n=ze(t.outShape,"int32"),s=t.strideDepth,r=t.strideHeight,a=t.strideWidth,o=t.dilationDepth,i=t.dilationHeight,l=t.dilationWidth,c=t.effectiveFilterDepth,u=t.effectiveFilterHeight,d=t.effectiveFilterWidth,p=t.padInfo.front,h=t.padInfo.top,f=t.padInfo.left;for(let m=0;m<t.batchSize;++m)for(let g=0;g<t.inChannels;++g)for(let y=0;y<t.outDepth;++y){let x=y*s-p,A=x;for(;A<0;)A+=o;let b=Math.min(t.inDepth,c+x);for(let w=0;w<t.outHeight;++w){let C=w*r-h,S=C;for(;S<0;)S+=i;let E=Math.min(t.inHeight,u+C);for(let R=0;R<t.outWidth;++R){let P=R*a-f,_=P;for(;_<0;)_+=l;let 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u=N.computePool3DInfo(a.shape,o,i,1,l,c),d=u.strideDepth,p=u.strideHeight,h=u.strideWidth,f=u.filterDepth,m=u.filterHeight,g=u.filterWidth,y=u.dilationDepth,x=u.dilationHeight,A=u.dilationWidth,b=u.effectiveFilterDepth,w=u.effectiveFilterHeight,C=u.effectiveFilterWidth,S=b-1-u.padInfo.front,E=C-1-u.padInfo.left,R=w-1-u.padInfo.top,P=ze(a.shape,"float32"),_=1/(f*m*g),$=n.bufferSync(r);for(let T=0;T<u.batchSize;++T)for(let O=0;O<u.inChannels;++O)for(let V=0;V<u.inDepth;++V)for(let K=0;K<u.inHeight;++K)for(let z=0;z<u.inWidth;++z){let j=V-S,W=K-R,Q=z-E,te=0;for(let ae=0;ae<b;ae+=y){let Z=(j+ae)/d;if(!(Z<0||Z>=u.outDepth||Math.floor(Z)!==Z))for(let oe=0;oe<w;oe+=x){let re=(W+oe)/p;if(!(re<0||re>=u.outHeight||Math.floor(re)!==re))for(let ge=0;ge<C;ge+=A){let ve=(Q+ge)/h;if(ve<0||ve>=u.outWidth||Math.floor(ve)!==ve)continue;te+=$.get(T,Z,re,ve,O)}}}P.set(te*_,T,V,K,z,O)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var Dq={kernelName:Eh,backendName:"cpu",kernelFunc:_q};function $q(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Ee([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=N.computePool2DInfo(o.shape,i,l,1,c),d=u.strideHeight,p=u.strideWidth,h=u.filterHeight,f=u.filterWidth,m=u.dilationHeight,g=u.dilationWidth,y=u.effectiveFilterHeight,x=u.effectiveFilterWidth,A=x-1-u.padInfo.left,b=y-1-u.padInfo.top,w=ze(o.shape,"float32"),C=1/(h*f),S=n.data.get(r.dataId).values,E=ze(r.shape,"float32",S);for(let R=0;R<u.batchSize;++R)for(let P=0;P<u.inChannels;++P)for(let _=0;_<u.inHeight;++_)for(let $=0;$<u.inWidth;++$){let T=_-b,O=$-A,V=0;for(let K=0;K<y;K+=m){let z=(T+K)/d;if(!(z<0||z>=u.outHeight||Math.floor(z)!==z))for(let j=0;j<x;j+=g){let W=(O+j)/p;if(W<0||W>=u.outWidth||Math.floor(W)!==W)continue;V+=E.get(R,z,W,P)}}w.set(V*C,R,_,$,P)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var Fq={kernelName:Nh,backendName:"cpu",kernelFunc:$q};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var Oq={kernelName:Xa,backendName:"cpu",kernelFunc:Pq};function Mq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;Ee([r],"batchToSpaceND");let i=a.reduce((y,x)=>y*x),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=Pt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Ws({inputs:{x:h},backend:n,attrs:{perm:c}}),m=Pt({inputs:{x:f},backend:n,attrs:{shape:u}}),g=Tl({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var zq={kernelName:gi,backendName:"cpu",kernelFunc:Mq};function Lq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=XA(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var 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Es=(re+es*h-Q)*K+kn,qs=es*_+js;for(let Rs=Fe;Rs<Ue;++Rs){let ba=(Te+Rs*f-W)*z+Es,Nn=Rs*$+qs;it+=T[ba+Je]*E[Nn+gt]}}}}A[Ze+gt]=it}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var sX={kernelName:$h,backendName:"cpu",kernelFunc:nX};function rX(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s;Ee([r],"conv3dBackpropInputV2");let c=v.computeStrides(r.shape),u=v.computeStrides(a.shape),d=N.computeConv3DInfo(l,a.shape,i,1,o),p=new sn(d.inShape,"float32"),h=p.values,[f,m,g,y]=p.strides,x=n.data.get(r.dataId).values,[A,b,w,C]=c,S=n.data.get(a.dataId).values,[E,R,P,_]=u,{batchSize:$,filterDepth:T,filterHeight:O,filterWidth:V,inChannels:K,inDepth:z,inHeight:j,inWidth:W,outChannels:Q,outDepth:te,outHeight:ae,outWidth:Z,strideDepth:oe,strideHeight:re,strideWidth:ge}=d,ve=T-1-d.padInfo.front,Re=O-1-d.padInfo.top,Te=V-1-d.padInfo.left;for(let Fe=0;Fe<$;++Fe)for(let Ue=0;Ue<K;++Ue)for(let ot=0;ot<z;++ot){let 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cX(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,[u,d,p,h]=r.shape,f=a.shape[0],[m,g]=i,y=ze([f,m,g,h],"float32"),x=n.data.get(a.dataId).values,A=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,w=v.computeStrides(r.shape),C=v.computeStrides(y.shape);for(let S=0;S<f;S++){let E=S*4,R=x[E],P=x[E+1],_=x[E+2],$=x[E+3],T=A[S];if(T>=u)continue;let O=m>1?(_-R)*(d-1)/(m-1):0,V=g>1?($-P)*(p-1)/(g-1):0;for(let K=0;K<m;K++){let z=m>1?R*(d-1)+K*O:.5*(R+_)*(d-1);if(z<0||z>d-1){for(let j=0;j<g;j++)for(let W=0;W<h;W++){let Q=W+j*C[2]+K*C[1]+S*C[0];y.values[Q]=c}continue}if(l==="bilinear"){let j=Math.floor(z),W=Math.ceil(z),Q=z-j;for(let te=0;te<g;te++){let ae=g>1?P*(p-1)+te*V:.5*(P+$)*(p-1);if(ae<0||ae>p-1){for(let ge=0;ge<h;ge++){let ve=ge+te*C[2]+K*C[1]+S*C[0];y.values[ve]=c}continue}let Z=Math.floor(ae),oe=Math.ceil(ae),re=ae-Z;for(let ge=0;ge<h;ge++){let ve=ge+Z*w[2]+j*w[1]+T*w[0],Re=b[ve];ve=ge+oe*w[2]+j*w[1]+T*w[0];let Te=b[ve];ve=ge+Z*w[2]+W*w[1]+T*w[0];let Fe=b[ve];ve=ge+oe*w[2]+W*w[1]+T*w[0];let Ue=b[ve],ot=Re+(Te-Re)*re,Je=Fe+(Ue-Fe)*re;ve=ge+te*C[2]+K*C[1]+S*C[0],y.values[ve]=ot+(Je-ot)*Q}}}else for(let j=0;j<g;++j){let W=g>1?P*(p-1)+j*V:.5*(P+$)*(p-1);if(W<0||W>p-1){for(let ae=0;ae<h;ae++){let Z=ae+j*C[2]+K*C[1]+S*C[0];y.values[Z]=c}continue}let Q=Math.round(W),te=Math.round(z);for(let ae=0;ae<h;ae++){let Z=ae+Q*w[2]+te*w[1]+T*w[0],oe=ae+j*C[2]+K*C[1]+S*C[0];y.values[oe]=b[Z]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var dX={kernelName:xi,backendName:"cpu",kernelFunc:cX};function pX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;Ee(r,"cumsum");let l=N.getAxesPermutation([a],r.shape.length),c=r;l!=null&&(c=Ws({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=N.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let d=Ln(c.dtype,"int32"),p=v.makeZerosTypedArray(v.sizeFromShape(c.shape),d),h=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=i?(y,x)=>y+f-x-1:(y,x)=>y+x;for(let y=0;y<h.length;y+=f)for(let x=0;x<f;x++){let A=m(y,x);if(x===0)p[A]=o?0:h[A];else{let b=m(y,x-1);p[A]=o?h[b]+p[b]:h[A]+p[b]}}let g=n.makeTensorInfo(c.shape,d,p);if(l!=null){let y=N.getUndoAxesPermutation(l),x=Ws({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),x}return g}var hX={kernelName:Ai,backendName:"cpu",kernelFunc:pX};function fX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=XA(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=x7(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var mX={kernelName:Ph,backendName:"cpu",kernelFunc:fX};function gX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;v.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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Got strides ${o} and dilations '${p}'`);let h=N.computeConv2DInfo(r.shape,a.shape,o,p,i,c,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:y,padInfo:x}=h,A=x.left,b=x.top,w=h.outChannels/h.inChannels,C=new sn(h.outShape,r.dtype),S=n.data.get(r.dataId).values,E=n.data.get(a.dataId).values,R=C.values;for(let P=0;P<h.batchSize;++P){let _=P*u[0],$=P*C.strides[0];for(let T=0;T<h.outHeight;++T){let O=$+T*C.strides[1],V=T*h.strideHeight-b;for(let K=0;K<f;++K){let z=V+K*g;if(z<0||z>=h.inHeight)continue;let j=K*d[0],W=_+z*u[1];for(let Q=0;Q<h.outWidth;++Q){let te=O+Q*C.strides[2],ae=Q*h.strideWidth-A;for(let Z=0;Z<m;++Z){let oe=ae+Z*y;if(oe<0||oe>=h.inWidth)continue;let re=j+Z*d[1],ge=W+oe*h.inChannels,ve=te,Re=re;for(let Te=0;Te<h.inChannels;++Te){let Fe=S[ge+Te];for(let Ue=0;Ue<w;++Ue)R[ve+Ue]+=Fe*E[Re+Ue];ve+=w,Re+=w}}}}}}return n.makeTensorInfo(C.shape,C.dtype,C.values)}var AX={kernelName:Va,backendName:"cpu",kernelFunc:cI};function xX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s;Ee([r,a],"depthwiseConv2dNativeBackpropFilter");let d=N.computeConv2DInfo(r.shape,u,o,i,l,c,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new sn(d.filterShape,"float32"),y=d.padInfo.left,x=d.padInfo.top,A=d.outChannels/d.inChannels,b=n.data.get(r.dataId).values,w=new sn(r.shape,r.dtype,b),C=n.data.get(a.dataId).values,S=new sn(a.shape,a.dtype,C);for(let E=0;E<f;++E){let R=Math.max(0,Math.ceil((x-E)/p)),P=Math.min(d.outHeight,(d.inHeight+x-E)/p);for(let _=0;_<m;++_){let $=Math.max(0,Math.ceil((y-_)/h)),T=Math.min(d.outWidth,(d.inWidth+y-_)/h);for(let O=0;O<d.outChannels;++O){let V=Math.trunc(O/A),K=O%A,z=0;for(let j=0;j<d.batchSize;++j)for(let W=R;W<P;++W){let Q=E+W*p-x;for(let te=$;te<T;++te){let ae=_+te*h-y;z+=w.get(j,Q,ae,V)*S.get(j,W,te,O)}}g.set(z,E,_,V,K)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var bX={kernelName:Oh,backendName:"cpu",kernelFunc:xX};function vX(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s;Ee([r,a],"depthwiseConv2DNativeBackpropInput");let d=v.computeStrides(r.shape),p=v.computeStrides(a.shape),h=N.computeConv2DInfo(u,a.shape,o,i,l,c,!0),f=new sn(h.inShape,"float32"),m=f.values,[g,y,x]=f.strides,A=n.data.get(r.dataId).values,[b,w,C]=d,S=n.data.get(a.dataId).values,[E,R,P]=p,{batchSize:_,filterHeight:$,filterWidth:T,inChannels:O,inHeight:V,inWidth:K,outChannels:z,outHeight:j,outWidth:W,strideHeight:Q,strideWidth:te}=h,ae=$-1-h.padInfo.top,Z=T-1-h.padInfo.left,oe=z/O;for(let re=0;re<_;++re)for(let ge=0;ge<O;++ge)for(let ve=0;ve<V;++ve){let Re=ve-ae,Te=Math.max(0,Math.ceil(Re/Q)),Fe=Math.min(j,($+Re)/Q);for(let Ue=0;Ue<K;++Ue){let ot=Ue-Z,Je=Math.max(0,Math.ceil(ot/te)),Ze=Math.min(W,(T+ot)/te),gt=0;for(let it=Te;it<Fe;++it){let yt=it*Q-Re;for(let _t=Je;_t<Ze;++_t){let Qn=_t*te-ot,lt=b*re+w*it+C*_t,ms=E*($-1-yt)+R*(T-1-Qn)+P*ge;for(let kn=0;kn<oe;++kn){let js=ge*oe+kn,es=A[lt+js],gs=S[ms+kn];gt+=es*gs}}}m[g*re+y*ve+x*Ue+ge]=gt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var wX={kernelName:Mh,backendName:"cpu",kernelFunc:vX};function kX(e){let{inputs:t,backend:n}=e,{x:s}=t,r=v.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,o=ze([r,r],s.dtype),i=o.values;for(let c=0;c<a.length;c++)i[c*r+c]=a[c];let l=[...s.shape,...s.shape];return n.makeTensorInfo(l,o.dtype,o.values)}var SX={kernelName:zh,backendName:"cpu",kernelFunc:kX},IX={kernelName:hd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:o,dilations:i}=n,l=t,c=l.data.get(s.dataId).values,u=s.shape.length,d=l.data.get(r.dataId).values,p=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:x,padInfo:A,strideHeight:b,strideWidth:w,filterHeight:C,filterWidth:S,dilationHeight:E,dilationWidth:R,outShape:P}=N.computeDilation2DInfo(s.shape,r.shape,a,o,"NHWC",i),_=v.sizeFromShape(P),$=P.length,T=v.getArrayFromDType(s.dtype,_);for(let V=0;V<h;++V)for(let K=0;K<y;++K){let z=K*b-A.top;for(let j=0;j<x;++j){let W=j*w-A.left;for(let Q=0;Q<g;++Q){let te=Number.MIN_SAFE_INTEGER;for(let Z=0;Z<C;++Z){let oe=z+Z*E;if(oe>=0&&oe<f)for(let re=0;re<S;++re){let ge=W+re*R;if(ge>=0&&ge<m){let ve=v.locToIndex([V,oe,ge,Q],u,v.computeStrides(s.shape)),Re=v.locToIndex([Z,re,Q],p,v.computeStrides(r.shape)),Te=c[ve]+d[Re];Te>te&&(te=Te)}}}let 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|
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
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|
${o.shape}`);let i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=n.data.get(o.dataId).values[0],[d,p,h,f,m]=U7(i,s.shape,s.dtype,l,r.dtype,c,u);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var sY={kernelName:bd,backendName:"cpu",kernelFunc:nY};function rY(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
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${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.data.get(r.dataId).values),i=n.data.get(s.dataId).values,l=Array.from(n.data.get(a.dataId).values),[c,u,d]=G7(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var aY={kernelName:Du,backendName:"cpu",kernelFunc:rY};function oY(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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|
${a.shape}`);if(r.shape[0]!==a.shape[0])throw new Error("segmentIds and indices should have same size.");let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[c,u]=QA(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var iY={kernelName:vd,backendName:"cpu",kernelFunc:oY};function lY(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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${a.shape}`);if(r.shape[0]!==a.shape[0])throw new Error("segmentIds and indices should have same size.");let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[c,u]=QA(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var uY={kernelName:wd,backendName:"cpu",kernelFunc:lY};function cY(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,sliceSize:u,strides:d,outputSize:p}=N.calculateShapes(a,r,i),h=!1,f=n.bufferSync(r),m=n.bufferSync(a),g=n.data.get(o.dataId).values[0],y=yI(f,m,i,p,u,c,l,d,g,h);return n.makeTensorInfo(i,y.dtype,y.values)}var dY={kernelName:kd,backendName:"cpu",kernelFunc:cY};function pY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=N.prepareSplitSize(r,a,i),c=new Array(r.shape.length).fill(0),u=r.shape.slice();return l.map(d=>{let p=[...u];p[i]=d;let 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$e=Y();$e.registerFlag("HAS_WEBGL",()=>$e.getNumber("WEBGL_VERSION")>0);$e.registerFlag("WEBGL_VERSION",()=>dx(2)?2:dx(1)?1:0);$e.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);$e.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>$e.get("WEBGL_VERSION")===2);$e.registerFlag("WEBGL_CPU_FORWARD",()=>!0);$e.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);$e.registerFlag("WEBGL_PACK",()=>$e.getBool("HAS_WEBGL"));$e.registerFlag("WEBGL_PACK_NORMALIZATION",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_CLIP",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_PACK_REDUCE",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_LAZILY_UNPACK",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_CONV_IM2COL",()=>$e.getBool("WEBGL_PACK"));$e.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>LI($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>BI($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=$e.getNumber("WEBGL_VERSION");return e===0?0:WI(e)});$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>$e.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Ou.isMobile());$e.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>VI($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>$e.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:$e.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));$e.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>UI($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_FENCE_API_ENABLED",()=>GI($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>$e.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);$e.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});$e.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Ou.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});$e.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);$e.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);$e.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);$e.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function Gn(){let e,t,n,s,r,a,o,i,l,c;return Y().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=`
|
|
bool isnan_custom(float val) {
|
|
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
|
|
}
|
|
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan_custom(val.x),
|
|
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
|
|
}
|
|
|
|
#define isnan(value) isnan_custom(value)
|
|
`,l="",c=`
|
|
#define round(value) newRound(value)
|
|
int newRound(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 newRound(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
|
|
#define isnan(value) isnan_custom(value)
|
|
bool isnan_custom(float val) {
|
|
return (val > 0. || val < 1. || val == 0.) ? false : true;
|
|
}
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
|
|
}
|
|
`,l=`
|
|
uniform float INFINITY;
|
|
|
|
bool isinf(float val) {
|
|
return abs(val) == INFINITY;
|
|
}
|
|
bvec4 isinf(vec4 val) {
|
|
return equal(abs(val), vec4(INFINITY));
|
|
}
|
|
`,c=`
|
|
int round(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 round(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:c}}function _l(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function Um(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / outShapeStrides[${a}]`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * outShapeStrides[${a}]`:`index -= ${e[a]} * outShapeStrides[${a}]`;return`${o}; ${i};`}).join("")}function uJ(e,t){let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function cJ(e,t,n="index"){let s=e.map((a,o)=>o),r=uJ(s,t);return r.map((a,o)=>{let i=`int ${e[o]} = ${n} / ${r[o]}`,l=o===r.length-1?`int ${e[o+1]} = ${n} - ${e[o]} * ${r[o]}`:`index -= ${e[o]} * ${r[o]}`;return`${i}; ${l};`}).join("")}function hx(e){let t=v.computeStrides(e).map(n=>n.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}function fx(){return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
|
|
}
|
|
`}var HI=`
|
|
const float FLOAT_MAX = 1.70141184e38;
|
|
const float FLOAT_MIN = 1.17549435e-38;
|
|
|
|
lowp vec4 encode_float(highp float v) {
|
|
if (isnan(v)) {
|
|
return vec4(255, 255, 255, 255);
|
|
}
|
|
|
|
highp float av = abs(v);
|
|
|
|
if(av < FLOAT_MIN) {
|
|
return vec4(0.0, 0.0, 0.0, 0.0);
|
|
} else if(v > FLOAT_MAX) {
|
|
return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;
|
|
} else if(v < -FLOAT_MAX) {
|
|
return vec4(0.0, 0.0, 128.0, 255.0) / 255.0;
|
|
}
|
|
|
|
highp vec4 c = vec4(0,0,0,0);
|
|
|
|
highp float e = floor(log2(av));
|
|
highp float m = exp2(fract(log2(av))) - 1.0;
|
|
|
|
c[2] = floor(128.0 * m);
|
|
m -= c[2] / 128.0;
|
|
c[1] = floor(32768.0 * m);
|
|
m -= c[1] / 32768.0;
|
|
c[0] = floor(8388608.0 * m);
|
|
|
|
highp float ebias = e + 127.0;
|
|
c[3] = floor(ebias / 2.0);
|
|
ebias -= c[3] * 2.0;
|
|
c[2] += floor(ebias) * 128.0;
|
|
|
|
c[3] += 128.0 * step(0.0, -v);
|
|
|
|
return c / 255.0;
|
|
}
|
|
`,{getBroadcastDims:jI}=N;function dJ(e,t,n){let s=[];if(e.forEach(h=>{let f=v.sizeFromShape(h.shapeInfo.logicalShape);if(h.shapeInfo.isUniform?s.push(`uniform float ${h.name}${f>1?`[${f}]`:""};`):(s.push(`uniform sampler2D ${h.name};`),s.push(`uniform int offset${h.name};`)),n.enableShapeUniforms){let{uniformShape:m}=mx(n.packedInputs,h.shapeInfo.logicalShape,h.shapeInfo.texShape);switch(m.length){case 1:s.push(`uniform int ${h.name}Shape;`);break;case 2:s.push(`uniform ivec2 ${h.name}Shape;`);break;case 3:s.push(`uniform ivec3 ${h.name}Shape;`);break;case 4:s.push(`uniform ivec4 ${h.name}Shape;`);break;default:break}s.push(`uniform ivec2 ${h.name}TexShape;`)}}),n.enableShapeUniforms){switch(t.logicalShape.length){case 1:s.push("uniform int outShape;");break;case 2:s.push("uniform ivec2 outShape;"),s.push("uniform int outShapeStrides;");break;case 3:s.push("uniform ivec3 outShape;"),s.push("uniform ivec2 outShapeStrides;");break;case 4:s.push("uniform ivec4 outShape;"),s.push("uniform ivec3 outShapeStrides;");break;default:break}s.push("uniform ivec2 outTexShape;")}n.customUniforms&&n.customUniforms.forEach(h=>{s.push(`uniform ${h.type} ${h.name}${h.arrayIndex?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
|
|
`),a=e.map(h=>pJ(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
|
|
`),o=t.texShape,i=Gn(),l=mJ(i),c,u,d=AJ(i);return t.isPacked?(c=hJ(t.logicalShape,o,n.enableShapeUniforms),u=yJ(i)):(c=fJ(t.logicalShape,o,n.enableShapeUniforms),u=gJ(i)),n.packedInputs&&(d+=wJ),[d,l,u,r,c,a,n.userCode].join(`
|
|
`)}function cc(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return FJ(e,t);case 1:return OJ(e,t);case 2:return zJ(e,t);case 3:return BJ(e,t);case 4:return VJ(e,t);case 5:return UJ(e);case 6:return GJ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function qI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return $J(e);case 1:return PJ(e,t);case 2:return MJ(e,t);case 3:return LJ(e,t);default:return WJ(e,t)}}function pJ(e,t,n=!1,s){let r="";n?r+=qI(e,s):r+=cc(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=HJ(e,t):r+=jJ(e,t)),r}function hJ(e,t,n){switch(e.length){case 0:return XI();case 1:return kJ(e,t,n);case 2:return _J(e,t,n);case 3:return IJ(e,t,n);default:return TJ(e,t,n)}}function fJ(e,t,n){switch(e.length){case 0:return XI();case 1:return SJ(e,t,n);case 2:return DJ(e,t,n);case 3:return CJ(e,t,n);case 4:return NJ(e,t,n);case 5:return EJ(e,t);case 6:return RJ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function mJ(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function gJ(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function yJ(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function AJ(e){return`${e.version}
|
|
precision highp float;
|
|
precision highp int;
|
|
precision highp sampler2D;
|
|
${e.varyingFs} vec2 resultUV;
|
|
${e.defineOutput}
|
|
const vec2 halfCR = vec2(0.5, 0.5);
|
|
|
|
struct ivec5
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
};
|
|
|
|
struct ivec6
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
int v;
|
|
};
|
|
|
|
uniform float NAN;
|
|
${e.defineSpecialNaN}
|
|
${e.defineSpecialInf}
|
|
${e.defineRound}
|
|
|
|
int imod(int x, int y) {
|
|
return x - y * (x / y);
|
|
}
|
|
|
|
int idiv(int a, int b, float sign) {
|
|
int res = a / b;
|
|
int mod = imod(a, b);
|
|
if (sign < 0. && mod != 0) {
|
|
res -= 1;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
//Based on the work of Dave Hoskins
|
|
//https://www.shadertoy.com/view/4djSRW
|
|
#define HASHSCALE1 443.8975
|
|
float random(float seed){
|
|
vec2 p = resultUV * seed;
|
|
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
|
|
p3 += dot(p3, p3.yzx + 19.19);
|
|
return fract((p3.x + p3.y) * p3.z);
|
|
}
|
|
|
|
${xJ}
|
|
${bJ}
|
|
${vJ}
|
|
`}var xJ=`
|
|
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
|
|
int texR = index / texNumC;
|
|
int texC = index - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
|
|
int texelIndex = index / 2;
|
|
int texR = texelIndex / texNumC;
|
|
int texC = texelIndex - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,bJ=`
|
|
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
|
|
int texNumC, int row, int col) {
|
|
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
|
|
int texR = texelIndex / texNumC;
|
|
int texC = texelIndex - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,vJ=`
|
|
vec2 packedUVfrom3D(int texNumR, int texNumC,
|
|
int texelsInBatch, int texelsInLogicalRow, int b,
|
|
int row, int col) {
|
|
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
|
|
int texR = index / texNumC;
|
|
int texC = index - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,wJ=`
|
|
float getChannel(vec4 frag, vec2 innerDims) {
|
|
vec2 modCoord = mod(innerDims, 2.);
|
|
return modCoord.x == 0. ?
|
|
(modCoord.y == 0. ? frag.r : frag.g) :
|
|
(modCoord.y == 0. ? frag.b : frag.a);
|
|
}
|
|
float getChannel(vec4 frag, int dim) {
|
|
float modCoord = mod(float(dim), 2.);
|
|
return modCoord == 0. ? frag.r : frag.g;
|
|
}
|
|
`;function XI(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function kJ(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return s[0]===1?n?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ceil(float(outTexShape[1]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${s[1]}.0);
|
|
}
|
|
`:s[1]===1?n?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ceil(float(outTexShape[0]) / 2.0));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${s[0]}.0);
|
|
}
|
|
`:n?`
|
|
int getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
return 2 * (resTexRC.x * packedTexShape[1] + resTexRC.y);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
return 2 * (resTexRC.x * ${s[1]} + resTexRC.y);
|
|
}
|
|
`}function SJ(e,t,n){return t[0]===1?n?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * float(outTexShape[1]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * ${t[1]}.0);
|
|
}
|
|
`:t[1]===1?n?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * float(outTexShape[0]));
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * ${t[0]}.0);
|
|
}
|
|
`:n?`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
return resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
return resTexRC.x * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function IJ(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
int index = resTexRC.x * ${s[1]} + resTexRC.y;
|
|
|
|
int b = index / ${a};
|
|
index -= b * ${a};
|
|
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function CJ(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${Um(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let s=_l(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function TJ(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let c=2;c<e.length-1;c++)o*=e[e.length-c-1],i=`
|
|
int b${c} = index / ${o};
|
|
index -= b${c} * ${o};
|
|
`+i,l=`b${c}, `+l;return`
|
|
ivec${e.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
int index = resTexRC.x * ${s[1]} + resTexRC.y;
|
|
|
|
${i}
|
|
|
|
int b = index / ${a};
|
|
index -= b * ${a};
|
|
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec${e.length}(${l});
|
|
}
|
|
`}function NJ(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
${Um(["r","c","d","d2"],e)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let s=_l(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function EJ(e,t){let n=_l(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function RJ(e,t){let n=_l(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function _J(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${s[0]}, ${s[1]}));
|
|
}
|
|
`;let r=Math.ceil(e[1]/2);return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${s[0]}, ${s[1]}));
|
|
|
|
int index = resTexRC.x * ${s[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${r});
|
|
int c = imod(index, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function DJ(e,t,n){return v.arraysEqual(e,t)?n?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:e[1]===1?n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:e[0]===1?n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
int r = index / outShape[1];
|
|
int c = index - r * outShape[1];
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${e[1]};
|
|
int c = index - r * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Dl(e){return`offset${e}`}function $J(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Gn();return`
|
|
vec4 ${n}() {
|
|
return ${s.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function FJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
|
|
float ${s}() {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let o=Dl(n);if(t)return`
|
|
float ${s}() {
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let[i,l]=e.shapeInfo.texShape;return`
|
|
float ${s}() {
|
|
vec2 uv = uvFromFlat(${i}, ${l}, ${o});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function PJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=Gn();if(t)return`
|
|
vec4 ${s}(int index) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom1D(
|
|
packedTexShape[0], packedTexShape[1], index);
|
|
return ${a.texture2D}(${n}, uv);
|
|
}
|
|
`;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
|
|
vec4 ${s}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${o[0]}, ${o[1]}, index);
|
|
return ${a.texture2D}(${n}, uv);
|
|
}
|
|
`}function OJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int index) {
|
|
${dc(e)}
|
|
}
|
|
`;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
|
|
float ${s}(int index) {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let i=Dl(n);return o===1?t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:a===1?t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function MJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=Gn();if(a!=null&&v.arraysEqual(n,a))return t?`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`:`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
|
|
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`;if(t)return`
|
|
vec4 ${r}(int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`;let c=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${u}, ${c[0]}, ${c[1]}, row, col);
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`}function zJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(n,a)){if(t)return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let p=a[0],h=a[1];return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}let{newShape:o,keptDims:i}=v.squeezeShape(n),l=o;if(l.length<n.length){let p=pc(e,l),h=["row","col"];return`
|
|
${cc(p,t)}
|
|
float ${r}(int row, int col) {
|
|
return ${r}(${hc(h,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
|
|
${dc(e)}
|
|
}
|
|
`;let c=a[0],u=a[1],d=Dl(s);return u===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${c}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:c===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:t?`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${s}Shape[1] + col + ${d};
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${n[1]} + col + ${d};
|
|
vec2 uv = uvFromFlat(${c}, ${u}, index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function LJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=pc(e,p),m=["b","row","col"];return`
|
|
${qI(f,t)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${hc(m,h)});
|
|
}
|
|
`}let i=Gn();if(t)return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom3D(
|
|
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
|
|
return ${i.texture2D}(${s}, uv);
|
|
}
|
|
`;let l=o[0],c=o[1],u=Math.ceil(n[2]/2),d=u*Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${l}, ${c}, ${d}, ${u}, b, row, col);
|
|
return ${i.texture2D}(${s}, uv);
|
|
}
|
|
`}function BJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=v.squeezeShape(n),c=i;if(c.length<n.length){let m=pc(e,c),g=["row","col","depth"];return`
|
|
${cc(m,t)}
|
|
float ${r}(int row, int col, int depth) {
|
|
return ${r}(${hc(g,l)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${a}, ${o}, 1)));
|
|
${dc(e)}
|
|
}
|
|
`;let u=e.shapeInfo.texShape,d=u[0],p=u[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
int stride1 = ${s}Shape[2];
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(stride1, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${o}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;if(p===o&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let f=Dl(s);return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int stride0 = ${s}Shape[1] * ${s}Shape[2];
|
|
int stride1 = ${s}Shape[2];
|
|
int index = row * ${a} + col * ${o} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a} + col * ${o} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${d}, ${p}, index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function WJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=Gn();if(t)return`
|
|
vec4 ${s}(int b2, int b, int row, int col) {
|
|
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
|
|
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
|
|
texelsInBatch *= ${n}Shape[1];
|
|
index = b2 * texelsInBatch + index;
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
int texR = index / packedTexShape[1];
|
|
int texC = index - texR * packedTexShape[1];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
|
|
}
|
|
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],c=l[0],u=l[1],d=Math.ceil(a[o-1]/2),p=d*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=a[o-m-1],f=`b${m} * ${p} + `+f;return`
|
|
vec4 ${s}(${h}) {
|
|
int index = ${f};
|
|
int texR = index / ${u};
|
|
int texC = index - texR * ${u};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${c});
|
|
return ${r.texture2D}(${n}, uv);
|
|
}
|
|
`}function VJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(n);if(l.length<n.length){let x=pc(e,l),A=["row","col","depth","depth2"];return`
|
|
${cc(x,t)}
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
return ${r}(${hc(A,c)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${o}, ${a}, 1)));
|
|
${dc(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&u==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
${f}
|
|
${m}
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(stride1, stride2, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${o}, ${a}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;if(h===a&&u==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${n[1]*n[2]}, ${n[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let y=Dl(s);return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
${f}
|
|
${m}
|
|
${g}
|
|
int index = row * stride0 + col * stride1 +
|
|
depth * stride2 + depth2;
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${y});
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${o} +
|
|
depth * ${a} + depth2;
|
|
vec2 uv = uvFromFlat(${p}, ${h}, index + ${y});
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function UJ(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(t);if(l.length<t.length){let m=pc(e,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${cc(m)}
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${s}(${hc(g,c)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${o}, ${a}, ${r})) +
|
|
depth3;
|
|
${dc(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&u==null)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${o}, ${a}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===r&&u==null)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]},
|
|
${t[2]*t[3]}, ${t[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=Dl(n);return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${o} + depth * ${a} +
|
|
depth2 * ${r} + depth3 + ${f};
|
|
vec2 uv = uvFromFlat(${p}, ${h}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function GJ(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=v.squeezeShape(t);if(r.length<t.length){let g=pc(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${cc(g)}
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${s}(${hc(y,a)});
|
|
}
|
|
`}let o=t[5],i=t[4]*o,l=t[3]*i,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${u}, ${c}, ${l}, ${i})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${o}, 1)));
|
|
${dc(e)}
|
|
}
|
|
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===u&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${c}, ${l}, ${i}, ${o})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(f===o&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=Dl(n);return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${u} + col * ${c} + depth * ${l} +
|
|
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
|
|
vec2 uv = uvFromFlat(${h}, ${f}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function dc(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function HJ(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=jI(e.shapeInfo.logicalShape,t.logicalShape),l=vt(o),c=o-a,u,d=["x","y","z","w","u","v"];a===0?u="":o<2&&i.length>=1?u="coords = 0;":u=i.map(x=>`coords.${d[x+c]} = 0;`).join(`
|
|
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((x,A)=>`coords.${d[A+c]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,y=v.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!y)h=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(m&&!y)o===1?h=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:h=`
|
|
return vec4(outputValue.x);
|
|
`;else if(i.length){let x=a-2,A=a-1;i.indexOf(x)>-1&&i.indexOf(A)>-1?h="return vec4(outputValue.x);":i.indexOf(x)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(A)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${r}() {
|
|
${l} coords = getOutputCoords();
|
|
${u}
|
|
vec4 outputValue = get${s}(${p});
|
|
${h}
|
|
}
|
|
`}function jJ(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=t.texShape,o=e.shapeInfo.texShape,i=e.shapeInfo.logicalShape.length,l=t.logicalShape.length;if(!e.shapeInfo.isUniform&&i===l&&e.shapeInfo.flatOffset==null&&v.arraysEqual(o,a))return`
|
|
float ${r}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let c=vt(l),u=jI(e.shapeInfo.logicalShape,t.logicalShape),d=l-i,p,h=["x","y","z","w","u","v"];i===0?p="":l<2&&u.length>=1?p="coords = 0;":p=u.map(m=>`coords.${h[m+d]} = 0;`).join(`
|
|
`);let f="";return l<2&&i>0?f="coords":f=e.shapeInfo.logicalShape.map((m,g)=>`coords.${h[g+d]}`).join(", "),`
|
|
float ${r}() {
|
|
${c} coords = getOutputCoords();
|
|
${p}
|
|
return get${s}(${f});
|
|
}
|
|
`}function vt(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function mx(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function pc(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function hc(e,t){return t.map(n=>e[n]).join(", ")}function qJ(e,t,n,s){let r=n.map((b,w)=>{let C={logicalShape:b.shape,texShape:b.isUniform?null:b.texData.texShape,isUniform:b.isUniform,isPacked:b.isUniform?!1:b.texData.isPacked,flatOffset:null};return b.texData!=null&&b.texData.slice!=null&&b.texData.slice.flatOffset>0&&(C.flatOffset=b.texData.slice.flatOffset),{name:t.variableNames[w],shapeInfo:C}}),a=r.map(b=>b.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=dJ(r,o,t),l=SI(e.gl,i),c=e.createProgram(l),u=null,d=e.getUniformLocation(c,"NAN",!1);Y().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let p=!1,h={},f={},m={};for(let b=0;b<t.variableNames.length;b++){let w=t.variableNames[b];h[w]=e.getUniformLocation(c,w,p),h[`offset${w}`]=e.getUniformLocation(c,`offset${w}`,p),t.enableShapeUniforms&&(f[`${w}Shape`]=e.getUniformLocation(c,`${w}Shape`,p),m[`${w}TexShape`]=e.getUniformLocation(c,`${w}TexShape`,p))}let g,y,x;t.enableShapeUniforms&&(g=e.getUniformLocation(c,"outShape",p),x=e.getUniformLocation(c,"outShapeStrides",p),y=e.getUniformLocation(c,"outTexShape",p));let A=[];return t.customUniforms&&t.customUniforms.forEach((b,w)=>{A[w]=e.getUniformLocation(c,b.name,p)}),{program:t,fragmentShader:l,source:i,webGLProgram:c,uniformLocations:h,customUniformLocations:A,inShapeInfos:a,outShapeInfo:o,infLoc:u,nanLoc:d,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:x,outTexShapeLocation:y}}function KI(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,s)=>{let r=n.logicalShape,a=t[s],o=a.shape;if(!v.arraysEqual(r,o))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${o} must match`);if(n.isUniform&&a.isUniform)return;let i=n.texShape,l=a.isUniform?null:a.texData.texShape;if(!v.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function XJ(e,t,n,s,r){t.program.enableShapeUniforms||(KI(t.inShapeInfos,n),KI([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,c)=>{let u=t.program.variableNames[c],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`],h=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(h){let{uniformShape:m}=mx(t.program.packedInputs,l.shape,l.texData.texShape);switch(m.length){case 1:e.gl.uniform1iv(h,new Int32Array(m));break;case 2:e.gl.uniform2iv(h,new Int32Array(m));break;case 3:e.gl.uniform3iv(h,new Int32Array(m));break;case 4:e.gl.uniform4iv(h,new Int32Array(m));break;default:break}}if(f&&e.gl.uniform2i(f,l.texData.texShape[0],l.texData.texShape[1]),d!=null){if(l.isUniform){if(v.sizeFromShape(l.shape)<2)e.gl.uniform1f(d,l.uniformValues[0]);else{let m=l.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),e.gl.uniform1fv(d,m)}return}l.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,l.texData.slice.flatOffset),e.setInputMatrixTexture(l.texData.texture,d,c)}});let i=t.outShapeLocation;if(i)switch(s.shape.length){case 1:e.gl.uniform1iv(i,new Int32Array(s.shape));break;case 2:e.gl.uniform2iv(i,new Int32Array(s.shape));break;case 3:e.gl.uniform3iv(i,new Int32Array(s.shape));break;case 4:e.gl.uniform4iv(i,new Int32Array(s.shape));break;default:break}if(t.outShapeStridesLocation){let l=v.computeStrides(s.shape);switch(s.shape.length){case 2:e.gl.uniform1iv(t.outShapeStridesLocation,new Int32Array(l));break;case 3:e.gl.uniform2iv(t.outShapeStridesLocation,new Int32Array(l));break;case 4:e.gl.uniform3iv(t.outShapeStridesLocation,new Int32Array(l));break;default:break}}t.outTexShapeLocation&&e.gl.uniform2i(t.outTexShapeLocation,s.texData.texShape[0],s.texData.texShape[1]),t.program.customUniforms&&r&&t.program.customUniforms.forEach((l,c)=>{let u=t.customUniformLocations[c],d=r[c];if(l.type==="float")e.gl.uniform1fv(u,d);else if(l.type==="vec2")e.gl.uniform2fv(u,d);else if(l.type==="vec3")e.gl.uniform3fv(u,d);else if(l.type==="vec4")e.gl.uniform4fv(u,d);else if(l.type==="int")e.gl.uniform1iv(u,d);else if(l.type==="ivec2")e.gl.uniform2iv(u,d);else if(l.type==="ivec3")e.gl.uniform3iv(u,d);else if(l.type==="ivec4")e.gl.uniform4iv(u,d);else throw Error(`uniform type ${l.type} is not supported yet.`)}),e.executeProgram()}function KJ(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:c,uniformShape:u,keptDims:d}=mx(e.packedInputs,o.shape,l),p="",h="",f="";if(u.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${w[0]>1}_${w[1]>1}`}else if(u.length===2&&!e.packedInputs)h=`${u[0]>1}_${u[1]>1}`;else if(u.length>2&&!e.packedInputs){let w=v.computeStrides(u);f=`${w[0]===l[1]}_${w[w.length-1]===l[1]}`}let m=o.shape.length,g=u.length===2&&v.arraysEqual(o.shape,l),y=v.sizeFromShape(o.shape)===1,x=N.getBroadcastDims(o.shape,n.shape),A=!e.packedInputs&&m===n.shape.length&&v.arraysEqual(l,n.texData.texShape),b=e.packedInputs||u.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${A}_${c?d:""}_${u.length}_${y}_${x}_${g}_${p}_${h}_${f}_${b}_${i}`}else{let l=o.isUniform?"uniform":o.texData.texShape;s+=`${o.shape}_${l}_${i}`}});let r=e.userCode,a=e.constructor.name;return a+="_"+s+"_"+r+`${Y().getNumber("WEBGL_VERSION")}`,a}function ds(e){return Y().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var ZJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Pm.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gn();this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Um(["r","c","d"],e):_l(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getA(rc.x, rc.y, rc.z);
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},YJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Pm.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gn();this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Um(["r","c","d"],e):_l(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},JJ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=rr.DOWNLOAD;let t=Gn();this.outputShape=e,this.userCode=`
|
|
${HI}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},QJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=rr.DOWNLOAD;let t=Gn();this.outputShape=e,this.userCode=`
|
|
${HI}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},eQ=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Gn();this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${this.enableShapeUniforms?fx():hx(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
int flatIndex = getFlatIndex(coords);
|
|
int offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / texShape[1];
|
|
int c = imod(flatIndex, texShape[1]);
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
|
|
vec4 values = ${n.texture2D}(A, uv);
|
|
|
|
float result;
|
|
|
|
if(offset == 0) {
|
|
result = values[0];
|
|
} else if(offset == 1) {
|
|
result = values[1];
|
|
} else if(offset == 2) {
|
|
result = values[2];
|
|
} else {
|
|
result = values[3];
|
|
}
|
|
|
|
${n.output} = vec4(${s}, 0., 0., 0.);
|
|
}
|
|
`}},tQ=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Gn();this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let s="",r="result";t&&(r="floor(result * 255. + 0.5)");for(let a=0;a<=1;a++)for(let o=0;o<=1;o++){let i=a*2+o;s+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${o} < ${this.enableShapeUniforms?"outShape[2]":`${e[2]}`}) {
|
|
localCoords[2] += ${o};
|
|
if (localCoords[1] + ${a} < ${this.enableShapeUniforms?"outShape[1]":`${e[1]}`}) {
|
|
localCoords[1] += ${a};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / texShape[1];
|
|
int c = imod(flatIndex, texShape[1]);
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(texShape[1], texShape[0]);
|
|
values = ${n.texture2D}(A, uv);
|
|
|
|
if (offset == 0) {
|
|
result[${i}] = values[0];
|
|
} else if (offset == 1) {
|
|
result[${i}] = values[1];
|
|
} else if (offset == 2) {
|
|
result[${i}] = values[2];
|
|
} else {
|
|
result[${i}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${this.enableShapeUniforms?fx():hx(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${s}
|
|
|
|
${n.output} = ${r};
|
|
}
|
|
`}},ZI={};Me(ZI,{bindVertexProgramAttributeStreams:()=>aC,createBufferFromOutputTexture:()=>lC,createFloat16MatrixTexture:()=>tC,createFloat16PackedMatrixTexture:()=>rC,createFloat32MatrixTexture:()=>eC,createIndexBuffer:()=>QI,createPackedMatrixTexture:()=>sC,createUnsignedBytesMatrixTexture:()=>nC,createVertexBuffer:()=>JI,createVertexShader:()=>YI,downloadByteEncodedFloatMatrixFromOutputTexture:()=>cC,downloadFloat32MatrixFromBuffer:()=>uC,downloadMatrixFromPackedOutputTexture:()=>pC,downloadPackedMatrixFromBuffer:()=>dC,getInternalFormatForFloat16MatrixTexture:()=>yx,getInternalFormatForFloat16PackedMatrixTexture:()=>bx,getInternalFormatForFloat32MatrixTexture:()=>gx,getInternalFormatForPackedMatrixTexture:()=>xx,getInternalFormatForUnsignedBytesMatrixTexture:()=>Ax,uploadDenseMatrixToTexture:()=>oC,uploadPixelDataToTexture:()=>iC});function YI(e){let t=Gn(),n=`${t.version}
|
|
precision highp float;
|
|
${t.attribute} vec3 clipSpacePos;
|
|
${t.attribute} vec2 uv;
|
|
${t.varyingVs} vec2 resultUV;
|
|
|
|
void main() {
|
|
gl_Position = vec4(clipSpacePos, 1);
|
|
resultUV = uv;
|
|
}`;return kI(e,n)}function JI(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return TI(e,t)}function QI(e){let t=new Uint16Array([0,1,2,2,1,3]);return NI(e,t)}function Sp(e,t,n,s,r,a){RI(t,n);let o=EI(e),i=e.TEXTURE_2D;return ke(e,()=>e.bindTexture(i,o)),ke(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),ke(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),ke(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),ke(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Y().getNumber("WEBGL_VERSION")===1?ke(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)):ke(e,()=>e.texStorage2D(i,1,s,t,n)),ke(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function gx(e){return e.internalFormatFloat}function eC(e,t,n,s){let[r,a]=bp(t,n);return Sp(e,r,a,gx(s),s.textureFormatFloat,e.FLOAT)}function yx(e){return e.internalFormatHalfFloat}function tC(e,t,n,s){let[r,a]=bp(t,n);return Sp(e,r,a,yx(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function Ax(e){return e.downloadTextureFormat}function nC(e,t,n,s){let[r,a]=bp(t,n);return Sp(e,r,a,Ax(s),e.RGBA,e.UNSIGNED_BYTE)}function xx(e){return e.internalFormatPackedFloat}function sC(e,t,n,s){let[r,a]=lc(t,n);return Sp(e,r,a,xx(s),e.RGBA,e.FLOAT)}function bx(e){return e.internalFormatPackedHalfFloat}function rC(e,t,n,s){let[r,a]=lc(t,n);return Sp(e,r,a,bx(s),e.RGBA,s.textureTypeHalfFloat)}function aC(e,t,n){let s=0,r=3*4,a=3*4+2*4;return ke(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),ux(e,t,"clipSpacePos",n,3,a,s)&&ux(e,t,"uv",n,2,a,r)}function oC(e,t,n,s,r,a){ke(e,()=>e.bindTexture(e.TEXTURE_2D,t));let o,i,l;r instanceof Uint8Array?(o=new Uint8Array(n*s*4),i=e.UNSIGNED_BYTE,l=e.RGBA):(o=new Float32Array(n*s*4),i=e.FLOAT,l=a.internalFormatPackedFloat),o.set(r),Y().getNumber("WEBGL_VERSION")===2?ke(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,s,e.RGBA,i,o)):ke(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),ke(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function iC(e,t,n){ke(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Y().getNumber("WEBGL_VERSION")===2?(ke(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,n.width,n.height,e.RGBA,e.UNSIGNED_BYTE,n.data)),e.flush()):ke(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Y().getNumber("WEBGL_VERSION")===2?(ke(e,()=>e.texSubImage2D(e.TEXTURE_2D,0,0,0,e.RGBA,e.UNSIGNED_BYTE,n)),e.flush()):ke(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),ke(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function lC(e,t,n,s){let r=e.createBuffer();ke(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return ke(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),ke(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),ke(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function uC(e,t,n){let s=e,r=new Float32Array(n);return s.bindBuffer(s.PIXEL_PACK_BUFFER,t),s.getBufferSubData(s.PIXEL_PACK_BUFFER,0,r),s.bindBuffer(s.PIXEL_PACK_BUFFER,null),r}function cC(e,t,n,s){let[r,a]=bp(t,n),o=4,i=new Uint8Array(ZY(t*n,o));return ke(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function dC(e,t,n,s,r,a,o,i){let l=e,c=new Float32Array(YY(a,o));return l.bindBuffer(l.PIXEL_PACK_BUFFER,t),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,c),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),c}function pC(e,t,n){let s=new Float32Array(t*n*4);return ke(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Gm=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=Y().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Fm(t,e)):this.gl=Wr(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(Y().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=vp(this.gl,r),Vs(this.gl,a))this.textureHalfFloatExtension=vp(this.gl,a);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Vs(this.gl,s))this.colorBufferHalfFloatExtension=vp(this.gl,s);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Vs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Vs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=JI(this.gl),this.indexBuffer=QI(this.gl),this.framebuffer=_I(this.gl),this.textureConfig=lx(this.gl,this.textureHalfFloatExtension)}get debug(){return Y().getBool("DEBUG")}dispose(){if(this.disposed)return;this.program!=null&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),this.outputTexture!=null&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture. This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");let e=this.gl;ke(e,()=>e.finish()),ke(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),ke(e,()=>e.deleteFramebuffer(this.framebuffer)),ke(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),ke(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),ke(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),eC(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),tC(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),nC(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),iC(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),oC(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),rC(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),sC(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(cx(this.gl,this.framebuffer),this.outputTexture=null),ke(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>cC(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return dC(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return uC(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=lC(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),s}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(Y().getBool("WEBGL_FENCE_API_ENABLED")){let s=e,r=s.fenceSync(s.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let a=s.clientWaitSync(r,0,0);return a===s.ALREADY_SIGNALED||a===s.CONDITION_SATISFIED},t=r}else Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>pC(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=YI(t));let n=II(t);return ke(t,()=>t.attachShader(n,this.vertexShader)),ke(t,()=>t.attachShader(n,e)),CI(t,n),this.debug&&Mm(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=aC(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&ke(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Mm(this.gl,this.program),ke(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?$I(this.gl,e,t):FI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),ke(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),PI(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=lc(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Mm(this.gl,this.program),wp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),ke(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),ke(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=vp(this.gl,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension}getQueryTimerExtensionWebGL2(){return this.getQueryTimerExtension()}getQueryTimerExtensionWebGL1(){return this.getQueryTimerExtension()}beginQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(s.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await v.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,s=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(s.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),s=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=nQ(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&v.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),zm(this.gl,e,this.framebuffer),this.debug&&wp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(zm(this.gl,this.outputTexture,this.framebuffer),this.debug&&wp(this.gl)):cx(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let s=this.gl;zm(s,e,this.framebuffer),this.debug&&wp(s),this.outputTexture=e,ke(s,()=>s.viewport(0,0,t,n)),ke(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),ke(this.gl,()=>this.gl.scissor(e,t,n,s))}throwIfDisposed(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")}throwIfNoProgram(){if(this.program==null)throw new Error("No GPU program is currently set.")}};function nQ(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:sQ,bincountImpl:hC,bincountReduceImpl:rQ,ceilImpl:aQ,concatImpl:oQ,equalImpl:iQ,expImpl:lQ,expm1Impl:uQ,floorImpl:cQ,gatherNdImpl:dQ,gatherV2Impl:pQ,greaterImpl:hQ,greaterEqualImpl:fQ,lessImpl:mQ,lessEqualImpl:gQ,linSpaceImpl:yQ,logImpl:AQ,maxImpl:xQ,maximumImpl:bQ,minimumImpl:vQ,multiplyImpl:wQ,negImpl:kQ,notEqualImpl:SQ,prodImpl:IQ,rangeImpl:CQ,rsqrtImpl:TQ,sigmoidImpl:NQ,simpleAbsImpl:fC,sliceImpl:EQ,sparseFillEmptyRowsImpl:RQ,sparseReshapeImpl:_Q,sparseSegmentReductionImpl:mC,sqrtImpl:DQ,stridedSliceImpl:$Q,stringNGramsImpl:FQ,stringSplitImpl:PQ,stringToHashBucketFastImpl:OQ,subImpl:MQ,tileImpl:zQ,topKImpl:LQ,transposeImpl:vx,uniqueImpl:BQ}=Nm;function gC(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Hn(e,t){return t===1?[e]:gC(e,t)}function WQ(e,t){if(e===1)return"rc";let n="";for(let s=0;s<e;s++)n+=t[s],s<e-1&&(n+=",");return n}var VQ=class{constructor(e){if(this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.enableShapeUniforms=ds(this.outputShape.length),this.rank===0)this.userCode=`
|
|
void main() {
|
|
setOutput(vec4(getA(), 0., 0., 0.));
|
|
}
|
|
`;else{let t=Hn("rc",this.rank),n=vt(this.rank),s=this.getOutOfBoundsCondition(t),r=this.getSetup(t),a=this.getOutput(t);this.userCode=`
|
|
void main() {
|
|
${n} rc = getOutputCoords();
|
|
|
|
if(${s}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${r}
|
|
|
|
setOutput(vec4(${a}));
|
|
}
|
|
}
|
|
`}}getSourceCoordsArr(e){let t=[];for(let n=0;n<=1;n++)for(let s=0;s<=1;s++){let r=`${n===0?"r":"rp1"}, ${s===0?"c":"cp1"}`;for(let a=2;a<this.rank;a++)r=`${e[e.length-1-a]},`+r;t.push(r)}return t}getOutOfBoundsCondition(e){if(this.rank===1)return`rc > ${this.enableShapeUniforms?"outShape":this.outputShape[0]}`;let t="";for(let n=this.rank-2;n<this.rank;n++)t+=`${e[n]} >= ${this.enableShapeUniforms?`outShape[${n}]`:this.outputShape[n]}`,n<this.rank-1&&(t+="||");return t}getSetup(e){if(this.rank===1)return"";let t=e.slice(-2),n=this.enableShapeUniforms?`outShape[${this.rank} - 1]`:this.outputShape[this.rank-1],s=this.enableShapeUniforms?`outShape[${this.rank} - 2]`:this.outputShape[this.rank-2];return`
|
|
int r = ${t[0]};
|
|
int c = ${t[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${n};
|
|
bool rEdge = rp1 >= ${s};
|
|
`}getOutput(e){let t=this.getSourceCoordsArr(e);return this.rank===1?`getA(rc),
|
|
rc + 1 >= ${this.enableShapeUniforms?"outShape":this.outputShape[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${t[0]}),
|
|
cEdge ? 0. : getA(${t[1]}),
|
|
rEdge ? 0. : getA(${t[2]}),
|
|
rEdge || cEdge ? 0. : getA(${t[3]})`}},yC=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let n="";for(let s=0;s<4;s++){let r="thisRC = rc;";s%2==1&&(r+="thisRC.z += 1;"),s>1&&(r+="thisRC.y += 1;"),n+=`
|
|
${r}
|
|
${s>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${s}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${s>0?"}":""}
|
|
`}this.userCode=`
|
|
${UQ(t,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?fx():hx(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${this.enableShapeUniforms?"outShape[1]":e[1]};
|
|
int cols = ${this.enableShapeUniforms?"outShape[2]":e[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function UQ(e,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?cJ(["r","c","d"],"inputShape"):_l(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var GQ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=xC(t,n),r=bC(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=AC(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===$n.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===$n.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===$n.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===$n.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===$n.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=xC(n,s),a=bC(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=AC(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${this.numFreeTextures} / ${this.numUsedTextures}`,`(${e})`);let t=this._numBytesFree/this._numBytesAllocated;console.log(`Bytes allocated: ${this._numBytesAllocated}`),console.log(`Bytes unused: ${this._numBytesFree} (${Math.round(100*t)}%)`)}get numBytesAllocated(){return this._numBytesAllocated}get numBytesFree(){return this._numBytesFree}getNumUsedTextures(){return this.numUsedTextures}getNumFreeTextures(){return this.numFreeTextures}dispose(){if(this.freeTextures!=null){for(let e in this.freeTextures)this.freeTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});for(let e in this.usedTextures)this.usedTextures[e].forEach(t=>{this.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0}}};function HQ(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F)return 16;if(t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;if(t===n.RGBA8)return 4;throw new Error(`Unknown internal format ${t}`)}function AC(e,t,n,s,r){let a=jQ(t,s),o;if(r){let[l,c]=lc(e[0],e[1]);o=l*c}else{let[l,c]=bp(e[0],e[1]);o=l*c}let i=HQ(n,a);return o*i}function jQ(e,t){switch(e){case $n.PACKED_2X2_FLOAT32:return xx(t);case $n.PACKED_2X2_FLOAT16:return bx(t);case $n.UNPACKED_FLOAT32:return gx(t);case $n.UNPACKED_FLOAT16:return yx(t);case $n.PACKED_4X1_UNSIGNED_BYTE:return Ax(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function qQ(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?$n.PACKED_2X2_FLOAT32:$n.UNPACKED_FLOAT32:e?$n.PACKED_2X2_FLOAT16:$n.UNPACKED_FLOAT16}function xC(e,t){if(e===rr.UPLOAD)return $n.PACKED_2X2_FLOAT32;if(e===rr.RENDER||e==null)return qQ(t);if(e===rr.DOWNLOAD||e===rr.PIXELS)return $n.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function bC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ko=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},kr="if (isnan(x)) return x;",XQ="return x;",vC="return abs(x);",KQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",ZQ=kr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,YQ=kr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Hm="return x;",JQ="return 1.0 / (1.0 + exp(-1.0 * x));",QQ="return x;",eee=`
|
|
vec4 result;
|
|
|
|
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
|
|
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
|
|
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
|
|
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
|
|
|
|
return result;
|
|
`,tee=`
|
|
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
|
|
bvec4 isNaN = isnan(x);
|
|
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`,nee=`
|
|
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
|
|
bvec4 isNaN = isnan(x);
|
|
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`,see="return 1.0 / (1.0 + exp(-1.0 * x));",fc=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},ree=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let t=e.length,n=Hn("rc",t),s=vt(t),r=WQ(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${o}));
|
|
}
|
|
`}},aee=er.whereImpl,oee=1e-7,iee=1e-4,jm={};function lee(e){return e in jm||(jm[e]={}),jm[e]}var uee=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),cee=600;function dee(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*cee/1024/1024}var wC=class extends su{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Wr(Y().getNumber("WEBGL_VERSION"));this.binaryCache=lee(Y().getNumber("WEBGL_VERSION")),this.gpgpu=new Gm(t),this.canvas=t.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpu=e,this.binaryCache={},this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;this.textureManager=new GQ(this.gpgpu),this.numMBBeforeWarning=dee(),this.texData=new od(this,as())}nextDataId(){return wC.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:rr.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:rr.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new fc(o,Hm):d=new Ko(o,Hm);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,c;l&&(c=v.now());let u;if(s==="complex64"){let d=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);u=N.mergeRealAndImagArrays(d,p)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new fc(s,Hm):h=new Ko(s,Hm);let f=this.runWebGLProgram(h,[{dataId:e,shape:s,dtype:a}],a),m=this.read(f.dataId);return this.disposeIntermediateTensorInfo(f),m}if(n!=null)return this.convertAndCacheOnCPU(e);if(Y().getBool("DEBUG")&&!Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&Y().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let l=null,c;if(a!=="complex64"&&Y().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...Om(s))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let u;if(a==="complex64"){let h=await Promise.all([this.read(o.real.dataId),this.read(o.imag.dataId)]),f=h[0],m=h[1];u=N.mergeRealAndImagArrays(f,m)}else if(l==null)u=this.getValuesFromTexture(e);else{let h=v.sizeFromShape(s);u=this.gpgpu.downloadFloat32MatrixFromBuffer(l,h)}if(c!=null&&this.disposeIntermediateTensorInfo(c),l!=null){let h=this.gpgpu.gl;ke(h,()=>h.deleteBuffer(l))}let d=this.convertAndCacheOnCPU(e,u),p=this.pendingRead.get(e);return this.pendingRead.delete(e),p.forEach(h=>h(d)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&as().removeDataId(e,this),this.pendingDeletes--),d}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!vI(n))throw Y().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...Om(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=Y().getBool("WEBGL_PACK")&&s===!0,o=a?Lm(t):t,i=a?new QJ(o):new JJ(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", ")}else o.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,o}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:s,usage:r,isPacked:a,slice:o}=this.texData.get(e),i=o&&o.origDataId||e,l=this.dataRefCount.get(i);l>1?this.dataRefCount.set(i,l-1):(this.dataRefCount.delete(i),t!=null&&(this.numBytesInGPU-=this.computeBytes(s,n),this.textureManager.releaseTexture(t,s,r,a)));let c=this.texData.get(e);c.texture=null,c.texShape=null,c.isPacked=!1,c.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=uee){return Y().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&v.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return aee(e.shape,t)}packedUnaryOp(e,t,n){let s=new fc(e.shape,t),r=this.compileAndRun(s,[e],n);return as().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=fC(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(Y().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,vC,e.dtype);let t=new Ko(e.shape,vC),n=this.compileAndRun(t,[e]);return as().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return this.texData.get(s).usage=null,{dataId:s,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:s}=this.makeTensorInfo(e,t,n);return as().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new ree(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new VQ(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[El(e.shape),...Rl(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[El(t),...Rl(t)],a=new yC(r,n),o=!0,i=[n],l=this.runWebGLProgram(a,[s],e.dtype,i,o);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:s,dtype:r}=t,a=Lm(s),o,i=Om(a);n?o=new YJ(a):o=new ZJ(a);let l=!0,c=[i],u=this.runWebGLProgram(o,[{shape:a,dtype:r,dataId:e}],r,c,l);return{dtype:r,shape:s,dataId:u.dataId}}runWebGLProgram(e,t,n,s,r=!1){let a=this.makeTensorInfo(e.outputShape,n),o=this.texData.get(a.dataId);if(e.packedOutput&&(o.isPacked=!0),e.outPackingScheme===Pm.DENSE){let m=Om(e.outputShape);o.texShape=m.map(g=>g*2)}if(e.outTexUsage!=null&&(o.usage=e.outTexUsage),v.sizeFromShape(a.shape)===0)return o.values=v.getTypedArrayFromDType(a.dtype,0),a;let i=[],l=t.map(m=>{if(m.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let g=this.texData.get(m.dataId);if(g.texture==null){if(!e.packedInputs&&v.sizeFromShape(m.shape)<=Y().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:m.shape,texData:null,isUniform:!0,uniformValues:g.values};e.packedInputs&&(g.isPacked=!0,g.shape=m.shape)}if(this.uploadToGPU(m.dataId),!!g.isPacked!=!!e.packedInputs)m=g.isPacked?this.unpackTensor(m):this.packTensor(m),i.push(m),g=this.texData.get(m.dataId);else if(g.isPacked&&!kp(g.shape,m.shape)){let y=m,x=m.shape;m.shape=g.shape,m=this.packedReshape(m,x),i.push(m),g=this.texData.get(m.dataId),y.shape=x}return{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:o,isUniform:!1},u=KJ(e,l,c),d=this.getAndSaveBinary(u,()=>qJ(this.gpgpu,e,l,c)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),XJ(this.gpgpu,d,l,c,s),i.forEach(m=>this.disposeIntermediateTensorInfo(m)),p&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let f=Y().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!Y().getBool("WEBGL_LAZILY_UNPACK")&&o.isPacked&&r===!1){let m=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),m}return a}compileAndRun(e,t,n,s,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,s,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(Y().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(t=>{this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram),delete this.binaryCache[t]}),this.textureManager.dispose(),this.canvas!=null&&typeof HTMLCanvasElement!="undefined"&&this.canvas instanceof HTMLCanvasElement?this.canvas.remove():this.canvas=null,this.gpgpuCreatedLocally&&(this.gpgpu.program=null,this.gpgpu.dispose()),this.disposed=!0)}floatPrecision(){return this.floatPrecisionValue==null&&(this.floatPrecisionValue=X(()=>{if(!Y().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=Y().getBool("DEBUG");Y().set("DEBUG",!1);let t=this.abs(Ce(1e-8)).dataSync()[0];if(Y().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?oee:iee}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:s,values:r,texture:a,usage:o,isPacked:i}=t;if(a!=null)return;let l=this.activeTimers!=null,c;l&&(c=v.now());let u=t.texShape;if(u==null&&(u=zI(n,i),t.texShape=u),r!=null){let d=Lm(n),p,h=u[1],f=u[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(i||!m)&&([h,f]=lc(u[0],u[1])),i?p=new tQ(d,m):p=new eQ(d,m);let g=m?[f,h]:u,y=this.makeTensorInfo(g,s),x=this.texData.get(y.dataId);m?x.usage=rr.PIXELS:x.usage=rr.UPLOAD,x.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,f,r);let A=[[f,h]],b=!0,w=this.runWebGLProgram(p,[y],s,A,b),C=this.texData.get(w.dataId);t.texture=C.texture,t.texShape=C.texShape,t.isPacked=C.isPacked,t.usage=C.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(w.dataId),t.values=null,l&&(this.uploadWaitMs+=v.now()-c)}else{let d=this.acquireTexture(u,o,s,i);t.texture=d}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:s}=n;return this.releaseGPUData(e),t!=null&&(n.values=pee(t,s)),n.values}acquireTexture(e,t,n,s){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,s)}computeBytes(e,t){return e[0]*e[1]*v.bytesPerElement(t)}},Ip=wC;Ip.nextDataId=0;function pee(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let s=0;s<n.length;++s)n[s]=Math.round(e[s]);return n}else throw new Error(`Unknown dtype ${t}`)}var hee="0.0.0";function kC(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}Ou.isBrowser()&&ul("webgl",()=>new Ip,2);var fee={forceHalfFloat:kC},SC=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,mc=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},qm=`
|
|
result.r = isNaN.r > 0. ? NAN : result.r;
|
|
result.g = isNaN.g > 0. ? NAN : result.g;
|
|
result.b = isNaN.b > 0. ? NAN : result.b;
|
|
result.a = isNaN.a > 0. ? NAN : result.a;
|
|
`,Cp=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=ds(r);let a="";if(s)if(r===0||v.sizeFromShape(this.outputShape)===1)a=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(a=`
|
|
${vt(r)} coords = getOutputCoords();
|
|
`,r===1)this.enableShapeUniforms?a+=`
|
|
result.y = (coords + 1) >= outShape ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`:a+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=Hn("coords",r);this.enableShapeUniforms?a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= outShape[${r} - 2];
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= outShape[${r} - 1];
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`:a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= ${this.outputShape[r-1]};
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`}this.userCode=`
|
|
vec4 binaryOperation(vec4 a, vec4 b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
vec4 a = getAAtOutCoords();
|
|
vec4 b = getBAtOutCoords();
|
|
|
|
vec4 result = binaryOperation(a, b);
|
|
${a}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function Ss(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var mee={kernelName:Za,backendName:"webgl",kernelFunc:Ss};function Zo(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=Ss({inputs:{x:s},backend:n}),l=Ss({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var gee={kernelName:cd,backendName:"webgl",kernelFunc:Zo},IC="return (a < 0.) ? b * a : a;",CC=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function yee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cp(CC,r.shape,o.shape):new mc(IC,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),l}var Aee={kernelName:Ya,backendName:"webgl",kernelFunc:yee},TC="return (a < 0.) ? b * a : a;",NC=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function xee(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cp(NC,s.shape,r.shape):new mc(TC,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var bee={kernelName:uo,backendName:"webgl",kernelFunc:xee},EC="if (isnan(x)) return x;",vee=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,wee=`
|
|
result.r = isNaN.r > 0. ? NAN : result.r;
|
|
result.g = isNaN.g > 0. ? NAN : result.g;
|
|
result.b = isNaN.b > 0. ? NAN : result.b;
|
|
result.a = isNaN.a > 0. ? NAN : result.a;
|
|
`;function at({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,l);return i.makeTensorInfo(o.shape,l,p)}let c=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new fc(o.shape,t):u=new Ko(o.shape,e),i.runWebGLProgram(u,[o],l)}}function In({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:c}=o,u=i;if(s&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(A=>{let[b,w]=A,C={dataId:b.dataId,dtype:b.dtype,shape:l.shape},S={dataId:w.dataId,dtype:w.dtype,shape:c.shape},E=new mc(e,l.shape,c.shape);return u.runWebGLProgram(E,[C,S],Ln(b.dtype,w.dtype))}),x=Zo({inputs:{real:g,imag:y},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(y),x}let d=a||Ln(l.dtype,c.dtype);if((l.dtype==="string"||c.dtype==="string"||u.shouldExecuteOnCPU([l,c]))&&r!=null){let f=u.texData.get(l.dataId).values,m=u.texData.get(c.dataId).values,g=l.dtype==="string"?N.fromUint8ToStringArray(f):f,y=l.dtype==="string"?N.fromUint8ToStringArray(m):m,[x,A]=r(l.shape,c.shape,g,y,d),b=u.makeTensorInfo(A,d),w=u.texData.get(b.dataId);return w.values=x,b}let p=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new Cp(t,l.shape,c.shape,n):h=new mc(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],d)}}function Xm(e,t=!1){if(e==="linear")return t?QQ:XQ;if(e==="relu")return t?tee:ZQ;if(e==="elu")return t?eee:KQ;if(e==="relu6")return t?nee:YQ;if(e==="prelu")return t?NC:TC;if(e==="leakyrelu")return t?CC:IC;if(e==="sigmoid")return t?see:JQ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var RC=class{constructor(e,t,n,s=!1,r=!1,a=!1,o=null,i=!1,l=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n,this.enableShapeUniforms=ds(this.outputShape.length);let c=s?e[1]:e[2],u=Math.ceil(c/2),d=s?"i * 2, rc.y":"rc.y, i * 2",p=r?"rc.z, i * 2":"i * 2, rc.z",h=s?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],f=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],m="",g="";o&&(i?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${o}
|
|
}`:l?m=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${o}
|
|
}`:m=`vec4 activation(vec4 x) {
|
|
${o}
|
|
}`,g="result = activation(result);");let y=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let x="rc.x",A="rc.x";e[0]<t[0]?x=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(A=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${m}
|
|
// Don't use uniform for sharedDimensionPacked for performance.
|
|
const float sharedDimension = ${u}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${u}; i++) {
|
|
int batchA = ${x};
|
|
int batchB = ${A};
|
|
vec4 a = getMatrixA(batchA, ${d});
|
|
vec4 b = getMatrixB(batchB, ${p});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${h[0]} * ${f[0]});
|
|
result += (${h[1]} * ${f[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${y}
|
|
|
|
${g}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},_C={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},DC=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},$C="return a * b;";function wx(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=N.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),c=new DC(_C.REAL,s.shape,r.shape),u=new DC(_C.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=Zo({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[c,u]=wQ(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(u,a),p=n.texData.get(d.dataId);return p.values=c,d}let o;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Cp($C,s.shape,r.shape):o=new mc($C,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var kee={kernelName:oo,backendName:"webgl",kernelFunc:wx};function See(e,t,n){let s=[El(e.shape),...Rl(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[El(t),...Rl(t)],o=new yC(a,s),i=!0,l=[s],c=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function be(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),c=v.sizeFromShape(l);v.assert(i===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let u=o.texData.get(r.dataId);return u.isPacked&&!kp(r.shape,l)&&!(u.texture!==null&&kp(u.shape,l))?See(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var Iee={kernelName:Li,backendName:"webgl",kernelFunc:be},FC=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o=Math.floor(n/4)*4,i=n%4,l="sumValue += dot(values, ones);";if(t!=null){let u=1/t;l=`sumValue += dot(values * ${v.isInt(u)?u.toPrecision(2):u}, ones);`}let c="";r%n>0&&(c=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${c}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${o}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${l}
|
|
}
|
|
|
|
int inIdx = inOffset + ${o};
|
|
if (${i===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${i===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${l}
|
|
} else if (${i===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},Cee=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?l="sumValue":t==="prod"?l="prodValue":t==="all"?l="allValue":t==="any"&&(l="anyValue");let c=Math.floor(n/4)*4,u=n%4,d=`
|
|
if (${t==="sum"}) {
|
|
sumValue += dot(values, ones);
|
|
} else if (${t==="prod"}) {
|
|
vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);
|
|
prodValue *= tmp[0] * tmp[1];
|
|
} else {
|
|
minMaxValue = ${i}(values, minMaxValue);
|
|
if (${t==="min"} || ${t==="max"}) {
|
|
minMaxValue = ${i}(values, minMaxValue);
|
|
bvec4 isNaN = isnan(values);
|
|
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
|
|
minMaxValue = vec4(NAN);
|
|
}
|
|
}
|
|
}
|
|
`,p="vec4";t==="all"?(o="1.0",d=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,p="bvec4"):t==="any"&&(o="0.0",d=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,p="bvec4");let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${o};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${h}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
vec4 minMaxValue = vec4(${o});
|
|
float prodValue = 1.0;
|
|
float sumValue = 0.0;
|
|
float allValue = 1.0;
|
|
float anyValue = 0.0;
|
|
|
|
for (int i = 0; i < ${c}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${d}
|
|
}
|
|
|
|
int inIdx = inOffset + ${c};
|
|
if (${u===1}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
} else if (${u===2}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
} else if (${u===3}) {
|
|
${p} values = ${p}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${d}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function Tee(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function $l(e,t,n,s){let r=Tee(e.shape),a=e;for(let o=0;o<r.length;o++){let{inSize:i,windowSize:l,outSize:c}=r[o],u,d;n==="mean"?u=o===0?new FC({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},i):new FC({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c}):u=new Cee({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},n),d=a,a=s.runWebGLProgram(u,[a],t),d.dataId!==e.dataId&&s.disposeIntermediateTensorInfo(d)}return a}var Nee=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];this.outputShape=n,this.rank=n.length;let s=vt(this.rank),r=Eee(t);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function Eee(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],s=new Array(t);for(let r=0;r<e.length;r++)s[e[r]]=n[r];return s.join()}var Ree=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let c=0;c<n.length;c++)n[c]=e[t[c]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let s=vt(this.rank),r=gC("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=r[c];let o=`vec2(${a.slice(-2).join()})`,i=`++${r[this.rank-1]} < ${n[this.rank-1]}`,l=`getChannel(getA(${a.join()}), ${o})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${l};
|
|
if(${i}) {
|
|
result[1] = ${l};
|
|
}
|
|
--${r[this.rank-1]};
|
|
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${l};
|
|
if(${i}) {
|
|
result[3] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Km(e,t,n){let s=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Ree(e.shape,t):new Nee(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function _ee(e,t,n,s){let r=t,a=e.shape.length,o=v.parseAxisParam(r,e.shape),i=o,l=N.getAxesPermutation(i,a),c=l!=null,u=e;c&&(u=Km(e,l,s),i=N.getInnerMostAxes(i.length,a)),N.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=N.computeOutAndReduceShapes(u.shape,i),h=d;n&&(h=N.expandShapeToKeepDim(d,o));let f=v.sizeFromShape(p),g=v.sizeFromShape(e.shape)/f,y=be({inputs:{x:u},attrs:{shape:[g,f]},backend:s}),x=Pd(e.dtype),A=$l(y,x,"sum",s),b=be({inputs:{x:A},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(y),s.disposeIntermediateTensorInfo(A),c&&s.disposeIntermediateTensorInfo(u),b}function Zm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return _ee(r,a,o,n)}var Dee={kernelName:Ao,backendName:"webgl",kernelFunc:Zm};function jn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];let c;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,p=vx(d,r.shape,r.dtype,a,l);c=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(c.dataId);h.values=p}else c=Km(r,a,o);return c}var $ee={kernelName:ko,backendName:"webgl",kernelFunc:jn},PC=1e3;function Ym({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),b=ol.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[y,d,h]:[y,h,d],C=s?[x,f,p]:[x,p,f],S=be({inputs:{x:e},backend:r,attrs:{shape:w}}),E=be({inputs:{x:t},backend:r,attrs:{shape:C}}),R=[S,E],P=Math.max(y,x),_=n?S.shape[1]:S.shape[2],$=a!=null,T=o!=null,O=l==="leakyrelu",V=l!=null?Xm(l,!0):null,K=$||T||O||V!=null,z;if((h===1||f===1)&&_>PC&&K===!1){let W=S,Q=E;n&&(W=jn({inputs:{x:S},backend:r,attrs:{perm:[0,2,1]}}),R.push(W)),s&&(Q=jn({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),R.push(Q));let te=f!==1,ae=f===1,Z=W;te&&(Z=be({inputs:{x:W},backend:r,attrs:{shape:[P,_,1]}}),R.push(Z));let oe=f===1?2:1,re=Q;ae&&(re=be({inputs:{x:Q},backend:r,attrs:{shape:[P,1,_]}}),R.push(re));let ge=wx({inputs:{a:Z,b:re},backend:r});z=Zm({inputs:{x:ge},backend:r,attrs:{axis:oe,keepDims:!0}}),R.push(ge)}else{let W=Ln(e.dtype,t.dtype),Q=new RC(w,C,[P,h,f],n,s,$,V,T,O),te=[S,E];if(a!=null&&te.push(a),T&&te.push(o),O){let ae=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));te.push(ae),R.push(ae)}z=r.runWebGLProgram(Q,te,W)}let j=be({inputs:{x:z},backend:r,attrs:{shape:b}});R.push(z);for(let W of R)r.disposeIntermediateTensorInfo(W);return j}function Fee(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return Ym({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var Pee={kernelName:Io,backendName:"webgl",kernelFunc:Fee},OC="return abs(x);";function Oee(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=fC(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new fc(s.shape,OC):r=new Ko(s.shape,OC),n.runWebGLProgram(r,[s],s.dtype)}var Mee={kernelName:mi,backendName:"webgl",kernelFunc:Oee},zee=kr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,Lee=at({opSnippet:zee}),Bee={kernelName:iu,backendName:"webgl",kernelFunc:Lee},Wee=kr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,Vee=at({opSnippet:Wee}),Uee={kernelName:lu,backendName:"webgl",kernelFunc:Vee},MC="return a + b;",Gee=In({opSnippet:MC,packedOpSnippet:MC,supportsComplex:!0,cpuKernelImpl:sQ}),Hee={kernelName:Kr,backendName:"webgl",kernelFunc:Gee},jee=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
float result = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}},qee=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${a}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let s=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
vec4 result = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}};function Jm(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Ss({inputs:{x:s[0]},backend:n});if(s.length>Y().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),c=Jm({inputs:s.slice(0,l),backend:n}),u=Jm({inputs:s.slice(l),backend:n});return Jm({inputs:[c,u],backend:n})}let r=s.map(l=>l.dtype).reduce((l,c)=>Ln(l,c)),a=s.map(l=>l.shape),i=Y().getBool("WEBGL_PACK")?new qee(s[0].shape,a):new jee(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var Xee={kernelName:Da,backendName:"webgl",kernelFunc:Jm};function Kee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,i)),N.assertAxesAreInnerMostDims("all",c,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=$l(m,m.dtype,"all",n),y;if(o){let x=N.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var Zee={kernelName:uu,backendName:"webgl",kernelFunc:Kee};function Yee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,i)),N.assertAxesAreInnerMostDims("any",c,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=$l(m,m.dtype,"any",n),y;if(o){let x=N.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var Jee={kernelName:cu,backendName:"webgl",kernelFunc:Yee},Qee=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:s,batchSize:r,outSize:a}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,a];let o=t==="max"?">":"<",i=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${s};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${s}; i++) {
|
|
int inIdx = ${i};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${o} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},ete=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],a=Math.ceil(r/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),s||this.variableNames.push("bestIndicesA");let o=this.outputShape,i=o.length,l=vt(i),c=Hn("coords",i),u,d;if(a===1){d=i+1;let S=vt(d);u=`
|
|
${S} sourceLocR = ${S}(${c.join()}, 0);
|
|
++${c[i-1]};
|
|
${S} sourceLocG = ${S}(${c.join()}, 0);
|
|
++${c[i-2]};
|
|
${S} sourceLocA = ${S}(${c.join()}, 0);
|
|
--${c[i-1]};
|
|
${S} sourceLocB = ${S}(${c.join()}, 0);
|
|
--${c[i-2]};`}else d=i,u=`
|
|
${l} sourceLocR = coords;
|
|
++${c[i-1]};
|
|
${l} sourceLocG = coords;
|
|
++${c[i-2]};
|
|
${l} sourceLocA = coords;
|
|
--${c[i-1]};
|
|
${l} sourceLocB = coords;
|
|
--${c[i-2]};`;let p=["x","y","z","w","u","v"].slice(0,d),h="."+p[d-1],f=p.map(S=>"int "+S),m=Hn("sourceLocR",d-1).concat("inIdx.r"),g=Hn("sourceLocG",d-1).concat("inIdx.g"),y=Hn("sourceLocB",d-1).concat("inIdx.b"),x=Hn("sourceLocA",d-1).concat("inIdx.a"),A=n==="max"?"greaterThan":"lessThan",b=s?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
|
|
getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${y.join()}),
|
|
getBestIndicesAChannel(${x.join()})));`,w=`vec4(
|
|
getAChannel(${m.join()}),
|
|
hasNextCol ? getAChannel(${g.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${y.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${x.join()}) : 0.)`,C=s?"":`
|
|
float getBestIndicesAChannel(${f.join()}) {
|
|
return getChannel(getBestIndicesA(${p.join()}),
|
|
vec2(${p.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${f.join()}) {
|
|
return getChannel(getA(${p.join()}),
|
|
vec2(${p.slice(-2).join()}));
|
|
}
|
|
${C}
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
bool hasNextCol = ${c[i-1]} < ${o[i-1]-1};
|
|
bool hasNextRow = ${c[i-2]} < ${o[i-2]-1};
|
|
${u}
|
|
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
|
|
sourceLocB${h}, sourceLocA${h}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${w};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${b}
|
|
vec4 candidate = ${w};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${A}(candidate, bestValue)) * (vec4(1.0) - vec4(nan)));
|
|
|
|
bestValue = vec4(replace.x ? candidate.x : bestValue.x,
|
|
replace.y ? candidate.y : bestValue.y,
|
|
replace.z ? candidate.z : bestValue.z,
|
|
replace.w ? candidate.w : bestValue.w);
|
|
bestIndex = mix(bestIndex, vec4(inIdx), vec4(replace));
|
|
srcIdx++;
|
|
}
|
|
setOutput(bestIndex);
|
|
}
|
|
`}};function zC(e,t,n,s=null){let r=t.shape[0],a=t.shape[1];s!=null&&(r=s.shape[0],a=s.shape[1]);let o=N.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new Qee(i,n,s==null),c=[t];s!=null&&c.push(s);let u=e.runWebGLProgram(l,c,"int32");if(u.shape[1]===1)return u;let d=zC(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}function LC(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=N.computeOptimalWindowSize(a),i=new ete(r,o,n,s==null),l=s==null?[t]:[t,s],c=e.runWebGLProgram(i,l,"int32");if(c.shape.length===t.shape.length){let u=LC(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function BC(e,t,n,s){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[c,u]=N.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(u),p=be({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=zC(e,p,s);a.push(h);let f=be({inputs:{x:h},backend:e,attrs:{shape:c}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return LC(e,t,s)}function tte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=jn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=BC(n,l,o[0],"max");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var nte={kernelName:$a,backendName:"webgl",kernelFunc:tte};function ste(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=jn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=BC(n,l,o[0],"min");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var rte={kernelName:du,backendName:"webgl",kernelFunc:ste},ate=kr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,ote=at({opSnippet:ate}),ite={kernelName:pu,backendName:"webgl",kernelFunc:ote},lte=kr+"return log(x + sqrt(x * x + 1.0));",ute=at({opSnippet:lte}),cte={kernelName:hu,backendName:"webgl",kernelFunc:ute},dte=kr+`
|
|
return atan(x);
|
|
`,pte=at({opSnippet:dte}),hte={kernelName:fu,backendName:"webgl",kernelFunc:pte},fte=vee+`
|
|
return atan(a, b);
|
|
`,mte=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+wee+`
|
|
return result;
|
|
`,gte=In({opSnippet:fte,packedOpSnippet:mte}),yte={kernelName:gu,backendName:"webgl",kernelFunc:gte},Ate=kr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,xte=at({opSnippet:Ate}),bte={kernelName:mu,backendName:"webgl",kernelFunc:xte},Tp=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideHeight,i=e.strideWidth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterHeight,d=e.effectiveFilterWidth,p=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let f=t==="avg",m=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,g=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let S=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${o}, ${i});
|
|
const ivec2 pads = ivec2(${p}, ${h});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
float avgValue = 0.0;
|
|
|
|
for (int wR = 0; wR < ${u};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${d};
|
|
wC += ${c}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xR, xC, d);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${S} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${s?r?m:g:`wR * ${d} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let x="max",A=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(A="avgValue / count");let b=Math.floor(a/4)*4,w=a%4,C=`
|
|
if (${f}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${x}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${o}, ${i});
|
|
const ivec2 pads = ivec2(${p}, ${h});
|
|
const float initializationValue = ${y};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xR, int xC, int d) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${y});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${u};
|
|
wR += ${l}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${b}; wC += 4) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
getValue(batch, xR, xC + 3 * ${c}, d)
|
|
);
|
|
|
|
${C}
|
|
}
|
|
|
|
int xC = xCCorner + ${b};
|
|
if (${w===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${w===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${w===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
}
|
|
}
|
|
setOutput(${A});
|
|
}
|
|
`}},kx=class{constructor(e,t,n,s=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,o=e.strideDepth,i=e.strideHeight,l=e.strideWidth,c=e.dilationDepth,u=e.dilationHeight,d=e.dilationWidth,p=e.effectiveFilterDepth,h=e.effectiveFilterHeight,f=e.effectiveFilterWidth,m=e.padInfo.front,g=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",A="0.0";if(x||(A="-1.0 / 1e-20"),n){let R=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${o}, ${i}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${y});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
|
|
for (int wD = 0; wD < ${p};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f};
|
|
wC += ${d}) {
|
|
int xC = xCCorner + wC;
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float value = getX(batch, xD, xR, xC, ch);
|
|
|
|
// If a min / max value has already been found, use it. If not,
|
|
// use the current value.
|
|
float currMinMaxValue = mix(
|
|
value, minMaxValue, minMaxValueFound);
|
|
if (value ${R} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${s?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${f} +
|
|
wR * ${f} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let b="max",w=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(w="avgValue / count");let C=Math.floor(a/4)*4,S=a%4,E=`
|
|
if (${x}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${b}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${o}, ${i}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${y});
|
|
const float initializationValue = ${A};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xD, int xR, int xC, int ch) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${A});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${p};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${C}; wC += 4) {
|
|
int xC = xCCorner + wC * ${d};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${d}, ch)
|
|
);
|
|
|
|
${E}
|
|
}
|
|
|
|
int xC = xCCorner + ${C};
|
|
if (${S===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${E}
|
|
} else if (${S===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${E}
|
|
} else if (${S===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${d}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${E}
|
|
}
|
|
}
|
|
setOutput(${w});
|
|
}
|
|
}
|
|
`}};function vte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;uc(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(N.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=N.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Ss({inputs:{x:r},backend:n});let d=new Tp(u,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var wte={kernelName:Fa,backendName:"webgl",kernelFunc:vte};function kte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s,u=[1,1,1],d=N.computePool3DInfo(r.shape,a,o,u,i,l,c),p=new kx(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var Ste={kernelName:ud,backendName:"webgl",kernelFunc:kte},Ite=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.top,u=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${c}, ${u});
|
|
const float avgMultiplier = float(${d});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${i};
|
|
wR += ${a}) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${l};
|
|
wC+= ${o}) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Cte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
|
|
const ivec3 pads = ivec3(${h}, ${f}, ${m});
|
|
const float avgMultiplier = float(${g});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, d) with pos mask(:, :, :, ch) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${u};
|
|
wD += ${i}) {
|
|
float dyD = float(dyDCorner + wD) / ${r}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${l}) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${p};
|
|
wC += ${c}) {
|
|
float dyC = float(dyCCorner + wC) / ${o}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Tte(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=N.computePool3DInfo(o.shape,i,l,d,c,u),h=new Cte(p);return n.runWebGLProgram(h,[r],o.dtype)}var Nte={kernelName:Eh,backendName:"webgl",kernelFunc:Tte};function Ete(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;uc([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=N.computePool2DInfo(o.shape,i,l,1,c),d=new Ite(u);return n.runWebGLProgram(d,[r],o.dtype)}var Rte={kernelName:Nh,backendName:"webgl",kernelFunc:Ete};function _te(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Ym({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var Dte={kernelName:Pa,backendName:"webgl",kernelFunc:_te},$te=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${o};
|
|
float scale = ${i};
|
|
float inv = scale * inversesqrt(variance + float(${a}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}},Fte=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),i="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${o};
|
|
vec4 scale = ${i};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}},Pte=({inputs:e,backend:t,attrs:n})=>{let{x:s,mean:r,variance:a,offset:o,scale:i}=e;v.assert(r.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),v.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),v.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=n;l==null&&(l=.001);let c=[s,r,a],u=null;o!=null&&(u=o.shape,c.push(o));let d=null;i!=null&&(d=i.shape,c.push(i));let p=Y().getBool("WEBGL_PACK_NORMALIZATION")?new Fte(s.shape,r.shape,a.shape,u,d,l):new $te(s.shape,r.shape,a.shape,u,d,l);return t.runWebGLProgram(p,c,c[0].dtype)},Ote={kernelName:Xa,backendName:"webgl",kernelFunc:Pte},Mte=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=vt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=zte(this.rank),s,r=e.map((a,o)=>`sourceLoc.${Sx[o]} = start[${o}] + coords.${Sx[o]};`);s=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${r.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${s}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}},Sx=["x","y","z","w","u","v"];function zte(e){if(e===1)return"sourceLoc";if(e<=6)return Sx.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Lte=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=vt(this.rank),n=Hn("coords",this.rank),s=Hn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
|
|
result.x = ${a};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${s[this.rank-1]};
|
|
result.y = ${a};
|
|
--${s[this.rank-1]};
|
|
}
|
|
`,i=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${s[this.rank-2]};
|
|
result.z = ${a};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${s[this.rank-1]};
|
|
result.w = ${a};
|
|
}
|
|
}
|
|
`,l=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${s[u]} = ${n[u]} + start[${u}];`).join(`
|
|
`);this.userCode=`
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${l}
|
|
vec4 result = vec4(0.);
|
|
${o}
|
|
${i}
|
|
setOutput(result);
|
|
}
|
|
`}};function Bte(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Mt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function gc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Mt.parseSliceParams(r,a,o);if(Mt.assertParamsValid(r,i,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=EQ(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:c}=n.texData.get(r.dataId),u=Mt.isSliceContinous(r.shape,i,l);if(c||!u){let d=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Lte(l):new Mte(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),Bte(r,i,l,n)}var Wte={kernelName:Gi,backendName:"webgl",kernelFunc:gc},Vte=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((x,A)=>x*A),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=[],f=be({inputs:{x:r},backend:n,attrs:{shape:l}}),m=jn({inputs:{x:f},backend:n,attrs:{perm:c}}),g=be({inputs:{x:m},backend:n,attrs:{shape:u}}),y=gc({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeIntermediateTensorInfo(x)),y},Ute={kernelName:gi,backendName:"webgl",kernelFunc:Vte};function Gte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.readSync(r.dataId),l=n.readSync(a.dataId),c=hC(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var Hte={kernelName:Rh,backendName:"webgl",kernelFunc:Gte};function jte(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),o=n.readSync(r.dataId),i=N.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var qte={kernelName:_h,backendName:"webgl",kernelFunc:jte},Xte="return float(a != b);",WC=In({opSnippet:Xte,cpuKernelImpl:SQ,dtype:"bool"}),Kte={kernelName:Di,backendName:"webgl",kernelFunc:WC};function Np(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Ss({inputs:{x:r.complexTensorInfos.real},backend:n})}var Zte={kernelName:xd,backendName:"webgl",kernelFunc:Np},Yte="return float(int(x));";function Jte(e,t){let n=new Ko(e.shape,Yte),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Ix(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Ss({inputs:{x:r},backend:n});let o=Gt(r.shape),i=Ix({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Zo({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=Np({inputs:{input:r},backend:n}),i=Ix({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Ss({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Jte(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=WC({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Qte={kernelName:Oa,backendName:"webgl",kernelFunc:Ix},VC="return ceil(x);",ene=at({opSnippet:VC,packedOpSnippet:VC,cpuKernelImpl:aQ}),tne={kernelName:Ma,backendName:"webgl",kernelFunc:ene},nne=class{constructor(e){this.variableNames=["A"],this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
|
|
|
|
void main() {
|
|
float value = getAAtOutCoords();
|
|
if (isnan(value)) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, minVal, maxVal));
|
|
}
|
|
`}},sne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"minVal",type:"float"},{name:"maxVal",type:"float"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 value = getAAtOutCoords();
|
|
|
|
if (any(isnan(value))) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
|
|
}
|
|
`}};function rne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;Y().getBool("WEBGL_PACK_CLIP")?i=new sne(r.shape):i=new nne(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var ane={kernelName:Zr,backendName:"webgl",kernelFunc:rne},one=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float re = abs(getRealAtOutCoords());
|
|
float im = abs(getImagAtOutCoords());
|
|
float mx = max(re, im);
|
|
|
|
// sadly the length function in glsl is not underflow-safe
|
|
// (at least not on Intel GPUs). So the safe solution is
|
|
// to ensure underflow-safety in all cases.
|
|
setOutput(
|
|
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
|
|
);
|
|
}
|
|
`}};function UC(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function ine(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new one(s.shape),o=[UC(s,r.complexTensorInfos.real),UC(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var lne={kernelName:dd,backendName:"webgl",kernelFunc:ine},une=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,o)=>`T${o}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let a=1;a<t.length;a++)t[a]=t[a-1]+e[a][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let o=t[a-1];n.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${o}));`)}let s=t.length,r=t[t.length-1];n.push(`else setOutput(getT${s}(yR, yC-${r}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},cne=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=vt(s),a=Hn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],c=o.slice(-2),u=o.join(),d=`if (${l} < ${i[0]}) {
|
|
return getChannel(
|
|
getT0(${u}), vec2(${c.join()}));
|
|
}`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
|
|
if (${l} < ${i[f]} && ${l} >= ${i[f-1]}) {
|
|
return getChannel(
|
|
getT${f}(${Qm(o,l,m)}),
|
|
vec2(${Qm(c,l,m)}));
|
|
}`}let p=i.length,h=i[i.length-1];d+=`
|
|
return getChannel(
|
|
getT${p}(${Qm(o,l,h)}),
|
|
vec2(${Qm(c,l,h)}));`,this.userCode=`
|
|
float getValue(${o.map(f=>"int "+f)}) {
|
|
${d}
|
|
}
|
|
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
|
|
|
|
${a[s-1]} = ${a[s-1]} + 1;
|
|
if (${a[s-1]} < ${n[s-1]}) {
|
|
result.g = getValue(${a});
|
|
}
|
|
|
|
${a[s-2]} = ${a[s-2]} + 1;
|
|
if (${a[s-2]} < ${n[s-2]}) {
|
|
result.a = getValue(${a});
|
|
}
|
|
|
|
${a[s-1]} = ${a[s-1]} - 1;
|
|
if (${a[s-2]} < ${n[s-2]} &&
|
|
${a[s-1]} < ${n[s-1]}) {
|
|
result.b = getValue(${a});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Qm(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function e0(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return Ss({inputs:{x:r.complexTensorInfos.imag},backend:n})}var dne={kernelName:md,backendName:"webgl",kernelFunc:e0};function yc(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>Np({inputs:{input:m},backend:n})),d=e.map(m=>e0({inputs:{input:m},backend:n})),p=yc(u,t,n),h=yc(d,t,n),f=Zo({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(y=>{let x=v.sizeFromShape(y.shape.slice(t));return be({inputs:{x:y},backend:n,attrs:{shape:[-1,x]}})}),d=u.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),p=N.computeOutShape(u.map(y=>y.shape),1),h=u[0].shape[0]===1,f=oQ(d,p,s,h),m=N.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),d=yc(e.slice(0,u),t,n),p=yc(e.slice(u),t,n),h=yc([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new cne(e.map(d=>d.shape),t);return n.runWebGLProgram(u,e,s)}let{tensors2D:a,outShape:o}=pne(e,t,n),i=new une(a.map(u=>u.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let c=be({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),c}function pne(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>be({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function GC(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=N.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return Ss({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return N.assertParamsConsistent(l,a),yc(i,a,n)}var hne={kernelName:yi,backendName:"webgl",kernelFunc:GC},HC=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,o=e.padInfo.left,i=e.strideHeight,l=e.strideWidth,c=e.dilationHeight,u=e.dilationWidth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4,m=e.dataFormat==="channelsLast",g=m?1:2,y=m?2:3,x=m?3:1,A="",b="";n&&(s?A=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?A=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:A=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${A}
|
|
|
|
const ivec2 strides = ivec2(${i}, ${l});
|
|
const ivec2 pads = ivec2(${a}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${x}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${g}], coords[${y}]) * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${d}; wR++) {
|
|
int xR = xRCorner + wR * ${c};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p}; wC++) {
|
|
int xC = xCCorner + wC * ${u};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xR, xC, d1),
|
|
getX(batch, xR, xC, d1 + 1),
|
|
getX(batch, xR, xC, d1 + 2),
|
|
getX(batch, xR, xC, d1 + 3)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec4 xValues = vec4(
|
|
getX(batch, d1, xR, xC),
|
|
getX(batch, d1 + 1, xR, xC),
|
|
getX(batch, d1 + 2, xR, xC),
|
|
getX(batch, d1 + 3, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
|
|
if (${f===1}) {
|
|
|
|
if (${m}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${h}) *
|
|
getW(wR, wC, ${h}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${h}, xR, xC) *
|
|
getW(wR, wC, ${h}, d2);
|
|
}
|
|
|
|
} else if (${f===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec2 xValues = vec2(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${f===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2),
|
|
getW(wR, wC, ${h} + 2, d2)
|
|
);
|
|
|
|
if (${m}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1),
|
|
getX(batch, xR, xC, ${h} + 2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec3 xValues = vec3(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC),
|
|
getX(batch, ${h} + 2, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${w}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}},fne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${r}, ${a}, ${o});
|
|
const ivec3 pads = ivec3(${t}, ${n}, ${s});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d2 = coords.u;
|
|
|
|
ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xFCorner = xFRCCorner.x;
|
|
int xRCorner = xFRCCorner.y;
|
|
int xCCorner = xFRCCorner.z;
|
|
|
|
// Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get
|
|
// y(yF, yR, yC, d2). ? = to be determined. : = across all
|
|
// values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${u}; wF++) {
|
|
int xF = xFCorner + wF * ${i};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${d}; wR++) {
|
|
int xR = xRCorner + wR * ${l};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p}; wC++) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${f===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${h}) *
|
|
getW(wF, wR, wC, ${h}, d2);
|
|
} else if (${f===2}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1)
|
|
);
|
|
vec2 wValues = vec2(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${f===3}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1),
|
|
getX(batch, xF, xR, xC, ${h} + 2)
|
|
);
|
|
vec3 wValues = vec3(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2),
|
|
getW(wF, wR, wC, ${h} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},mne=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"},{name:"pad",type:"ivec2"},{name:"stride",type:"ivec2"},{name:"dilation",type:"ivec2"},{name:"inChannels",type:"int"},{name:"itemsPerBlockRow",type:"int"},{name:"outWidth",type:"int"}],this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let{dataFormat:n}=t,s=Gn(),r=n==="channelsLast",a=r?0:1,o=r?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let c=0;c<=1;c++)for(let u=0;u<=1;u++)l+=`
|
|
blockIndex = rc.y + ${u};
|
|
pos = rc.x + ${c};
|
|
|
|
${i}
|
|
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
|
|
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
|
|
|
|
if(d0 < inputShape[${a}] && d0 >= 0) {
|
|
// Use custom imod instead mod. On Intel GPU, mod may generate
|
|
// unexpected value.
|
|
// https://github.com/tensorflow/tfjs/issues/5447
|
|
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
|
|
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
|
|
inChannels);
|
|
|
|
if(d1 < inputShape[${o}] && d1 >= 0) {
|
|
|
|
ch = imod(pos, inChannels);
|
|
|
|
if (${r}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${c*2+u}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${c*2+u}] = getChannel(
|
|
getA(ch, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
`;this.userCode=`
|
|
void main() {
|
|
ivec2 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0);
|
|
|
|
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
|
|
vec2 innerDims;
|
|
|
|
${l}
|
|
|
|
${s.output} = result;
|
|
}
|
|
`}};function jC({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=s.texData.get(e.dataId),u=n.inChannels,d=l[0]*l[1]*l[2],p=n.outChannels,h=n.dataFormat==="channelsLast",f=!1,m=!1,g,y=[];if(!((d===1||p===1)&&u>PC)&&c.isPacked&&h&&c.texture!=null&&l[2]%2!=0&&v.arraysEqual(c.shape.slice(-3),l.slice(-3))){let b=l[0]*l[1]*(l[2]+1),w={dataId:e.dataId,shape:[1,b,n.inChannels],dtype:e.dtype},C=c.shape;c.shape=c.shape.slice(),c.shape[c.shape.length-2]++,v.assert(kp(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let S=be({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(S);let E=Ym({a:w,b:S,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),R=s.texData.get(E.dataId);v.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=C,R.shape=n.outShape,g=Ss({inputs:{x:E},backend:s}),g.shape=n.outShape,y.push(E)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=be({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),C=be({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),S=Ym({a:w,b:C,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=be({inputs:{x:S},backend:s,attrs:{shape:n.outShape}}),y.push(w),y.push(C),y.push(S)}for(let b of y)s.disposeIntermediateTensorInfo(b);return g}function qC({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=l*c*u,g=p*d,y=[m,g],x=!0,A=!1,b=[],w=be({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),C=be({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(C);let S=new mne(y,n),E=[w.shape,[n.padInfo.top,n.padInfo.left],[n.strideHeight,n.strideWidth],[n.dilationHeight,n.dilationWidth],[n.inChannels],[n.filterWidth*n.inChannels],[n.outWidth]],R=s.runWebGLProgram(S,[w],"float32",E),P=be({inputs:{x:R},backend:s,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(P);let _=r!=null,$=a!=null,T=i==="leakyrelu",O=i?Xm(i,!0):null,V=new RC(P.shape,C.shape,[1,g,n.outChannels],x,A,_,O,$,T),K=[P,C];if(r&&K.push(r),$&&K.push(a),T){let Q=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));K.push(Q),b.push(Q)}let z=s.runWebGLProgram(V,K,"float32"),j=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],W=be({inputs:{x:z},backend:s,attrs:{shape:j}});b.push(z);for(let Q of b)s.disposeIntermediateTensorInfo(Q);return W}function gne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=s,d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d),h;if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))h=jC({x:r,filter:a,convInfo:p,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=qC({x:r,filter:a,convInfo:p,backend:n});else{let m=new HC(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=be({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var yne={kernelName:za,backendName:"webgl",kernelFunc:gne},Ane=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.dataFormat==="channelsLast";this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int wR = coords.x;
|
|
int wC = coords.y;
|
|
int d1 = coords.z;
|
|
int d2 = coords.w;
|
|
|
|
// Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${t} - ${s};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
if (${a}) {
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
} else {
|
|
float dyValue = getDy(b, d2, yR, yC);
|
|
float xValue = getX(b, d1, xR, xC);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},xne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dataFormat==="channelsLast",o=t-1-e.padInfo.top,i=n-1-e.padInfo.left,l=a?1:2,c=a?2:3,u=a?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${o}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${u}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${l}], coords[${c}]) - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
|
|
if (${a}) {
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
} else {
|
|
float xValue = getDy(batch, d2, idyR, idyC);
|
|
float wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},bne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.padInfo.front,a=e.padInfo.top,o=e.padInfo.left;this.userCode=`
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int wF = coords.x;
|
|
int wR = coords.y;
|
|
int wC = coords.z;
|
|
int d1 = coords.w;
|
|
int d2 = coords.u;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yF = 0; yF < ${e.outDepth}; yF++) {
|
|
int xF = wF + yF * ${t} - ${r};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${n} - ${a};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${s} - ${o};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},vne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${i}, ${l}, ${c});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.u;
|
|
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyFCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${t}; wF++) {
|
|
float dyF = float(dyFCorner + wF) / ${r}.0;
|
|
|
|
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyF = int(dyF);
|
|
|
|
int wFPerm = ${t} - 1 - wF;
|
|
|
|
for (int wR = 0; wR < ${n}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${n} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${s}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${o}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${s} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
float xValue = getDy(batch, idyF, idyR, idyC, d2);
|
|
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function wne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,filterShape:u}=s,d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),h=new Ane(p);return n.runWebGLProgram(h,[r,a],"float32")}var kne={kernelName:Dh,backendName:"webgl",kernelFunc:wne};function Sne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=N.convertConv2DDataFormat(c),p=N.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=new xne(p);return n.runWebGLProgram(h,[r,a],"float32")}var Ine={kernelName:La,backendName:"webgl",kernelFunc:Sne};function Cne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=N.computeConv3DInfo(r.shape,a.shape,o,l,i),u=new fne(c);return n.runWebGLProgram(u,[r,a],"float32")}var Tne={kernelName:pd,backendName:"webgl",kernelFunc:Cne};function Nne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,c=N.computeConv3DInfo(r.shape,l,o,1,i),u=new bne(c);return n.runWebGLProgram(u,[r,a],"float32")}var Ene={kernelName:$h,backendName:"webgl",kernelFunc:Nne};function Rne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,c=N.computeConv3DInfo(l,a.shape,i,1,o),u=new vne(c);return n.runWebGLProgram(u,[r,a],"float32")}var _ne={kernelName:Fh,backendName:"webgl",kernelFunc:Rne},Dne=EC+`
|
|
return cos(x);
|
|
`,$ne=at({opSnippet:Dne}),Fne={kernelName:Ba,backendName:"webgl",kernelFunc:$ne},Pne=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,One=at({opSnippet:Pne}),Mne={kernelName:Wa,backendName:"webgl",kernelFunc:One},zne=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[c]=t,[u,d]=n;this.outputShape=[c,u,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,y]=u>1?[`${(o-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,A,b]=d>1?[`${(i-1)/(d-1)}`,"(x2-x1) * width_ratio",`x1*${f} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${f}`];this.userCode=`
|
|
const float height_ratio = float(${m});
|
|
const float width_ratio = float(${x});
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int y = coords[1];
|
|
int x = coords[2];
|
|
int d = coords[3];
|
|
|
|
// get box vals
|
|
float y1 = getBoxes(b,0);
|
|
float x1 = getBoxes(b,1);
|
|
float y2 = getBoxes(b,2);
|
|
float x2 = getBoxes(b,3);
|
|
|
|
// get image in batch index
|
|
int bInd = round(getBoxInd(b));
|
|
if(bInd < 0 || bInd >= ${a}) {
|
|
return;
|
|
}
|
|
|
|
float height_scale = ${g};
|
|
float width_scale = ${A};
|
|
|
|
float in_y = ${y};
|
|
if( in_y < 0.0 || in_y > ${h} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
float in_x = ${b};
|
|
if( in_x < 0.0 || in_x > ${f} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${p} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},Lne=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new zne(r.shape,a.shape,i,l,c);return n.runWebGLProgram(u,[r,a,o],"float32")},Bne={kernelName:xi,backendName:"webgl",kernelFunc:Lne},XC=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${KC(s,"coords")})`,a=e[e.length-1],o="",i="";t?(o=n?`end != ${a-1}`:"end != 0",i=n?"end + 1":"end - 1"):(o=n?`end + pow2 < ${a}`:"end >= pow2",i=n?"end + pow2":"end - pow2"),this.userCode=`
|
|
void main() {
|
|
${vt(s)} coords = getOutputCoords();
|
|
int end = ${ZC(s,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${o}) {
|
|
int idx = ${i};
|
|
${ZC(s,"coords")} = idx;
|
|
val += getX(${KC(s,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}};function KC(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function ZC(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Wne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,c=N.getAxesPermutation([a],l),u=r;c!=null&&(u=jn({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=N.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=Ss({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new XC(u.shape,!1,i),g=[[f]],y=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(o){let f=new XC(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=N.getUndoAxesPermutation(c),m=jn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var Vne={kernelName:Ai,backendName:"webgl",kernelFunc:Wne};function Une(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=hC(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=rQ(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Gne={kernelName:Ph,backendName:"webgl",kernelFunc:Une},Hne=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int h = ${this.getHeightCoordString()};
|
|
int w = ${this.getWidthCoordString()};
|
|
int d = ${this.getDepthCoordString()};
|
|
|
|
int in_h = h / ${t};
|
|
int offset_h = imod(h, ${t});
|
|
int in_w = w / ${t};
|
|
int offset_w = imod(w, ${t});
|
|
int offset_d = (offset_h * ${t} + offset_w) *
|
|
${this.getOutputDepthSize()};
|
|
int in_d = d + offset_d;
|
|
|
|
float result = ${this.getInputSamplingString()};
|
|
setOutput(result);
|
|
}
|
|
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function jne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=new Hne(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var qne={kernelName:bi,backendName:"webgl",kernelFunc:jne},YC=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ds(this.outputShape.length);let a=e.filterHeight,o=e.filterWidth,i=e.outChannels/e.inChannels,l="",c="";n&&(s?l=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?l=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:l=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,c="result = activation(result);");let u=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${l}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${i};
|
|
int q = d2 - d1 * ${i};
|
|
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
// TO DO(dsmilkov): Flatten the two for loops and vec4 the operations.
|
|
for (int wR = 0; wR < ${a}; wR++) {
|
|
int xR = xRCorner + wR * dilations[0];
|
|
|
|
if (xR < 0 || xR >= inDims[0]) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${o}; wC++) {
|
|
int xC = xCCorner + wC * dilations[1];
|
|
|
|
if (xC < 0 || xC >= inDims[1]) {
|
|
continue;
|
|
}
|
|
|
|
float xVal = getX(batch, xR, xC, d1);
|
|
float wVal = getW(wR, wC, d1, q);
|
|
dotProd += xVal * wVal;
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${u}
|
|
${c}
|
|
setOutput(result);
|
|
}
|
|
`}},JC=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ds(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,u=e.filterWidth,d=u,p=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<u;g++)p+=`
|
|
vec4 xTexelC${g*2};
|
|
int xTexelC${g*2}Ready;
|
|
vec4 xTexelC${g*2+1};
|
|
int xTexelC${g*2+1}Ready;
|
|
vec4 xC${g};`;p+=`
|
|
for (int r = 0; r < ${c}; r++) {
|
|
`;for(let g=0;g<u;g++)p+=`
|
|
xTexelC${g*2} = vec4(0.0);
|
|
xTexelC${g*2}Ready = 0;
|
|
xTexelC${g*2+1} = vec4(0.0);
|
|
xTexelC${g*2+1}Ready = 0;
|
|
xC${g} = vec4(0.0);`;p+=`
|
|
xR = xRCorner + r * dilations[0];
|
|
if (xR >=0 && xR < inDims[0]) {
|
|
`;for(let g=0;g<(d+1)/2;g++){let y=g*2;if(p+=`
|
|
xC = xCCorner + ${y*l};
|
|
`,i===1){if(y<u&&(o%2==1?(p+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
`,l===1&&y>0?p+=`
|
|
xC${y} = vec4(xTexelC${y-2}.zw, xTexelC${y}.xy);
|
|
`:p+=`
|
|
xCOffset = xC + 1 - 2;
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
previous.zw = vec2(0.0);
|
|
}
|
|
|
|
xC${y} = vec4(previous.zw, xTexelC${y}.xy);
|
|
} else {
|
|
xC${y} = vec4(0.0, 0.0, xTexelC${y}.xy);
|
|
}
|
|
`):p+=`
|
|
if (xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
xC${y} = xTexelC${y};
|
|
`,y+1<u)){let x=o%2==0?v.nearestLargerEven(l):l;l%2==0&&o%2==1||l%2!=0&&o%2!=1?(p+=`
|
|
xCOffset = xC + imod(pads[1], 2) + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
`,l>1&&(p+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
`),p+=`
|
|
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.xy);
|
|
`):x===1?p+=`
|
|
xC${y+1} = xTexelC${y};
|
|
`:p+=`
|
|
xCOffset = xC + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y+1} = xTexelC${y+1};
|
|
`}}else y<u&&(o%2==1?(p+=`
|
|
xCOffset = xC + 1 - strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
|
|
`,y+1<u&&(p+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1]) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${y+1} = vec4(xTexelC${y+1}.xy, final.xy);
|
|
`)):(p+=`
|
|
if(xC >= 0 && xC < inDims[1] && xTexelC${y}Ready == 0) {
|
|
xTexelC${y} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${y}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${y}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${y+1}Ready == 0) {
|
|
xTexelC${y+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${y+1}.zw = vec2(0.);
|
|
}
|
|
xTexelC${y+1}Ready = 1;
|
|
}
|
|
|
|
xC${y} = vec4(
|
|
xTexelC${y}.xy, xTexelC${y+1}.xy);
|
|
`,y+1<u&&(p+=`
|
|
xC${y+1} = vec4(xTexelC${y}.zw, xTexelC${y+1}.zw);
|
|
`)));y<u&&(p+=`
|
|
wTexel = getW(r, ${y}, d1, q);
|
|
dotProd += xC${y} * vec4(wTexel.xz, wTexel.xz);
|
|
`,y+1<u&&(p+=`
|
|
wTexel = getW(r, ${y+1}, d1, q);
|
|
dotProd += xC${y+1} * vec4(wTexel.xz, wTexel.xz);
|
|
`))}p+=`
|
|
}
|
|
`,p+=`
|
|
}
|
|
`;let h="",f="";n&&(s?h=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?h=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:h=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,f="result = activation(result);");let m=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${h}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${a};
|
|
int q = d2 - d1 * ${a};
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
|
|
vec4 dotProd = vec4(0.000000000000001);
|
|
|
|
${p}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${m}
|
|
${f}
|
|
setOutput(result);
|
|
}
|
|
`}};function Xne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(o,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let d=N.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new JC(d):p=new YC(d);let h=[[d.padInfo.top,d.padInfo.left],[d.strideHeight,d.strideWidth],[d.dilationHeight,d.dilationWidth],[d.inHeight,d.inWidth]];return n.runWebGLProgram(p,[r,a],"float32",h)}var Kne={kernelName:Va,backendName:"webgl",kernelFunc:Xne},Zne=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,s=e.padInfo.top,r=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int wR = coords.x;
|
|
int wC = coords.y;
|
|
int d1 = coords.z;
|
|
int dm = coords.w;
|
|
int d2 = d1 * ${a} + dm;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
// TO DO: Vec4 over the batch size
|
|
for (int b = 0; b < ${e.batchSize}; b++) {
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${t} - ${s};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Yne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${a}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[3];
|
|
ivec2 dyCorner = coords.yz - pads;
|
|
int dyRCorner = dyCorner.x;
|
|
int dyCCorner = dyCorner.y;
|
|
|
|
float dotProd = 0.0;
|
|
|
|
for (int wR = 0; wR < ${t}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
// TO DO: Vec4 over the channelMul
|
|
for (int dm = 0; dm < ${i}; dm++) {
|
|
int d2 = d1 * ${i} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Jne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s,d=N.computeConv2DInfo(r.shape,u,o,i,l,c,!0),p=new Zne(d);return n.runWebGLProgram(p,[r,a],"float32")}var Qne={kernelName:Oh,backendName:"webgl",kernelFunc:Jne};function ese(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s,d=N.computeConv2DInfo(u,a.shape,o,i,l,c,!0),p=new Yne(d);return n.runWebGLProgram(p,[r,a],"float32")}var tse={kernelName:Mh,backendName:"webgl",kernelFunc:ese},nse=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function sse(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=be({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new nse(a),l=n.runWebGLProgram(i,[o],o.dtype),c=be({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var rse={kernelName:zh,backendName:"webgl",kernelFunc:sse},ase=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:c}=e,{top:u,left:d}=s;this.userCode=`
|
|
const ivec2 strides = ivec2(${r}, ${a});
|
|
const ivec2 pads = ivec2(${u}, ${d});
|
|
const float neg_infinity = -3.4e38;
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.w;
|
|
ivec2 outTopLeftCorner =
|
|
coords.yz * strides - pads;
|
|
int hBeg = outTopLeftCorner.x;
|
|
int wBeg = outTopLeftCorner.y;
|
|
|
|
float curVal = neg_infinity;
|
|
for (int h = 0; h < ${o}; h++) {
|
|
int hIn = hBeg + h * ${l};
|
|
|
|
if (hIn >= 0 && hIn < ${t}) {
|
|
for (int w = 0; w < ${i}; w++) {
|
|
int wIn = wBeg + w * ${c};
|
|
|
|
if (wIn >= 0 && wIn < ${n}) {
|
|
float xVal = getX(batch, hIn, wIn, d1);
|
|
float wVal = getW(h, w, d1);
|
|
|
|
float val = xVal + wVal;
|
|
if (val > curVal) {
|
|
curVal = val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = curVal;
|
|
setOutput(result);
|
|
}
|
|
`}};function ose(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=N.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),u,d=new ase(c);u=n.runWebGLProgram(d,[r,a],"float32");let p=be({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var ise={kernelName:hd,backendName:"webgl",kernelFunc:ose};function lse(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=N.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:y,expandDims:x}=N.getEinsumPermutation(h,l[g]),A;N.isIdentityPermutation(y)?A=a[g]:(A=jn({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(A.shape,b)||(A=be({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),p===null?p=A:(p=wx({inputs:{a:A,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Zm({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeIntermediateTensorInfo(m);return p}var use={kernelName:fd,backendName:"webgl",kernelFunc:lse},cse="return (x >= 0.0) ? x : (exp(x) - 1.0);",dse=`
|
|
vec4 result;
|
|
|
|
result.r = (x.r >= 0.0) ? x.r : (exp(x.r) - 1.0);
|
|
result.g = (x.g >= 0.0) ? x.g : (exp(x.g) - 1.0);
|
|
result.b = (x.b >= 0.0) ? x.b : (exp(x.b) - 1.0);
|
|
result.a = (x.a >= 0.0) ? x.a : (exp(x.a) - 1.0);
|
|
|
|
return result;
|
|
`,pse=at({opSnippet:cse,packedOpSnippet:dse}),hse={kernelName:Ga,backendName:"webgl",kernelFunc:pse},fse="return (b >= 1.0) ? a : a * (b + 1.0);",mse=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,gse=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cp(mse,s.shape,r.shape):new mc(fse,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},yse={kernelName:Wh,backendName:"webgl",kernelFunc:gse},Ase=`
|
|
return vec4(equal(a, b));
|
|
`,xse="return float(a == b);",bse=In({opSnippet:xse,packedOpSnippet:Ase,dtype:"bool",cpuKernelImpl:iQ}),vse={kernelName:vi,backendName:"webgl",kernelFunc:bse},wse=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${N.ERF_P};
|
|
float a1 = ${N.ERF_A1};
|
|
float a2 = ${N.ERF_A2};
|
|
float a3 = ${N.ERF_A3};
|
|
float a4 = ${N.ERF_A4};
|
|
float a5 = ${N.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,kse=at({opSnippet:wse}),Sse={kernelName:yu,backendName:"webgl",kernelFunc:kse},QC="return exp(x);",e4=at({opSnippet:QC,packedOpSnippet:QC,cpuKernelImpl:lQ,dtype:"float32"}),Ise={kernelName:Ha,backendName:"webgl",kernelFunc:e4};function Cx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),be({inputs:{x:a},backend:s,attrs:{shape:i}})}var Cse={kernelName:wi,backendName:"webgl",kernelFunc:Cx},t4="return exp(x) - 1.0;",Tse=at({opSnippet:t4,packedOpSnippet:t4,cpuKernelImpl:uQ}),Nse={kernelName:ki,backendName:"webgl",kernelFunc:Tse},n4=class{constructor(e,t,n){this.variableNames=["real","imag"];let s=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=n?`${s}.0`:"1.0",o;if(e==="real")o="return real * expR - imag * expI;";else if(e==="imag")o="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
|
|
const float exponentMultiplier = ${r};
|
|
|
|
float unaryOpComplex(float real, float expR, float imag, float expI) {
|
|
${o}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${s});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${s}; i++) {
|
|
// x = (-2|2 * PI / N) * index * i;
|
|
float x = exponentMultiplierTimesIndexRatio * float(i);
|
|
float expR = cos(x);
|
|
float expI = sin(x);
|
|
float real = getReal(batch, i);
|
|
float imag = getImag(batch, i);
|
|
|
|
result +=
|
|
unaryOpComplex(real, expR, imag, expI) / ${a};
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
setOutput(mulMatDFT(coords[0], coords[1]));
|
|
}
|
|
`}};function s4(e,t,n){let s=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=be({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,c=new n4("real",l,t),u=new n4("imag",l,t),d=[{dataId:s.complexTensorInfos.real.dataId,dtype:s.complexTensorInfos.real.dtype,shape:l},{dataId:s.complexTensorInfos.imag.dataId,dtype:s.complexTensorInfos.imag.dtype,shape:l}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=Zo({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=be({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function Ese(e){let{inputs:t,backend:n}=e,{input:s}=t;return s4(s,!1,n)}var Rse={kernelName:Vh,backendName:"webgl",kernelFunc:Ese},_se=class{constructor(e,t){this.outputShape=[],this.customUniforms=[{name:"value",type:"float"}],this.variableNames=["x"],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
// Input can be obtained from uniform value.
|
|
setOutput(value);
|
|
}
|
|
`}};function Ep(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new _se(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var Dse={kernelName:Au,backendName:"webgl",kernelFunc:Ep},$se=class{constructor(e){this.variableNames=["Image"],this.outputShape=[];let t=e[2];this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
|
|
int coordX = ${t} - x - 1;
|
|
float outputValue;
|
|
if(coordX >= 0 && coordX < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},Fse={kernelName:Si,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new $se(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},r4="return floor(x);",Pse=at({opSnippet:r4,packedOpSnippet:r4,cpuKernelImpl:cQ}),Ose={kernelName:ja,backendName:"webgl",kernelFunc:Pse},Mse=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,zse=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,Lse=In({opSnippet:Mse,packedOpSnippet:zse,dtype:"int32"}),Bse={kernelName:qa,backendName:"webgl",kernelFunc:Lse},Wse=class{constructor(e){this.variableNames=["A"];let t=Gn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},Vse=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Gn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}.0, ${n}.0);
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
result[row * 2 + col] = floor(value * 255.0 + 0.5);
|
|
}
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},Use={kernelName:Cd,backendName:"webgl",kernelFunc:Gse},Ac;function Gse(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,c]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=[c,l],d=[c,l,a];(i||o)&&(Ac==null&&(Ac=document.createElement("canvas").getContext("2d")),Ac.canvas.width=l,Ac.canvas.height=c,Ac.drawImage(r,0,0,l,c),r=Ac.canvas);let p=n.makeTensorInfo(u,"int32");n.texData.get(p.dataId).usage=rr.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=Y().getBool("WEBGL_PACK")?new Vse(d):new Wse(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function Hse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(u),g=N.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),y,x=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=jC({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=qC({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,w=i!=null,C=h==="leakyrelu",S=h?Xm(h,!1):null,E=new HC(g,b,S,w,C),R=[r,a];if(o&&R.push(o),i&&R.push(i),C){let P=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));R.push(P),x.push(P)}y=n.runWebGLProgram(E,R,"float32")}let A=be({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return x.push(y),x.forEach(b=>n.disposeIntermediateTensorInfo(b)),A}var jse={kernelName:Co,backendName:"webgl",kernelFunc:Hse};function qse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p,leakyreluAlpha:h}=s,f=[],m=u;m==null&&(m=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=N.computeConv2DInfo(r.shape,a.shape,l,m,c,d,!0),y=Y().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,x=p?Xm(p,y):null,A=[r,a],b=o!=null,w=i!=null,C=p==="leakyrelu";if(b&&A.push(o),w&&A.push(i),C){let P=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));A.push(P),f.push(P)}let S;y?S=new JC(g,b,x,w,C):S=new YC(g,b,x,w,C);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=n.runWebGLProgram(S,A,"float32",E);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),R}var Xse={kernelName:To,backendName:"webgl",kernelFunc:qse},Kse=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=vt(t.length),r=vt(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${s} strides = ${s}(${this.strides});
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
int flattenIndex = 0;
|
|
for (int j = 0; j < ${this.sliceDim}; j++) {
|
|
int index = round(getIndices(coords[0], j));
|
|
flattenIndex += index * ${a};
|
|
}
|
|
setOutput(getX(flattenIndex, coords[1]));
|
|
}
|
|
`}};function Zse(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=N.prepareAndValidate(s,r),p=be({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=be({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),x=n.bufferSync(s),A=dQ(y,x,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,A.values)}let f=new Kse(o,d,[c,u]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=be({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var Yse={kernelName:Ci,backendName:"webgl",kernelFunc:Zse},Jse=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=vt(this.rank),s=Qse(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function Qse(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push("int(getIndices(resRC.x, resRC.z))"):s.push(`${n[r]}`);return s.join()}function a4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=n.readSync(a.dataId),u=r.shape[l];for(let b=0;b<c.length;++b){let w=c[b];v.assert(w<=u-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${u-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=v.sizeFromShape(a.shape),h=[],f=be({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=be({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,p/d.batchSize]}});h.push(f),h.push(m);let g=[d.batchSize,d.outerSize,p/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let b=n.bufferSync(m),w=n.bufferSync(f),C=pQ(w,b,g);return h.forEach(S=>n.disposeIntermediateTensorInfo(S)),n.makeTensorInfo(d.outputShape,C.dtype,C.values)}let y=new Jse(f.shape,g),x=n.runWebGLProgram(y,[f,m],f.dtype);h.push(x);let A=be({inputs:{x},backend:n,attrs:{shape:d.outputShape}});return h.forEach(b=>n.disposeIntermediateTensorInfo(b)),A}var ere={kernelName:Ii,backendName:"webgl",kernelFunc:a4},tre="return float(a > b);",nre=`
|
|
return vec4(greaterThan(a, b));
|
|
`,sre=In({opSnippet:tre,packedOpSnippet:nre,cpuKernelImpl:hQ,dtype:"bool"}),rre={kernelName:Ti,backendName:"webgl",kernelFunc:sre},are="return float(a >= b);",ore=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,ire=In({opSnippet:are,packedOpSnippet:ore,dtype:"bool",cpuKernelImpl:fQ}),lre={kernelName:Ka,backendName:"webgl",kernelFunc:ire};function ure(e){let{inputs:t,backend:n}=e,{input:s}=t;return s4(s,!0,n)}var cre={kernelName:Uh,backendName:"webgl",kernelFunc:ure},dre="return float(!isnan(x) && !isinf(x));",pre=at({opSnippet:dre,dtype:"bool"}),hre={kernelName:xu,backendName:"webgl",kernelFunc:pre},fre="return float(isinf(x));",mre=at({opSnippet:fre,dtype:"bool"}),gre={kernelName:bu,backendName:"webgl",kernelFunc:mre},yre="return float(isnan(x));",Are=at({opSnippet:yre,dtype:"bool"}),xre={kernelName:vu,backendName:"webgl",kernelFunc:Are},bre="return float(a < b);",vre=`
|
|
return vec4(lessThan(a, b));
|
|
`,wre=In({opSnippet:bre,packedOpSnippet:vre,cpuKernelImpl:mQ,dtype:"bool"}),kre={kernelName:Ni,backendName:"webgl",kernelFunc:wre},Sre="return float(a <= b);",Ire=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,Cre=In({opSnippet:Sre,packedOpSnippet:Ire,cpuKernelImpl:gQ,dtype:"bool"}),Tre={kernelName:Ei,backendName:"webgl",kernelFunc:Cre};function Nre(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=yQ(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var Ere={kernelName:Gh,backendName:"webgl",kernelFunc:Nre},Rre=`if (x < 0.0) return NAN;
|
|
return log(x);`,_re=`
|
|
vec4 result = log(x);
|
|
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
|
|
result.r = isNaN.r == 1.0 ? NAN : result.r;
|
|
result.g = isNaN.g == 1.0 ? NAN : result.g;
|
|
result.b = isNaN.b == 1.0 ? NAN : result.b;
|
|
result.a = isNaN.a == 1.0 ? NAN : result.a;
|
|
|
|
return result;
|
|
`,Dre=at({opSnippet:Rre,packedOpSnippet:_re,cpuKernelImpl:AQ}),$re={kernelName:Ja,backendName:"webgl",kernelFunc:Dre},Fre="return log(1.0 + x);",Pre=at({opSnippet:Fre}),Ore={kernelName:wu,backendName:"webgl",kernelFunc:Pre},Mre="return float(a >= 1.0 && b >= 1.0);",zre=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,Lre=In({opSnippet:Mre,packedOpSnippet:zre,dtype:"bool"}),Bre={kernelName:Ri,backendName:"webgl",kernelFunc:Lre},Wre="return float(!(x >= 1.0));",Vre=at({opSnippet:Wre}),Ure={kernelName:ku,backendName:"webgl",kernelFunc:Vre},Gre="return float(a >= 1.0 || b >= 1.0);",Hre=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,jre=In({opSnippet:Gre,packedOpSnippet:Hre,dtype:"bool"}),qre={kernelName:gd,backendName:"webgl",kernelFunc:jre},Xre=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
int d = coords[3];
|
|
float x = getX(b, r, c, d);
|
|
float sum = 0.0;
|
|
for (int j = -${a}; j <= ${a}; j++) {
|
|
int idx = d + j;
|
|
if (idx >= 0 && idx <= ${o}) {
|
|
float z = getX(b, r, c, idx);
|
|
sum += z * z;
|
|
}
|
|
}
|
|
float val = x * ${i};
|
|
setOutput(val);
|
|
}
|
|
`}},Kre=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${n}) + float(${s}) * sum`;r===.5?i=`inversesqrt(${l})`:r===1?i=`1.0/(${l})`:i=`exp(log(${l}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords.x;
|
|
int r = coords.y;
|
|
int c = coords.z;
|
|
int d = coords.w;
|
|
|
|
bool hasNextCol = d < ${this.outputShape[3]};
|
|
bool hasNextRow = c < ${this.outputShape[2]};
|
|
|
|
vec4 sum = vec4(0.);
|
|
vec4 xFragAtOutputCoords = getX(b, r, c, d);
|
|
|
|
vec4 xAtOutputCoords = vec4(
|
|
getChannel(xFragAtOutputCoords, vec2(c, d)),
|
|
hasNextCol ?
|
|
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
|
|
hasNextRow ?
|
|
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
|
|
);
|
|
|
|
int firstChannel = d - ${a};
|
|
vec2 cache = vec2(0.);
|
|
if(firstChannel >= 0){
|
|
vec4 firstChannelFrag = getX(b, r, c, firstChannel);
|
|
cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));
|
|
if(hasNextRow){
|
|
cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));
|
|
}
|
|
}
|
|
|
|
ivec2 depth = ivec2(d, d + 1);
|
|
for (int j = - ${a}; j <= ${a}; j++) {
|
|
ivec2 idx = depth + j;
|
|
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
|
|
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${o}));
|
|
|
|
bool depthInRange = aboveLowerBound.x && belowUpperBound.x;
|
|
bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;
|
|
|
|
if(depthInRange || depthPlusOneInRange){
|
|
vec4 z = vec4(0.);
|
|
vec4 xFragAtCurrentDepth;
|
|
z.xz = cache.xy;
|
|
if(depthPlusOneInRange && hasNextCol){
|
|
xFragAtCurrentDepth = idx.y != d ?
|
|
getX(b, r, c, idx.y) : xFragAtOutputCoords;
|
|
z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));
|
|
if(hasNextRow){
|
|
z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));
|
|
}
|
|
}
|
|
cache.xy = z.yw;
|
|
sum += z * z;
|
|
}
|
|
}
|
|
vec4 result = xAtOutputCoords * ${i};
|
|
setOutput(result);
|
|
}
|
|
`}},Zre=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,c=Y().getBool("WEBGL_PACK_NORMALIZATION")?new Kre(r.shape,a,o,i,l):new Xre(r.shape,a,o,i,l);return n.runWebGLProgram(c,[r],r.dtype)},Yre={kernelName:yd,backendName:"webgl",kernelFunc:Zre},Jre=class{constructor(e,t,n,s,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=s,this.beta=r,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float result = 0.0;
|
|
for (int d = 0; d < ${this.depth}; ++d) {
|
|
int depthBegin = int(max(0.0, float(d - ${t})));
|
|
int depthEnd = int(min(float(${this.depth}),
|
|
float(d + ${t} + 1)));
|
|
|
|
const int MIN_DEPTH_BEGIN = 0;
|
|
const int MAX_DEPTH_END = ${this.depth};
|
|
|
|
float norm = 0.0;
|
|
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd) {
|
|
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
norm = float(${s}) * norm + float(${n});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${s})
|
|
* float(${r})
|
|
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${r});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}},Qre=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r,y:a,dy:o}=t,{depthRadius:i,bias:l,alpha:c,beta:u}=s,d=new Jre(r.shape,i,l,c,u);return n.runWebGLProgram(d,[r,a,o],r.dtype)},eae={kernelName:Hh,backendName:"webgl",kernelFunc:Qre};function tae(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=$l(i,e.dtype,"max",s),c=be({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}function o4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=u!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let A=n.texData.get(h.dataId).values,b=new Array(i);for(let S=0;S<b.length;S++)b[S]=r.shape[u[S]];let w=vx(A,r.shape,r.dtype,u,b);h=n.makeTensorInfo(b,r.dtype);let C=n.texData.get(h.dataId);C.values=w}else h=Km(r,u,n);c=N.getInnerMostAxes(c.length,i)}N.assertAxesAreInnerMostDims("max",c,i);let[f,m]=N.computeOutAndReduceShapes(h.shape,c),g=f;o&&(g=N.expandShapeToKeepDim(f,l));let y;if(p){let A=n.texData.get(h.dataId).values,b=xQ(A,v.sizeFromShape(m),g,r.dtype);y=n.makeTensorInfo(g,r.dtype);let w=n.texData.get(y.dataId);w.values=b}else y=tae(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),y}var nae={kernelName:Qa,backendName:"webgl",kernelFunc:o4},sae=SC+`
|
|
return max(a, b);
|
|
`,rae=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+qm+`
|
|
return result;
|
|
`,aae=In({opSnippet:sae,packedOpSnippet:rae,cpuKernelImpl:bQ}),oae={kernelName:eo,backendName:"webgl",kernelFunc:aae};function iae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;uc(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(N.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=N.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Ss({inputs:{x:r},backend:n});let d=new Tp(u,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var lae={kernelName:to,backendName:"webgl",kernelFunc:iae};function uae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dataFormat:l,dimRoundingMode:c}=s,u=[1,1,1],d=N.computePool3DInfo(r.shape,a,o,u,i,c,l),p=new kx(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var cae={kernelName:Ad,backendName:"webgl",kernelFunc:uae},dae=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,s=e.dilationHeight,r=e.effectiveFilterHeight,a=e.effectiveFilterWidth,o=r-1-e.padInfo.top,i=a-1-e.padInfo.left,l=r*a-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${o}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${r};
|
|
wR += ${s}) {
|
|
float dyR = float(dyRCorner + wR) / ${t}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${a}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${n}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
int maxPosValue = ${l} - int(getMaxPos(b, idyR, idyC, d));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue = wR * ${a} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},pae=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,h=i*l*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${u}, ${d}, ${p});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyDCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
// Convolve dy(?, ?, ?, ch) with pos mask(:, :, :, d) to get
|
|
// dx(xD, xR, xC, ch).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
|
|
for (int wD = 0; wD < ${i};
|
|
wD += ${r}) {
|
|
float dyD = float(dyDCorner + wD) / ${t}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${l};
|
|
wR += ${a}) {
|
|
float dyR = float(dyRCorner + wR) / ${n}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${c};
|
|
wC += ${o}) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
int maxPosValue = ${h} -
|
|
int(getMaxPos(batch, idyD, idyR, idyC, ch));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue =
|
|
wD * ${l} * ${c} +
|
|
wR * ${c} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function hae(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a,{filterSize:i,strides:l,pad:c,dimRoundingMode:u}=s,d=[1,1,1],p=N.computePool3DInfo(o.shape,i,l,d,c,u),h=new kx(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new pae(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var fae={kernelName:qh,backendName:"webgl",kernelFunc:hae};function mae(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;uc([a,o],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:d}=s,p=N.computePool2DInfo(i.shape,l,c,1,u,d),h=!0,f=new Tp(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new dae(p),y=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var gae={kernelName:jh,backendName:"webgl",kernelFunc:mae};function yae(e,t,n,s){let r=new Tp(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new Tp(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var Aae={kernelName:Xh,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{filterSize:r,strides:a,pad:o,includeBatchInIndex:i}=t,l=n;v.assert(s.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${s.shape.length}.`);let c=[1,1];v.assert(N.eitherStridesOrDilationsAreOne(a,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let u=N.computePool2DInfo(s.shape,r,a,c,o),[d,p]=yae(s,i,u,l);return[d,p]}};function xae(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=$l(i,"float32","mean",s),c=be({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}var bae={kernelName:no,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{keepDims:r,axis:a}=t,o=n,i=s.shape.length,l=v.parseAxisParam(a,s.shape),c=l,u=N.getAxesPermutation(c,i),d=u!=null,p=o.shouldExecuteOnCPU([s]),h=[],f=s;if(d){if(p){let b=o.texData.get(f.dataId).values,w=new Array(i);for(let E=0;E<w.length;E++)w[E]=s.shape[u[E]];let C=vx(b,s.shape,s.dtype,u,w);f=o.makeTensorInfo(w,s.dtype);let S=o.texData.get(f.dataId);S.values=C}else f=Km(s,u,o);h.push(f),c=N.getInnerMostAxes(c.length,i)}N.assertAxesAreInnerMostDims("sum",c,i);let[m,g]=N.computeOutAndReduceShapes(f.shape,c),y=m;r&&(y=N.expandShapeToKeepDim(m,l));let x=xae(f,g,y,o);for(let A of h)o.disposeIntermediateTensorInfo(A);return x}};function vae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,r.shape.length)),N.assertAxesAreInnerMostDims("min",c,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=$l(m,m.dtype,"min",n),y;if(o){let x=N.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var wae={kernelName:so,backendName:"webgl",kernelFunc:vae},kae=SC+`
|
|
return min(a, b);
|
|
`,Sae=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+qm+`
|
|
return result;
|
|
`,Iae=In({opSnippet:kae,packedOpSnippet:Sae,cpuKernelImpl:vQ}),Cae={kernelName:ro,backendName:"webgl",kernelFunc:Iae},Tae=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((c,u)=>c[0]+e[u]+c[1]);let s=e.length,r=vt(s),a=t.map(c=>c[0]).join(","),o=t.map((c,u)=>c[0]+e[u]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s),l=n==="reflect"?0:1;if(s===1){this.userCode=`
|
|
int start = ${a};
|
|
int end = ${o};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start) {
|
|
outC = start * 2 - outC - ${l};
|
|
} else if(outC >= end) {
|
|
outC = (end - 1) * 2 - outC + ${l};
|
|
}
|
|
setOutput(getX(outC - start));
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${a});
|
|
${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
for (int i = 0; i < ${s}; i++) {
|
|
if (outC[i] < start[i]) {
|
|
outC[i] = start[i] * 2 - outC[i] - ${l};
|
|
} else if(outC[i] >= end[i]) {
|
|
outC[i] = (end[i] - 1) * 2 - outC[i] + ${l};
|
|
}
|
|
}
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}},Nae=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,f)=>h[0]+e[f]+h[1]);let s=e.length,r=vt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=Hn("rc",s),l=Hn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(s===1){let h=`
|
|
${r} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${d};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${d};
|
|
}
|
|
source -= start;
|
|
`;p=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${u});
|
|
${i[s-1]} += 1;
|
|
if(${c}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`}else{let h=`
|
|
${r} source = rc;
|
|
${r} lt = ${r}(lessThan(source, start));
|
|
${r} gte = ${r}(greaterThanEqual(source, end));
|
|
${r} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${d}) +
|
|
gte * ((end - 1) * 2 - source + ${d});
|
|
source -= start;
|
|
`;p=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${u});
|
|
${i[s-1]} += 1;
|
|
if(${c}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
rc = outputLoc;
|
|
${i[s-2]} += 1;
|
|
if(${i[s-2]} < ${this.outputShape[s-2]}) {
|
|
${h}
|
|
result[2] = getChannel(getX(${l.join()}), ${u});
|
|
${i[s-1]} += 1;
|
|
if(${c}) {
|
|
${h}
|
|
result[3] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${r} start = ${r}(${a});
|
|
const ${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${p}
|
|
setOutput(result);
|
|
}
|
|
`}},Eae=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Nae(s.shape,r,a):new Tae(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},Rae={kernelName:ao,backendName:"webgl",kernelFunc:Eae},_ae=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,Dae=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+qm+`
|
|
return result;
|
|
`,$ae=In({opSnippet:_ae,packedOpSnippet:Dae}),Fae={kernelName:Su,backendName:"webgl",kernelFunc:$ae},Pae=class{constructor(e,t,n){this.variableNames=["probs"],this.customUniforms=[{name:"seed",type:"float"}],this.outputShape=[e,n],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
|
|
float r = random(seed);
|
|
float cdf = 0.0;
|
|
|
|
for (int i = 0; i < ${t-1}; i++) {
|
|
cdf += getProbs(batch, i);
|
|
|
|
if (r < cdf) {
|
|
setOutput(float(i));
|
|
return;
|
|
}
|
|
}
|
|
|
|
// If no other event happened, last event happened.
|
|
setOutput(float(${t-1}));
|
|
}
|
|
`}},Oae=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,Mae=`
|
|
// vec4 one = vec4(equal(a, b));
|
|
// return one + (vec4(1.0) - one) * a / b;
|
|
vec4 result = a / b;
|
|
if(a.x == b.x) {
|
|
result.x = 1.;
|
|
}
|
|
if(a.y == b.y) {
|
|
result.y = 1.;
|
|
}
|
|
if(a.z == b.z) {
|
|
result.z = 1.;
|
|
}
|
|
if(a.w == b.w) {
|
|
result.w = 1.;
|
|
}
|
|
|
|
return result;
|
|
`,i4=In({opSnippet:Oae,packedOpSnippet:Mae,checkOutOfBounds:!0}),zae={kernelName:Ua,backendName:"webgl",kernelFunc:i4},l4="return a - b;",u4=In({opSnippet:l4,packedOpSnippet:l4,supportsComplex:!0,cpuKernelImpl:MQ}),Lae={kernelName:vo,backendName:"webgl",kernelFunc:u4};function c4(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=o4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,o),c=be({inputs:{x:i},backend:n,attrs:{shape:l}}),u=u4({inputs:{a:r,b:c},backend:n}),d=e4({inputs:{x:u},backend:n}),p=Zm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=be({inputs:{x:p},backend:n,attrs:{shape:l}}),f=i4({inputs:{a:d,b:h},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(u),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}var Bae={kernelName:xo,backendName:"webgl",kernelFunc:c4};function Wae(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:c4({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],d=new Pae(c,u,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var Vae={kernelName:Kh,backendName:"webgl",kernelFunc:Wae},d4="return -x;";function Uae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=kQ(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new fc(s.shape,d4):r=new Ko(s.shape,d4),n.runWebGLProgram(r,[s],s.dtype)}var Gae={kernelName:_i,backendName:"webgl",kernelFunc:Uae},Hae=er.nonMaxSuppressionV3Impl;function jae(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=Hae(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var qae={kernelName:$i,backendName:"webgl",kernelFunc:jae},Xae=er.nonMaxSuppressionV4Impl;function Kae(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),{selectedIndices:p,validOutputs:h}=Xae(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Zae={kernelName:Iu,backendName:"webgl",kernelFunc:Kae},Yae=er.nonMaxSuppressionV5Impl;function Jae(e){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:y}=Yae(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Qae={kernelName:Fi,backendName:"webgl",kernelFunc:Jae},eoe=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${s}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},toe=e=>{let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=v.sizeFromShape(r.shape),c=new eoe(l,a,o,i),u=be({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let p=[...r.shape,a],h=be({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},noe={kernelName:Oi,backendName:"webgl",kernelFunc:toe};function t0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Np({inputs:{input:s},backend:n}),a=t0({inputs:{x:r},backend:n}),o=e0({inputs:{input:s},backend:n}),i=t0({inputs:{x:o},backend:n}),l=Zo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Ep({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var soe={kernelName:Qi,backendName:"webgl",kernelFunc:t0};function p4(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Np({inputs:{input:s},backend:n}),a=p4({inputs:{x:r},backend:n}),o=e0({inputs:{input:s},backend:n}),i=t0({inputs:{x:o},backend:n}),l=Zo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Ep({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var roe={kernelName:Pi,backendName:"webgl",kernelFunc:p4};function aoe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Cx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=Cx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=GC({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var ooe={kernelName:Mi,backendName:"webgl",kernelFunc:aoe},ioe=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((l,c)=>l[0]+e[c]+l[1]);let s=e.length,r=vt(s),a=t.map(l=>l[0]).join(","),o=t.map((l,c)=>l[0]+e[c]).join(","),i=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,s);if(s===1){this.userCode=`
|
|
int start = ${a};
|
|
int end = ${o};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${a});
|
|
${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${i}));
|
|
}
|
|
}
|
|
`}},loe=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"value",type:"float"}],this.outputShape=t.map((f,m)=>f[0]+e[m]+f[1]);let s=e.length,r=vt(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Hn("rc",s),l=Hn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
|
|
if(${c}) {
|
|
`,s===1?"":`}
|
|
rc = outputLoc;
|
|
${i[s-2]} += 1;
|
|
if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1;
|
|
if(${c}) {`],p=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
|
|
${d[f]}
|
|
if (${p}) {
|
|
result[${f}] = float(value);
|
|
} else {
|
|
${r} source = rc - start;
|
|
result[${f}] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`;h+=s===1?"} ":"}}",this.userCode=`
|
|
const ${r} start = ${r}(${a});
|
|
const ${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}},h4=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return Ep({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new loe(r.shape,a,o):new ioe(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},uoe={kernelName:io,backendName:"webgl",kernelFunc:h4},coe=`
|
|
if(a < 0.0 && floor(b) < b){
|
|
return NAN;
|
|
}
|
|
if (b == 0.0) {
|
|
return 1.0;
|
|
}
|
|
return (round(mod(b, 2.0)) != 1) ?
|
|
pow(abs(a), b) : sign(a) * pow(abs(a), b);
|
|
`,doe=`
|
|
// isModRound1 has 1 for components with round(mod(b, 2.0)) == 1, 0 otherwise.
|
|
vec4 isModRound1 = vec4(equal(round(mod(b, 2.0)), ivec4(1)));
|
|
vec4 multiplier = sign(a) * isModRound1 + (vec4(1.0) - isModRound1);
|
|
vec4 result = multiplier * pow(abs(a), b);
|
|
|
|
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
|
|
bvec4 isExpZero = equal(b, vec4(0.0));
|
|
result.r = isExpZero.r ? 1.0 : result.r;
|
|
result.g = isExpZero.g ? 1.0 : result.g;
|
|
result.b = isExpZero.b ? 1.0 : result.b;
|
|
result.a = isExpZero.a ? 1.0 : result.a;
|
|
|
|
vec4 isNaN = vec4(lessThan(a, vec4(0.0))) * vec4(lessThan(floor(b), b));
|
|
`+qm+`
|
|
return result;
|
|
`,poe=In({opSnippet:coe,packedOpSnippet:doe}),hoe={kernelName:lo,backendName:"webgl",kernelFunc:poe};function foe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=[],c=v.parseAxisParam(a,r.shape),u=c,d=N.getAxesPermutation(u,i),p=r;d!=null&&(p=jn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=N.getInnerMostAxes(u.length,i),l.push(p)),N.assertAxesAreInnerMostDims("prod",u,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:y}=IQ(p.shape,p.dtype,f,u);h=n.makeTensorInfo(g,y,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,u),g=v.sizeFromShape(m),y=be({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),x=Pd(r.dtype),A=$l(y,x,"prod",n);h=be({inputs:{x:A},backend:n,attrs:{shape:f}}),l.push(y),l.push(A)}if(o){l.push(h);let f=N.expandShapeToKeepDim(h.shape,c);h=be({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var moe={kernelName:zi,backendName:"webgl",kernelFunc:foe},f4=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=CQ(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},goe={kernelName:Cu,backendName:"webgl",kernelFunc:f4},yoe="return 1.0 / x;",Aoe=at({opSnippet:yoe}),xoe={kernelName:Tu,backendName:"webgl",kernelFunc:Aoe},boe=kr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,voe=`
|
|
vec4 result = x * vec4(greaterThanEqual(x, vec4(0.0)));
|
|
bvec4 isNaN = isnan(x);
|
|
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`,woe=at({opSnippet:boe,packedOpSnippet:voe}),koe={kernelName:co,backendName:"webgl",kernelFunc:woe},Soe=kr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Ioe=`
|
|
vec4 result = min(x, vec4(6.)) * vec4(greaterThanEqual(x, vec4(0.0)));
|
|
bvec4 isNaN = isnan(x);
|
|
|
|
result.r = isNaN.r ? x.r : result.r;
|
|
result.g = isNaN.g ? x.g : result.g;
|
|
result.b = isNaN.b ? x.b : result.b;
|
|
result.a = isNaN.a ? x.a : result.a;
|
|
|
|
return result;
|
|
`,Coe=at({opSnippet:Soe,packedOpSnippet:Ioe}),Toe={kernelName:ho,backendName:"webgl",kernelFunc:Coe},Noe=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":d="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]});
|
|
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${d};
|
|
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
|
|
ivec2 sourceCeilRC = ivec2(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
|
|
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
|
|
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
|
|
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
|
|
|
|
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracRC.y;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
|
|
float newValue = top + (bottom - top) * fracRC.x;
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},Eoe=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d;r?d="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":d="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]},
|
|
${c[1]/u[1]});
|
|
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
|
|
${i}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${d};
|
|
|
|
// Compute the four integer indices.
|
|
ivec3 sourceFloorRC = ivec3(max(sourceFracIndexRC, vec3(0.0)));
|
|
ivec3 sourceCeilRC = ivec3(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
// In parallel, construct four corners for all four components in
|
|
// packed 2x2 cell.
|
|
vec4 topLeft = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomLeft = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 topRight = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomRight = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
|
|
|
|
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
|
|
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
|
|
vec4 newValue = mix(top, bottom, fracRC.x);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function Roe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Eoe(r.shape,l,c,a,o):new Noe(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],"float32")}var _oe={kernelName:po,backendName:"webgl",kernelFunc:Roe},Doe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${c});
|
|
const float widthScale = float(${u});
|
|
|
|
const float invHeightScale = float(${d});
|
|
const float invWidthScale = float(${p});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${f});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(startRLerp - float(winHeight / 2));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(startCLerp - float(winWidth / 2));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${a}) {
|
|
continue;
|
|
}
|
|
|
|
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
|
|
int dyC = dyCOffset + startDyC;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyC < 0 || dyC >= ${o}) {
|
|
continue;
|
|
}
|
|
|
|
float dxR = float(dyR) * heightScale;
|
|
int topDxRIndex = int(floor(dxR));
|
|
int bottomDxRIndex = int(min(ceil(dxR), ${s-1}.0));
|
|
float dxRLerp = dxR - float(topDxRIndex);
|
|
float inverseDxRLerp = 1.0 - dxRLerp;
|
|
|
|
float dxC = float(dyC) * widthScale;
|
|
int leftDxCIndex = int(floor(dxC));
|
|
int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
|
|
float dxCLerp = dxC - float(leftDxCIndex);
|
|
float inverseDxCLerp = 1.0 - dxCLerp;
|
|
|
|
if (r == topDxRIndex && c == leftDxCIndex) {
|
|
// topLeft
|
|
accumulator +=
|
|
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == topDxRIndex && c == rightDxCIndex) {
|
|
// topRight
|
|
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == leftDxCIndex) {
|
|
// bottomLeft
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == rightDxCIndex) {
|
|
// bottomRight
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function $oe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Doe(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Foe={kernelName:Yh,backendName:"webgl",kernelFunc:$oe},Poe=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]});
|
|
const vec2 inputShapeRC = vec2(${o}.0, ${i}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${p};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},Ooe=class{constructor(e,t,n,s,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,o,i,l]=e;this.outputShape=[a,t,n,l];let c=[s&&t>1?o-1:o,s&&n>1?i-1:i],u=[s&&t>1?t-1:t,s&&n>1?n-1:n],d=s?"0.5":"0.0",p;r?p="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${c[0]/u[0]},
|
|
${c[1]/u[1]},
|
|
${c[1]/u[1]});
|
|
const vec3 inputShapeRC = vec3(${o}.0, ${i}.0,
|
|
${i}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${p};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec3 sourceNearestRC = ivec3(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${d})));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${l-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function Moe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Ooe(r.shape,l,c,a,o):new Poe(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],r.dtype)}var zoe={kernelName:Nu,backendName:"webgl",kernelFunc:Moe},Loe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${c});
|
|
const float widthScale = float(${u});
|
|
|
|
const float invHeightScale = float(${d});
|
|
const float invWidthScale = float(${p});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${f});
|
|
|
|
// Compute bounds for where in dy we will look
|
|
float startRLerp = floor(float(r) * invHeightScale);
|
|
int startDyR = int(floor(startRLerp - float(winHeight / 2)));
|
|
|
|
float startCLerp = floor(float(c) * invWidthScale);
|
|
int startDyC = int(floor(startCLerp - float(winWidth / 2)));
|
|
|
|
// Loop over dy
|
|
for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {
|
|
int dyR = dyROffset + startDyR;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyR < 0 || dyR >= ${a}) {
|
|
continue;
|
|
}
|
|
|
|
for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {
|
|
int dyC = dyCOffset + startDyC;
|
|
|
|
// Guard against the window exceeding the bounds of dy
|
|
if (dyC < 0 || dyC >= ${o}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${i[0]}) *
|
|
(float(dyR) / float(${l[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${i[1]}) *
|
|
(float(dyC) / float(${l[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${s}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${r}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function Boe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Loe(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Woe={kernelName:Zh,backendName:"webgl",kernelFunc:Boe},Voe=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let s=o=>t.indexOf(o)!==-1&&e[o]!==1?`${e[o]} - coords[${o}] - 1`:`coords[${o}]`,r=e.map((o,i)=>s(i)).join(","),a=vt(n);this.userCode=`
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${r}));
|
|
}
|
|
`}},Uoe=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=Hn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=vt(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${e[0]} - rc - 1),
|
|
${e[0]} - rc - 1);
|
|
if(${r}){
|
|
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
|
|
${e[0]} - (rc + 1) - 1);
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`:this.userCode=`
|
|
void main() {
|
|
${o} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${i(s.slice())};
|
|
if(${r}){
|
|
result.g = ${l(s.slice())};
|
|
}
|
|
if(${a}) {
|
|
result.b = ${c(s.slice())};
|
|
if(${r}) {
|
|
result.a = ${u(s.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((y,x)=>p(x,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function Goe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dims:a}=s,o=r.shape.length,i=v.parseAxisParam(a,r.shape);if(o===0)return Ss({inputs:{x:r},backend:n});let l=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Uoe(r.shape,i):new Voe(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var Hoe={kernelName:Bi,backendName:"webgl",kernelFunc:Goe},joe=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[],this.customUniforms=[{name:"params",type:"vec4"}];let n=e[1],s=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
|
|
vec3 fill = vec3(${t.join(",")});
|
|
float outputValue = fill[coords[3]];`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
float coordXFloat = (float(x) - params[0]) * params[3] -
|
|
(float(y) - params[1]) * params[2];
|
|
float coordYFloat = (float(x) - params[0]) * params[2] +
|
|
(float(y) - params[1]) * params[3];
|
|
int coordX = int(round(coordXFloat + params[0]));
|
|
int coordY = int(round(coordYFloat + params[1]));
|
|
${r}
|
|
if(coordX >= 0 && coordX < ${s} && coordY >= 0 && coordY < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},qoe={kernelName:el,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new joe(s.shape,a),[c,u]=N.getImageCenter(o,s.shape[1],s.shape[2]),d=[[c,u,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},Xoe=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,Koe=at({opSnippet:Xoe}),Zoe={kernelName:Wi,backendName:"webgl",kernelFunc:Koe},Yoe="return inversesqrt(x);",Joe=at({opSnippet:Yoe,cpuKernelImpl:TQ}),Qoe={kernelName:fo,backendName:"webgl",kernelFunc:Joe},m4=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=vt(r.length),l=vt(a.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${c})`,d="";s===1?d="i":s===2&&(d="i, coords[1]");let p=`getUpdates(${d})`,h=t>1?"strides[j]":"strides";this.userCode=`
|
|
${i} strides = ${i}(${r});
|
|
|
|
void main() {
|
|
${l} coords = getOutputCoords();
|
|
float sum = 0.0;
|
|
bool found = false;
|
|
for (int i = 0; i < ${e}; i++) {
|
|
int flattenedIndex = 0;
|
|
for (int j = 0; j < ${t}; j++) {
|
|
int index = round(${u});
|
|
flattenedIndex += index * ${h};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${p};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function eie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=N.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=be({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=be({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new m4(l,i,h.shape.length,f.shape.length,u,p),y=n.runWebGLProgram(g,[f,h,m],f.dtype),x=be({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),x}var tie={kernelName:Vi,backendName:"webgl",kernelFunc:eie},nie=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let s,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",s="resRC";else{let o=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],l=[];for(let c=0;c<t.length;c++)l.push(`${o[c]}`),c<e&&i.push(`${o[c]}`);s=i.join(),r=l.join()}let a=vt(n);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
float cVal = getC(${s});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${r}));
|
|
} else {
|
|
setOutput(getB(${r}));
|
|
}
|
|
}
|
|
`}};function sie(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new nie(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Ln(r.dtype,a.dtype))}var rie={kernelName:Ui,backendName:"webgl",kernelFunc:sie},aie=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${N.SELU_SCALEALPHA};
|
|
float scale = ${N.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,oie=at({opSnippet:aie}),iie={kernelName:Eu,backendName:"webgl",kernelFunc:oie},g4="return 1.0 / (1.0 + exp(-1.0 * x));",lie=at({opSnippet:g4,packedOpSnippet:g4,cpuKernelImpl:NQ}),uie={kernelName:go,backendName:"webgl",kernelFunc:lie},cie=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,die=at({opSnippet:cie}),pie={kernelName:Ru,backendName:"webgl",kernelFunc:die},hie=EC+`
|
|
return sin(x);
|
|
`,fie=at({opSnippet:hie}),mie={kernelName:mo,backendName:"webgl",kernelFunc:fie},gie=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,yie=at({opSnippet:gie}),Aie={kernelName:Hi,backendName:"webgl",kernelFunc:yie},xie=`
|
|
float epsilon = 1.1920928955078125e-7;
|
|
float threshold = log(epsilon) + 2.0;
|
|
|
|
bool too_large = x > -threshold;
|
|
bool too_small = x < threshold;
|
|
|
|
float result;
|
|
float exp_x = exp(x);
|
|
|
|
if (too_large){
|
|
result = x;
|
|
}
|
|
else if (too_small){
|
|
result = exp_x;
|
|
}
|
|
else{
|
|
result = log(exp_x + 1.0);
|
|
}
|
|
return result;
|
|
`,bie=at({opSnippet:xie}),vie={kernelName:_u,backendName:"webgl",kernelFunc:bie},wie=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<r.shape.length;++y)l.push([0,0]);let c=[],u=h4({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,a,i,!1),p=N.getPermuted(d.length,a.length,!1),h=N.getReshapedPermuted(u.shape,a,i,!1),f=be({inputs:{x:u},backend:n,attrs:{shape:d}}),m=jn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=be({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},kie={kernelName:ji,backendName:"webgl",kernelFunc:wie};function Sie(e){let{inputs:t,backend:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=t;if(a.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
|
|
${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[d,p,h,f,m]=RQ(i,s.shape,s.dtype,l,r.dtype,c,u);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var Iie={kernelName:bd,backendName:"webgl",kernelFunc:Sie};function Cie(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[c,u,d]=_Q(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Tie={kernelName:Du,backendName:"webgl",kernelFunc:Cie};function Nie(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=mC(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var Eie={kernelName:vd,backendName:"webgl",kernelFunc:Nie};function Rie(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=mC(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var _ie={kernelName:wd,backendName:"webgl",kernelFunc:Rie};function Die(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=N.calculateShapes(a,r,i),p=!1,h=new m4(c,l,r.shape.length,a.shape.length,u,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=be({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var $ie={kernelName:kd,backendName:"webgl",kernelFunc:Die};function Fie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=N.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=gc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var Pie={kernelName:qi,backendName:"webgl",kernelFunc:Fie},y4="return sqrt(x);",Oie=at({opSnippet:y4,packedOpSnippet:y4,cpuKernelImpl:DQ}),Mie={kernelName:yo,backendName:"webgl",kernelFunc:Oie},zie="return x * x;",Lie=at({opSnippet:zie}),Bie={kernelName:$u,backendName:"webgl",kernelFunc:Lie},A4="return (a - b) * (a - b);",Wie=In({opSnippet:A4,packedOpSnippet:A4}),Vie={kernelName:bo,backendName:"webgl",kernelFunc:Wie};function Uie({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=kr+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,a=new Ko(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var Gie={kernelName:So,backendName:"webgl",kernelFunc:Uie},Hie=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=vt(n.length),a=vt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,c)=>(i++,n.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${i-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
|
|
${r} begin = ${r}(${e});
|
|
${r} strides = ${r}(${t});
|
|
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}};function jie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Mt.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=be({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let S=Mt.computeOutShape(x,A,b),E=gc({inputs:{x:r},backend:n,attrs:{begin:x,size:S}});w=be({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),R=ze(r.shape,r.dtype,E),P=$Q(h,R,b,x);w=n.makeTensorInfo(f,r.dtype,P.values)}else{let E=new Hie(x,b,h);w=n.runWebGLProgram(E,[r],r.dtype)}let C=be({inputs:{x:w},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(w),C}var qie={kernelName:Xi,backendName:"webgl",kernelFunc:jie};function Xie(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=FQ(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Kie={kernelName:Sd,backendName:"webgl",kernelFunc:Xie};function Zie(e){let{inputs:t,backend:n,attrs:s}=e,{skipEmpty:r}=s,{input:a,delimiter:o}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(a.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${a.shape}`);if(o.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${o.shape}`);let i=n.readSync(a.dataId),l=n.readSync(o.dataId)[0],[c,u,d]=PQ(i,l,r),p=u.length;return[n.makeTensorInfo([p,2],"int32",c),n.makeTensorInfo([p],"string",u),n.makeTensorInfo([2],"int32",new Int32Array(d))]}var Yie={kernelName:Jh,backendName:"webgl",kernelFunc:Zie};function Jie(e){let{inputs:t,backend:n,attrs:s}=e,{numBuckets:r}=s,{input:a}=t;if(a.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let o=n.readSync(a.dataId),i=OQ(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var Qie={kernelName:Qh,backendName:"webgl",kernelFunc:Jie},ele="return tan(x);",tle=at({opSnippet:ele}),nle={kernelName:Ki,backendName:"webgl",kernelFunc:tle},sle=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,rle=at({opSnippet:sle}),ale={kernelName:wo,backendName:"webgl",kernelFunc:rle},ole=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[a]*t[a];this.outputShape=n,this.rank=n.length;let s=vt(this.rank),r=ile(e);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function ile(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],s=[];for(let r=0;r<e.length;r++)s.push(`imod(${n[r]}, ${e[r]})`);return s.join()}function x4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(r.dtype==="string"||r.shape.length>5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=ze(r.shape,r.dtype,c),d=zQ(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new ole(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var lle={kernelName:Yr,backendName:"webgl",kernelFunc:x4},ule=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"negativeInf",type:"float"},{name:"dir",type:"int"},{name:"inc",type:"int"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int elemIdx = coords[1];
|
|
|
|
// We compare elements pair-wise within a group of size 2 * inc.
|
|
// The comparing rule for each group alternates between ascending
|
|
// and descending. Within each group, we compare each pair at
|
|
// positions i and i+inc. To decide whether an element at position i
|
|
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
|
|
// inc, it is in the first half of the group, we denote it as x0,
|
|
// otherwise we denote it as x1.
|
|
// For example, as shown in the Bitonic top K paper referenced above,
|
|
// Figure5(a) shows that element[1] is in the
|
|
// second half of the group when group size is 2, but it is in the
|
|
// first half of the group when group size is 4.
|
|
|
|
bool isFirstInPair = imod(elemIdx, 2 * inc) < inc;
|
|
int i = isFirstInPair ? elemIdx : elemIdx - inc;
|
|
|
|
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
|
|
int i1 = firstPass == 1 ? i + inc : int(getIndices(batch, i + inc));
|
|
float x0 = i0 < n ? getX(batch, i0) : negativeInf;
|
|
float x1 = i1 < n ? getX(batch, i1) : negativeInf;
|
|
|
|
// Denotes which direction indices are in (ascending or descending).
|
|
bool reverse = imod(elemIdx, 2 * dir) >= dir;
|
|
bool isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
|
|
if (reverse == isGreater) { // Elements in opposite order of direction
|
|
int iTemp = i0;
|
|
i0 = i1;
|
|
i1 = iTemp;
|
|
}
|
|
if (isFirstInPair) {
|
|
setOutput(float(i0));
|
|
} else {
|
|
setOutput(float(i1));
|
|
}
|
|
}
|
|
`}},cle=class{constructor(e){this.variableNames=["x","indices"],this.customUniforms=[{name:"n",type:"int"},{name:"firstPass",type:"int"},{name:"k",type:"int"}],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
// Takes max of indices (0, k), (1, k + 1), (2, k + 2) ...
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int elemIdx = coords[1];
|
|
|
|
// The output size is half of the previous size.
|
|
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _ (k=4),
|
|
// we only need to output the indices at positions |, the indices at
|
|
// positions _ can be thrown away, see Figure5(b) After Phase 2
|
|
// (Merge phase) in the Bitonic Top K paper referenced above.
|
|
// For example, the paper shows we only need to output the orange bars.
|
|
// The output sequence should look like this | | | | | | | |.
|
|
// Because the sequence is halved, to map the output index back
|
|
// to the previous sequence to find the corresponding value,
|
|
// we need to double the index. When we double the index,
|
|
// we basically interpolate a position, so 2i looks like
|
|
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k position
|
|
// of each 2k positions by - elemIdx % k. E.g. for output at
|
|
// index 4,5,6,7, we want to get the corresponding element at
|
|
// original index 8,9,10,11, for output at index 8,9,10,11,
|
|
// we want to get the corresponding element at original index
|
|
// 16,17,18,19, so on and so forth.
|
|
|
|
int i = elemIdx < k ? elemIdx : (elemIdx * 2 - imod(elemIdx, k));
|
|
int i0 = firstPass == 1 ? i : int(getIndices(batch, i));
|
|
int i1 = firstPass == 1 ? i + k : int(getIndices(batch, i + k));
|
|
|
|
float x0 = getX(batch, i0);
|
|
float x1 = i1 < n ? getX(batch, i1) : x0;
|
|
|
|
setOutput(x0 >= x1 ? float(i0) : float(i1));
|
|
}
|
|
`}};function Fl(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function b4(e){let t=1;for(;t<e;)t*=2;return t}function dle(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),c=r.shape,u=c[c.length-1];if(n.shouldExecuteOnCPU([r])||u<i||a>l){let P=n.readSync(r.dataId),[_,$]=LQ(P,c,r.dtype,a,o);return[n.makeTensorInfo(_.shape,_.dtype,_.values),n.makeTensorInfo($.shape,$.dtype,$.values)]}if(a===0)return c[c.length-1]=0,[n.makeTensorInfo(c,r.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(u===1)return[r,Ep({attrs:{shape:c,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,m=v.sizeFromShape(c)/u,g=be({inputs:{x:h},attrs:{shape:[m,u]},backend:n});p&&Fl(n,h);let y=b4(a),x=b4(u),A=null,b=()=>A===null?[g,g]:[g,A],w=(P,_,$)=>{let T=b(),O=new ule($),K=[[u],[A===null?1:0],[Number.NEGATIVE_INFINITY],[P],[_]],z=A;A=n.runWebGLProgram(O,T,"int32",K),Fl(n,z)};for(let P=1;P<y;P*=2){let _=P*2;for(let $=P;$>=1;$/=2)w(_,$,[m,x])}for(let P=x;P>y;P/=2){let _=b(),$=new cle([m,P/2]),O=[[u],[A===null?1:0],[y]],V=A;A=n.runWebGLProgram($,_,"int32",O),Fl(n,V);let K=y/2,z=K*2;for(let j=K;j>=1;j/=2)w(z,j,A.shape)}let C=A;A=gc({inputs:{x:A},backend:n,attrs:{begin:0,size:[m,a]}}),Fl(n,C);let S=a4({inputs:{x:g,indices:A},backend:n,attrs:{axis:1,batchDims:1}});Fl(n,g);let E=c.slice(0,-1);E.push(a),C=A,A=be({inputs:{x:A},attrs:{shape:E},backend:n}),Fl(n,C);let R=S;return S=be({inputs:{x:S},attrs:{shape:E},backend:n}),Fl(n,R),[S,A]}var ple={kernelName:Zi,backendName:"webgl",kernelFunc:dle},hle=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${i} == 2) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
if (inCoord < sz2) {
|
|
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
|
|
inCoord;
|
|
}
|
|
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
inCoord -= sz2 * float(int(float(inCoord / sz2)));
|
|
if (inCoord >= len) {
|
|
inCoord = sz2 - inCoord - 1.0;
|
|
}
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${i} == 3) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord -= len * float(int(float(inCoord / sz)));
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${i} == 4) {
|
|
return clamp(outCoord, 0.0, len - 1.0);
|
|
} else {
|
|
return outCoord;
|
|
}
|
|
}
|
|
|
|
float readWithFillValue(int batch, int coordY, int coordX,
|
|
int channel) {
|
|
float outputValue;
|
|
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
|
|
outputValue = getImage(batch, coordY, coordX, channel);
|
|
} else {
|
|
outputValue = float(${r});
|
|
}
|
|
return outputValue;
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
float outputValue;
|
|
int batch = coords[0];
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
int channel = coords[3];
|
|
float xf = float(x);
|
|
float yf = float(y);
|
|
float a1 = getTransforms(batch, 0);
|
|
float a2 = getTransforms(batch, 1);
|
|
float a3 = getTransforms(batch, 2);
|
|
float b1 = getTransforms(batch, 3);
|
|
float b2 = getTransforms(batch, 4);
|
|
float b3 = getTransforms(batch, 5);
|
|
float c1 = getTransforms(batch, 6);
|
|
float c2 = getTransforms(batch, 7);
|
|
float projection = c1 * xf + c2 * yf + 1.0;
|
|
if (projection == 0.0) {
|
|
outputValue = float(${r});
|
|
} else {
|
|
float inX = (a1 * xf + a2 * yf + a3) / projection;
|
|
float inY = (b1 * xf + b2 * yf + b3) / projection;
|
|
float mapX = mapCoord(inX, float(${t}));
|
|
float mapY = mapCoord(inY, float(${e}));
|
|
|
|
if (${o} == 1) {
|
|
int coordY = int(round(mapY));
|
|
int coordX = int(round(mapX));
|
|
outputValue = readWithFillValue(batch, coordY, coordX,
|
|
channel);
|
|
} else {
|
|
float yFloor = floor(mapY);
|
|
float xFloor = floor(mapX);
|
|
float yCeil = yFloor + 1.0;
|
|
float xCeil = xFloor + 1.0;
|
|
float valueYFloor = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
|
|
float valueYCeil = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
|
|
outputValue = (yCeil - mapY) * valueYFloor +
|
|
(mapY - yFloor) * valueYCeil;
|
|
}
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};function fle(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],y=new hle(d,p,o,i,l,g);return n.runWebGLProgram(y,[r,a],"float32")}var mle={kernelName:Yi,backendName:"webgl",kernelFunc:fle};function gle(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;uc(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:c}=BQ(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var yle={kernelName:ef,backendName:"webgl",kernelFunc:gle};function Ale(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=gc({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),y=be({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=y,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var xle={kernelName:Ji,backendName:"webgl",kernelFunc:Ale},ble=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,s=e.batchSize,r=e.inSize,a=e.numSegments,o=a*Math.ceil(r/n);this.outputShape=[s,o];let i="0.0",l="sumValue",c=Math.floor(n/4)*4,u=n%4,d=`
|
|
sumValue += dot(values, segFilter);
|
|
`,p="";r%n>0&&(p=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`);let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return -1.0;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${i};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${p}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
float getSegmentIdAtIndex(int inIdx) {
|
|
${h}
|
|
return getSegmentIds(inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = int(floor(float(outIdx) / float(
|
|
${a})) * float(${n}));
|
|
int currentSeg = int(mod(float(outIdx), float(${a})));
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${c}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0
|
|
);
|
|
|
|
${d}
|
|
}
|
|
|
|
int inIdx = inOffset + ${c};
|
|
if (${u===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
int inIdxSeg = int(getSegmentIdAtIndex(inIdx));
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
0,
|
|
0,
|
|
0
|
|
);
|
|
|
|
${d}
|
|
} else if (${u===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
0,
|
|
0
|
|
);
|
|
|
|
${d}
|
|
} else if (${u===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
vec4 segFilter = vec4(
|
|
int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,
|
|
int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,
|
|
0
|
|
);
|
|
|
|
${d}
|
|
}
|
|
setOutput(${l});
|
|
}
|
|
`}};function vle(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,segmentIds:a}=t,{numSegments:o}=s,i=r.shape.length,l=[],c=0,u=N.getAxesPermutation([c],i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(d),c=N.getInnerMostAxes(1,i)[0]);let p=N.segment_util.computeOutShape(d.shape,c,o),h=v.sizeFromShape([d.shape[c]]),f=be({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=Pd(r.dtype),g=(b,w,C,S,E)=>{let R=b.shape[0],P=b.shape[1],_=N.segment_util.segOpComputeOptimalWindowSize(P,E),$={windowSize:_,inSize:P,batchSize:R,numSegments:E},T=new ble($,w),O=n.compileAndRun(T,[b,C],S);if(l.push(O),O.shape[1]===E)return O;let V=f4({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),K=x4({inputs:{x:V},backend:n,attrs:{reps:[P/_]}});return l.push(V),l.push(K),g(O,w,K,S,E)},y=g(f,"unsortedSegmentSum",a,m,o),x=be({inputs:{x:y},backend:n,attrs:{shape:p}}),A=x;if(u!=null){l.push(x);let b=N.getUndoAxesPermutation(u);A=jn({inputs:{x:A},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),A}var wle={kernelName:Id,backendName:"webgl",kernelFunc:vle},kle=[Yre,eae,Pee,Mee,Bee,Uee,Hee,Xee,Zee,Jee,nte,rte,ite,cte,yte,hte,bte,Ste,wte,Nte,Rte,Dte,Ote,Ute,Hte,qte,Qte,tne,ane,lne,gee,hne,kne,Ine,yne,Ene,_ne,Tne,Fne,Mne,Bne,Vne,Gne,qne,Qne,tse,Kne,rse,ise,use,hse,yse,vse,Sse,Ise,Cse,Nse,Rse,Dse,Fse,Ose,Bse,Use,jse,Xse,Yse,ere,rre,lre,mee,cre,dne,hre,gre,xre,Aee,kre,Tre,Ere,Ore,$re,Bre,Ure,qre,nae,cae,lae,fae,gae,Aae,oae,bae,wae,Cae,Rae,Fae,Vae,kee,Gae,qae,Zae,Qae,Kte,noe,roe,ooe,uoe,hoe,bee,moe,goe,Zte,zae,xoe,Toe,koe,Iee,_oe,Foe,zoe,Woe,Hoe,qoe,Zoe,Qoe,tie,rie,iie,uie,pie,mie,Aie,Wte,Bae,vie,kie,Iie,Tie,Eie,_ie,$ie,Pie,Mie,Bie,Vie,Gie,qie,Kie,Yie,Qie,Lae,Dee,nle,ale,lle,ple,mle,$ee,yle,xle,wle,soe];for(let e of kle)cr(e);var Vr=Y();Vr.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Vr.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Vr.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Vr.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Vr.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Vr.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Vr.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Vr.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Vr.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Vr.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function Sle(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${t}[${a}]`),r=new Array(n-1);r[n-2]=s[n-1];for(let a=n-3;a>=0;--a)r[a]=`(${r[a+1]} * ${s[a+1]})`;return r}function wn(e){if(e<=1)return"i32";if(e===2)return"vec2<i32>";if(e===3)return"vec3<i32>";if(e===4)return"vec4<i32>";throw Error(`GPU for rank ${e} is not yet supported`)}function n0(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Tx(){return`
|
|
[[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]]
|
|
`}function ua(){return`
|
|
${Tx()}
|
|
fn main([[builtin(local_invocation_id)]] LocalId : vec3<u32>,
|
|
[[builtin(global_invocation_id)]] GlobalId : vec3<u32>,
|
|
[[builtin(num_workgroups)]] NumWorkgroups: vec3<u32>) {
|
|
localId = LocalId;
|
|
globalId = GlobalId;
|
|
numWorkgroups = NumWorkgroups;
|
|
`}function Ke(){return`
|
|
${ua()}
|
|
let index = getGlobalIndex();
|
|
`}function Ile(e,t,n,s=!1){let r=`
|
|
let workGroupSizeX = ${n.workGroupSize[0]}u;
|
|
let workGroupSizeY = ${n.workGroupSize[1]}u;
|
|
let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(s===!0){let h=k4(t.shape),f=`
|
|
struct Matrix0 {
|
|
numbers: array<${n0(t.dtype,n.isVec4)}>;
|
|
};
|
|
struct Uniform {
|
|
size : i32;
|
|
numChannels : i32;
|
|
outShapeStrides : vec2<i32>;
|
|
dispatchSize : vec3<u32>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
|
|
[[group(0), binding(2)]] var<uniform> uniforms: Uniform;
|
|
`;return[v4,f,r,w4,h,n.getUserCode()].join(`
|
|
`)}let a=[],o="struct Uniforms { NAN : f32; ";n.variableNames.forEach((h,f)=>{o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${wn(e[f].shape.length)}; `}),o+=`outShape : ${wn(t.shape.length)} ; `;let i=t.shape.length-1;o+=`
|
|
outShapeStrides: ${wn(i)}; `,n.size&&(o+="size : i32; "),n.uniforms&&(o+=n.uniforms),o+="};",a.push(o),n.atomic?a.push(`
|
|
struct Matrix0 {
|
|
numbers: array<atomic<i32>>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, read_write> result : Matrix0;
|
|
`):a.push(`
|
|
struct Matrix0 {
|
|
numbers: array<${n0(t.dtype,n.isVec4)}>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
|
|
`),n.variableNames.forEach((h,f)=>{a.push(`
|
|
struct Matrix${1+f} {
|
|
numbers: array<${n0(e[f].dtype,n.isVec4)}>;
|
|
};
|
|
[[group(0), binding(${1+f})]] var<storage, read> ${h} : Matrix${1+f};
|
|
`)}),o!==""&&a.push(`
|
|
[[group(0), binding(${1+n.variableNames.length})]] var<uniform> uniforms : Uniforms;
|
|
`),a.push(r);let[l,c]=_le(t.shape,n.dispatchLayout),u=k4(t.shape),d=[v4,a.join(`
|
|
`),w4,u,l,Cle(t.shape.length)];if(n.atomic||d.push(Tle(t.shape,t.dtype,n.isVec4)),c===t.shape.length){let h=e.map(f=>Nle(f,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
|
|
`);d.push(h)}return d.push(n.getUserCode()),d.join(`
|
|
`)}var v4=`
|
|
var<private> localId: vec3<u32>;
|
|
var<private> globalId: vec3<u32>;
|
|
var<private> numWorkgroups: vec3<u32>;
|
|
|
|
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
|
|
var res: i32 = a / b;
|
|
let mod: i32 = a % b;
|
|
if (sign < 0. && mod != 0) {
|
|
res = res - 1;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
fn isNanCustom(val : f32) -> bool {
|
|
if (val > 0.0) {
|
|
return false;
|
|
}
|
|
if (val < 0.0) {
|
|
return false;
|
|
}
|
|
if (val == 0.0) {
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
fn isNanCustomVec4(val : vec4<f32>) -> vec4<bool> {
|
|
return vec4<bool>(isNanCustom(val[0]), isNanCustom(val[1]), isNanCustom(val[2]), isNanCustom(val[3]));
|
|
}
|
|
|
|
// Checks whether coordinates lie within the bounds of the shape.
|
|
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
|
|
return all(coord >= vec4<i32>(0)) &&
|
|
all(coord < shape);
|
|
}
|
|
|
|
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
|
|
return all(coord >= vec3<i32>(0)) &&
|
|
all(coord < shape);
|
|
}
|
|
|
|
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
|
|
return all(coord >= vec2<i32>(0)) &&
|
|
all(coord < shape);
|
|
}
|
|
`,w4=`
|
|
fn getFlatIndex1D(coord : i32, shape : i32) -> i32 {
|
|
return coord;
|
|
}
|
|
|
|
fn getFlatIndex2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
|
|
return dot(coords, vec2<i32>(shape.y, 1));
|
|
}
|
|
|
|
fn getFlatIndex3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
|
|
return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
|
|
}
|
|
|
|
fn getFlatIndex4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
|
|
return dot(coords, vec4<i32>(
|
|
shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
|
|
}
|
|
|
|
// Only used when the y/z dimension of workgroup size is 1.
|
|
fn getGlobalIndex() -> i32 {
|
|
if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
|
|
return i32(globalId.x);
|
|
}
|
|
|
|
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
|
|
localId.y * workGroupSizeX + localId.x;
|
|
let workGroupID = (globalId - localId)/vec3<u32>(
|
|
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
|
|
|
|
return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
|
|
workGroupID.y * numWorkgroups.x + workGroupID.x) *
|
|
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
|
|
localInvocationIndex);
|
|
}
|
|
`;function Cle(e){let t="";switch(e){case 0:case 1:t+=`
|
|
fn getOutputFlatIndex(coords : i32) -> i32 {
|
|
return coords;
|
|
}
|
|
`;break;case 2:t+=`
|
|
fn getOutputFlatIndex(coords : vec2<i32>) -> i32 {
|
|
return dot(coords, vec2<i32>(uniforms.outShapeStrides, 1));
|
|
}
|
|
`;break;case 3:t+=`
|
|
fn getOutputFlatIndex(coords : vec3<i32>) -> i32 {
|
|
return dot(coords, vec3<i32>(uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, 1));
|
|
}
|
|
`;break;case 4:t+=`
|
|
fn getOutputFlatIndex(coords : vec4<i32>) -> i32 {
|
|
return dot(coords, vec4<i32>(
|
|
uniforms.outShapeStrides.x, uniforms.outShapeStrides.y, uniforms.outShapeStrides.z, 1));
|
|
}
|
|
`;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Tle(e,t,n){let s=e.length,r=n0(t,n),a;if(n?a=`fn setOutputFlat(flatIndex : i32, value : vec4<f32>) {
|
|
result.numbers[flatIndex] = ${r}(value);
|
|
}
|
|
fn setOutputFlatI32(flatIndex : i32, value : vec4<i32>) {
|
|
result.numbers[flatIndex] = ${r}(value);
|
|
}`:a=`fn setOutputFlat(flatIndex : i32, value : f32) {
|
|
result.numbers[flatIndex] = ${r}(value);
|
|
}
|
|
fn setOutputFlatI32(flatIndex : i32, value : i32) {
|
|
result.numbers[flatIndex] = ${r}(value);
|
|
}`,s>=2){let o=["d0","d1","d2","d3"].slice(0,s),i=wn(s);n?a+=`
|
|
fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<f32>) {
|
|
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
|
|
setOutputFlat(flatIndex / 4, value);
|
|
}
|
|
fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : vec4<i32>) {
|
|
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
|
|
setOutputFlatI32(flatIndex / 4, value);
|
|
}
|
|
`:a+=`
|
|
fn setOutput(${o.map(l=>`${l} : i32`).join(", ")}, value : f32) {
|
|
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
|
|
setOutputFlat(flatIndex, value);
|
|
}
|
|
fn setOutputI32(${o.map(l=>`${l} : i32`).join(", ")}, value : i32) {
|
|
let flatIndex = getOutputFlatIndex(${i}(${o.join(", ")}));
|
|
setOutputFlatI32(flatIndex, value);
|
|
}
|
|
`}return a}function Nle(e,t,n,s){let r=Ele(e,n);return e.shape.length<=t.length&&(r+=Rle(e,t,n,s)),r}function Ele(e,t){let n=e.name,s=e.shape.length,r=wn(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,s),i=o.map(u=>`${u} : i32`).join(", ");if(s<1)return t?`
|
|
fn ${a}() -> vec4<f32> {
|
|
return vec4<f32>(${n}.numbers[0]);
|
|
}
|
|
`:`
|
|
fn ${a}() ->f32 {
|
|
return f32(${n}.numbers[0]);
|
|
}
|
|
`;let l=`uniforms.${n.charAt(0).toLowerCase()+n.slice(1)}Shape`,c=`${s}D`;return s===0&&(c="1D"),t?`
|
|
fn ${a}(${i}) -> vec4<f32> {
|
|
return vec4<f32>(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}),
|
|
${l}) / 4]);
|
|
}
|
|
`:`
|
|
fn ${a}(${i}) -> f32 {
|
|
return f32(${n}.numbers[getFlatIndex${c}(${r}(${o.join(",")}),
|
|
${l})]);
|
|
}
|
|
`}function Rle(e,t,n,s){let r=e.name,a=r.charAt(0).toUpperCase()+r.slice(1),o="get"+a+"AtOutCoords",i=e.shape.length,l=t.length,c=wn(l);if(v.arraysEqual(e.shape,t)&&s)return n?`
|
|
fn ${o}ByGlobalIndex(globalIndex : i32) -> vec4<f32> {
|
|
return vec4<f32>(${r}.numbers[globalIndex]);
|
|
}
|
|
|
|
fn ${o}ByCoords(coords : ${c}) -> vec4<f32> {
|
|
return vec4<f32>(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"} / 4]);
|
|
}
|
|
`:`
|
|
fn ${o}ByGlobalIndex(globalIndex : i32) -> f32 {
|
|
return f32(${r}.numbers[globalIndex]);
|
|
}
|
|
|
|
fn ${o}ByCoords(coords : ${c}) -> f32 {
|
|
return f32(${r}.numbers[${l>1?"getOutputFlatIndex(coords)":"coords"}]);
|
|
}
|
|
`;let u=N.getBroadcastDims(e.shape,t),d=l-i,p="";if(i===0)return n?`
|
|
fn ${o}ByGlobalIndex(globalIndex : i32) -> vec4<f32> {
|
|
return get${a}();
|
|
}
|
|
|
|
fn ${o}ByCoords(coords : ${c}) -> vec4<f32> {
|
|
return get${a}();
|
|
}
|
|
`:`
|
|
fn ${o}ByGlobalIndex(globalIndex : i32) -> f32{
|
|
return get${a}();
|
|
}
|
|
|
|
fn ${o}ByCoords(coords : ${c}) -> f32{
|
|
return get${a}();
|
|
}
|
|
`;l<2&&u.length>=1?p="coords = 0;":p=u.map(g=>`coords[${g+d}] = 0;`).join(`
|
|
`);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=wn(i),y=e.shape.map((x,A)=>`coords[${A+d}]`).join(", ");h=`${g}(${y})`}else h="coords";let f=`uniforms.${r.charAt(0).toLowerCase()+r.slice(1)}Shape`,m=`${i}D`;return n?`
|
|
fn ${o}ByGlobalIndex(globalIndex : i32) -> vec4<f32> {
|
|
var coords = getCoordsFromFlatIndex(globalIndex);
|
|
${p}
|
|
return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
|
|
}
|
|
|
|
fn ${o}ByCoords(coordsIn : ${c}) -> vec4<f32> {
|
|
var coords = coordsIn;
|
|
${p}
|
|
return ${r}.numbers[getFlatIndex${m}(${h}, ${f}) / 4];
|
|
}
|
|
`:`
|
|
fn ${o}ByGlobalIndex(globalIndex : i32) -> f32 {
|
|
var coords = getCoordsFromFlatIndex(globalIndex);
|
|
${p}
|
|
return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]);
|
|
}
|
|
|
|
fn ${o}ByCoords(coordsIn : ${c}) -> f32 {
|
|
var coords = coordsIn;
|
|
${p}
|
|
return f32(${r}.numbers[getFlatIndex${m}(${h}, ${f})]);
|
|
}
|
|
`}function _le(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${wn(a)}{
|
|
let globalIndex = getGlobalIndex();
|
|
return getCoordsFromFlatIndex(globalIndex);
|
|
}
|
|
`,a];let o="",i=[n,s,r],l=0;for(let p=0;p<i.length;p++){let h=i[p];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${p}]);`;else{let f=Sle(h,"uniforms.outShape");o+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${p} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${p} - d${h[m]} * ${f[m]};`:o+=`index${p} = index${p} - d${h[m]} * ${f[m]};`}}let c=[];for(let p=0;p<l;p++)c.push(`d${p}`);let u=wn(l),d=`fn getOutputCoords() -> ${u} {
|
|
${o}
|
|
`;return c.length===0?d+=`return ${u}(0); }`:d+=`return ${u}(${c.join(",")}); }`,[d,l]}function k4(e){let t=e.length;if(t<=1)return"fn getCoordsFromFlatIndex(index : i32) -> i32 { return index; }";let n=v.computeStrides(e),s=wn(t),r=[];for(let o=0;o<t;o++)r.push(`d${o}`);if(n.length===1)return` fn getCoordsFromFlatIndex(index : i32) -> vec2<i32> {
|
|
let d0 = index / uniforms.outShapeStrides; let d1 = index - d0 * uniforms.outShapeStrides;
|
|
return vec2<i32>(d0, d1);
|
|
}`;let a="var index2 = index;"+n.map((o,i)=>{let l=`let ${r[i]} = index2 / uniforms.outShapeStrides[${i}]`,c=i===n.length-1?`let ${r[i+1]} = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`:`index2 = index2 - ${r[i]} * uniforms.outShapeStrides[${i}]`;return`${l}; ${c};`}).join("");return`
|
|
fn getCoordsFromFlatIndex(index : i32) -> ${s} {
|
|
${a}
|
|
return ${s}(${r.join(",")});
|
|
}
|
|
`}var S4={};Me(S4,{ArrayBufferToTypedArray:()=>I4,GPUBytesPerElement:()=>_x,computeDispatch:()=>Oe,computeWorkGroupSizeForConv2d:()=>Nx,computeWorkGroupSizeForMatMul:()=>Ex,computeWorkPerThreadForConv2d:()=>Rx,flatDispatchLayout:()=>He,isWebGPUSupported:()=>Dx,tilesFitEvenlyIntoShape:()=>ca});var xc=65535,Pl=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function ca(e,t){if(e.length!==t.length)throw new Error(`Cannot compute whether rank ${e.length} tiles fit evenly into rank ${t.length} shape - ranks must match.`);return t.every((n,s)=>n%e[s]==0)}function Oe(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(Pl(e.x.map(l=>t[l]))/(n[0]*s[0])),e.y?Math.ceil(Pl(e.y.map(l=>t[l]))/(n[1]*s[1])):1,e.z?Math.ceil(Pl(e.z.map(l=>t[l]))/(n[2]*s[2])):1];if(r<=xc&&a<=xc&&o<=xc)return[r,a,o];v.assert(r>xc&&e.y===void 0&&e.z===void 0,()=>"Dispatch size exceeds WebGPU limits in Y or Z dimension.");let i=Math.ceil(Math.sqrt(r));return i>xc?(i=Math.ceil(Math.cbrt(r)),v.assert(i<=xc,()=>"Total dispatch size exceeds WebGPU maximum."),[i,i,i]):[i,i,1]}function Nx(e,t){let n=Pl(e.x.map(r=>t[r])),s=Pl(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function Ex(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Rx(e,t){let n=Pl(e.x.map(r=>t[r])),s=Pl(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function He(e){return{x:e.map((t,n)=>n)}}function _x(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function I4(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string"){let n=new Int32Array(e),s=new ArrayBuffer(n.length),r=new Uint8Array(s);for(let a=0;a<n.length;a++)r[a]=n[a];return r}else throw new Error(`Unknown dtype ${t}`)}function Dx(){return!!navigator.gpu}var jt=(e=>(e[e.MUL=0]="MUL",e[e.ADD=1]="ADD",e[e.SUB=2]="SUB",e[e.DIV=3]="DIV",e[e.EQUAL=4]="EQUAL",e[e.GREATER=5]="GREATER",e[e.GREATER_EQUAL=6]="GREATER_EQUAL",e[e.LESS=7]="LESS",e[e.LESS_EQUAL=8]="LESS_EQUAL",e[e.LOGICAL_AND=9]="LOGICAL_AND",e[e.NOT_EQUAL=10]="NOT_EQUAL",e[e.SQUARED_DIFFERENCE=11]="SQUARED_DIFFERENCE",e[e.INT_DIV=12]="INT_DIV",e[e.POW=13]="POW",e[e.PRELU=14]="PRELU",e[e.MAX=15]="MAX",e[e.MIN=16]="MIN",e[e.COMPLEX_MULTIPLY_REAL=17]="COMPLEX_MULTIPLY_REAL",e[e.COMPLEX_MULTIPLY_IMAG=18]="COMPLEX_MULTIPLY_IMAG",e))(jt||{}),Dle="return a + b;",$le="return areal * breal - aimag * bimag;",Fle="return areal * bimag + aimag * breal;",Ple="return a / b;",Ole="return a * b;",Mle="return (a - b) * (a - b);",zle="return a - b;",Lle="return f32(a == b);",Ble="return vec4<f32>(a == b);",Wle="return f32(a > b);",Vle="return vec4<f32>(a > b);",Ule="return f32(a >= b);",Gle="return vec4<f32>(a >= b);",Hle="return f32(a < b);",jle="return vec4<f32>(a < b);",qle="return f32(a <= b);",Xle="return vec4<f32>(a <= b);",Kle="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Zle=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
|
|
vec4<f32>(b >= vec4<f32>(1.0)));`,Yle=`
|
|
if (isNanCustom(a)) { return a; }
|
|
if (isNanCustom(b)) { return b; }
|
|
`,C4=`
|
|
if (isNaN.r) {
|
|
resultTemp.r = uniforms.NAN;
|
|
}
|
|
if (isNaN.g) {
|
|
resultTemp.g = uniforms.NAN;
|
|
}
|
|
if (isNaN.b) {
|
|
resultTemp.b = uniforms.NAN;
|
|
}
|
|
if (isNaN.a) {
|
|
resultTemp.a = uniforms.NAN;
|
|
}
|
|
`,Jle=`
|
|
let s = sign(a) * sign(b);
|
|
let ia = i32(round(a));
|
|
let ib = i32(round(b));
|
|
return f32(idiv(ia, ib, s));
|
|
`,Qle=`
|
|
let ia = vec4<i32>(round(a));
|
|
let ib = vec4<i32>(round(b));
|
|
let cond = ib != vec4<i32>(0);
|
|
var resultTemp = vec4<i32>(0);
|
|
let s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
resultTemp[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
resultTemp[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
resultTemp[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
resultTemp[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4<f32>(resultTemp);
|
|
`,eue="return f32(a != b);",tue="return vec4<f32>(a != b);",nue=`
|
|
if(a < 0.0 && floor(b) < b) {
|
|
return uniforms.NAN;
|
|
}
|
|
if (b == 0.0) {
|
|
return 1.0;
|
|
}
|
|
if (round(abs(b) % 2.0) != 1.0) {
|
|
return pow(abs(a), b);
|
|
}
|
|
return sign(a) * pow(abs(a), b);
|
|
`,sue=`
|
|
let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
|
|
let isModRound1 = vec4<f32>(isModRound1Bool);
|
|
let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
|
|
var resultTemp = multiplier * pow(abs(a), b);
|
|
|
|
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
|
|
let isExpZero = b == vec4<f32>(0.0);
|
|
if (isExpZero.r) {
|
|
resultTemp.r = 1.0;
|
|
}
|
|
if (isExpZero.g) {
|
|
resultTemp.g = 1.0;
|
|
}
|
|
if (isExpZero.b) {
|
|
resultTemp.b = 1.0;
|
|
}
|
|
if (isExpZero.a) {
|
|
resultTemp.a = 1.0;
|
|
}
|
|
let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
|
|
${C4}
|
|
return resultTemp;
|
|
`,rue="if (a < 0.0) { return b * a; } return a;",aue=`
|
|
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
|
|
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
|
|
`;function T4(e,t){let n=t?C4:Yle;return t?`
|
|
var resultTemp = vec4<f32>(${e}(a, b));
|
|
let isNaN = isNanCustomVec4(a) | isNanCustomVec4(b);
|
|
`+n+`
|
|
return resultTemp;
|
|
`:n+`
|
|
return ${e}(a, b);
|
|
`}function Rp(e,t){switch(e){case 0:return Ole;case 1:return Dle;case 2:return zle;case 3:return Ple;case 4:return t?Ble:Lle;case 5:return t?Vle:Wle;case 6:return t?Gle:Ule;case 7:return t?jle:Hle;case 8:return t?Xle:qle;case 9:return t?Zle:Kle;case 10:return t?tue:eue;case 11:return Mle;case 12:return t?Qle:Jle;case 14:return t?aue:rue;case 15:return T4("max",t);case 16:return T4("min",t);case 13:return t?sue:nue;case 17:return $le;case 18:return Fle;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var wt=(e=>(e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.LINEAR=8]="LINEAR",e[e.LOG=9]="LOG",e[e.LOGICAL_NOT=10]="LOGICAL_NOT",e[e.NEG=11]="NEG",e[e.PRELU=12]="PRELU",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.LEAKYRELU=15]="LEAKYRELU",e[e.RSQRT=16]="RSQRT",e[e.SIN=17]="SIN",e[e.SINH=18]="SINH",e[e.SIGMOID=19]="SIGMOID",e[e.SQRT=20]="SQRT",e[e.SQUARE=21]="SQUARE",e[e.TANH=22]="TANH",e[e.TO_INT=23]="TO_INT",e))(wt||{}),oue="return abs(a);",iue="return ceil(a);",lue="return cos(a);",uue=`
|
|
let e2x = exp(-a);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,cue="return exp(a) - 1.0;",due="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",pue=`
|
|
var resFloat = exp(a) - vec4<f32>(1.0);
|
|
if (a.r >= 0.0) {
|
|
resFloat.r = a.r;
|
|
}
|
|
if (a.g >= 0.0) {
|
|
resFloat.g = a.g;
|
|
}
|
|
if (a.b >= 0.0) {
|
|
resFloat.b = a.b;
|
|
}
|
|
if (a.a >= 0.0) {
|
|
resFloat.a = a.a;
|
|
}
|
|
return resFloat;
|
|
`,hue="return exp(a);",fue="return floor(a);",mue="return a;",gue=`if (a < 0.0) { return 1.0/0.0; }
|
|
return log(a);`,yue="return f32(!(a >= 1.0));",Aue="return -a;",xue="return (a < 0.0) ? b * a : a;",bue="if (a < 0.0) { return uniforms.alpha * a; } return a;",vue="return max(a, 0.0);",wue="return clamp(a, 0.0, 6.0);",kue="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Sue=`
|
|
var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
|
|
let isNaN = isNanCustomVec4(a);
|
|
|
|
if (isNaN.r) {
|
|
resFloat.r = a.r;
|
|
}
|
|
if (isNaN.g) {
|
|
resFloat.g = a.g;
|
|
}
|
|
if (isNaN.b) {
|
|
resFloat.b = a.b;
|
|
}
|
|
if (isNaN.a) {
|
|
resFloat.a = a.a;
|
|
}
|
|
return resFloat;
|
|
`,Iue="return 1.0/sqrt(a);",Cue="return 1.0 / (1.0 + exp(-1.0 * a));",Tue="return sin(a);",Nue=`
|
|
let e2x = exp(a);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,Eue="return sqrt(a);",Rue="return a * a;",_ue=`
|
|
let e2x = exp(-2.0 * abs(a));
|
|
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,Due="return f32(i32((a)));";function bc(e,t){switch(e){case 0:return oue;case 2:return lue;case 3:return uue;case 1:return iue;case 4:return t?pue:due;case 5:return hue;case 6:return cue;case 7:return fue;case 8:return mue;case 9:return gue;case 10:return yue;case 11:return Aue;case 12:return xue;case 15:return bue;case 13:return t?Sue:vue;case 14:return t?kue:wue;case 16:return Iue;case 19:return Cue;case 17:return Tue;case 18:return Nue;case 20:return Eue;case 21:return Rue;case 22:return _ue;case 23:return Due;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function da(e,t=!1){if(e===null)return null;if(e==="linear")return bc(wt.LINEAR);if(e==="relu")return bc(wt.RELU,t);if(e==="elu")return bc(wt.ELU,t);if(e==="relu6")return bc(wt.RELU6,t);if(e==="prelu")return Rp(jt.PRELU,t);if(e==="sigmoid")return bc(wt.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function N4(e,t){let n={RowPerThread:e[1],ColPerThread:e[0],TileAOuter:t[1]*e[1],TileBOuter:t[0]*e[0],TileInner:t[0]*e[0]};return`
|
|
var<workgroup> mm_Asub : array<array<vec4<f32>, ${n.TileInner/n.ColPerThread}>, ${n.TileAOuter}>;
|
|
var<workgroup> mm_Bsub : array<array<vec4<f32>, ${n.TileBOuter/n.ColPerThread}>, ${n.TileInner}>;
|
|
|
|
let RowPerThread = ${n.RowPerThread};
|
|
let ColPerThread = ${n.ColPerThread}; // only support ColPerThread = 4
|
|
let TileAOuter = ${n.TileAOuter};
|
|
let TileBOuter = ${n.TileBOuter};
|
|
let TileInner = ${n.TileInner};
|
|
|
|
${ua()}
|
|
|
|
let tileRow = i32(localId.y) * RowPerThread;
|
|
let tileCol = i32(localId.x);
|
|
|
|
let globalRow = i32(globalId.y) * RowPerThread;
|
|
let globalCol = i32(globalId.x);
|
|
let numTiles = (uniforms.dimInner - 1) / TileInner + 1;
|
|
|
|
var acc: array<vec4<f32>, ${n.RowPerThread}>;
|
|
var ACached : vec4<f32>;
|
|
var BCached : array<vec4<f32>, 4>;
|
|
|
|
// Loop over shared dimension.
|
|
var globalColA = tileCol;
|
|
let RowPerThreadB = TileInner / ${t[1]};
|
|
let tileRowB = i32(localId.y) * RowPerThreadB;
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
// Load one tile of A into local memory.
|
|
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
|
|
let inputRow = tileRow + innerRow;
|
|
let inputCol = tileCol;
|
|
mm_Asub[inputRow][inputCol] = mm_readA(globalRow + innerRow, globalColA, globalId);
|
|
}
|
|
globalColA = globalColA + TileInner / ColPerThread;
|
|
|
|
// Load one tile of B into local memory.
|
|
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
|
|
let inputRow = tileRowB + innerRow;
|
|
let inputCol = tileCol;
|
|
mm_Bsub[inputRow][inputCol] = mm_readB(t * TileInner + inputRow, globalCol, globalId);
|
|
}
|
|
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < TileInner / ColPerThread; k = k + 1) {
|
|
BCached[0] = mm_Bsub[k * ColPerThread][tileCol];
|
|
BCached[1] = mm_Bsub[k * ColPerThread + 1][tileCol];
|
|
BCached[2] = mm_Bsub[k * ColPerThread + 2][tileCol];
|
|
BCached[3] = mm_Bsub[k * ColPerThread + 3][tileCol];
|
|
|
|
for (var i = 0; i < RowPerThread; i = i + 1) {
|
|
ACached = mm_Asub[tileRow + i][k];
|
|
acc[i] = BCached[0] * ACached.x + acc[i];
|
|
acc[i] = BCached[1] * ACached.y + acc[i];
|
|
acc[i] = BCached[2] * ACached.z + acc[i];
|
|
acc[i] = BCached[3] * ACached.w + acc[i];
|
|
}
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
for (var innerRow = 0; innerRow < RowPerThread; innerRow = innerRow + 1) {
|
|
mm_write(globalRow + innerRow,
|
|
globalCol,
|
|
acc[innerRow], globalId);
|
|
}
|
|
}`}function $ue(e){return`
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
let tileSize = ${e[0]*4};
|
|
${ua()}
|
|
let tileCol = i32(localId.x);
|
|
let globalCol = i32(globalId.x);
|
|
let globalRow = i32(globalId.y);
|
|
|
|
let numTiles = (uniforms.dimInner - 1) / tileSize + 1;
|
|
|
|
// Without this initialization strange values show up in acc.
|
|
var acc = vec4<f32>(0.0);
|
|
|
|
// Loop over shared dimension.
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
// Load one tile of A into local memory.
|
|
let colA = t * tileSize / 4 + tileCol;
|
|
mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId);
|
|
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < tileSize / 4; k = k + 1) {
|
|
let rowB = t * tileSize + k * 4;
|
|
let BCached0 = mm_readB(rowB, globalCol, globalId);
|
|
let BCached1 = mm_readB(rowB + 1, globalCol, globalId);
|
|
let BCached2 = mm_readB(rowB + 2, globalCol, globalId);
|
|
let BCached3 = mm_readB(rowB + 3, globalCol, globalId);
|
|
|
|
let ACached = mm_Asub[k];
|
|
acc = acc + BCached0 * ACached.x;
|
|
acc = acc + BCached1 * ACached.y;
|
|
acc = acc + BCached2 * ACached.z;
|
|
acc = acc + BCached3 * ACached.w;
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
|
|
mm_write(globalRow, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var Fue=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=Ex(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${n}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(){let e=this.aShape[2],t=this.outputShape[2],n=[this.outputShape[0],e,t],s=this.workGroupSize[1]*this.workPerThread,r=this.workGroupSize[0]*this.vecSize,a=r,o=[s,a],i=[a,r];return[ca(o,this.aShape.slice(1)),ca(i,n.slice(1))]}getUserCode(){let e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col]":`if (coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
|
|
return A.numbers[batch * batchASize + row * uniforms.dimInner / 4 + col];
|
|
}
|
|
return vec4<f32>(0.0)`,t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col]":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
|
|
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter / 4 + col];
|
|
}
|
|
return vec4<f32>(0.0)`,n="",s="";if(this.activation){let o=da(this.activation,this.isVec4);this.hasPreluActivationWeights?n=`fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${o}
|
|
}`:n=`
|
|
fn activation(a : vec4<f32>, outCoord : vec3<i32>) -> vec4<f32> {
|
|
${o}
|
|
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${n}
|
|
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
|
|
let batchASize = uniforms.aShape[1] * uniforms.aShape[2] / ${this.vecSize};
|
|
let batch = i32(globalId.z);
|
|
${e};
|
|
}
|
|
|
|
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
|
|
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2] / ${this.vecSize};
|
|
let batch = i32(globalId.z);
|
|
${t};
|
|
}
|
|
|
|
fn mm_write(row : i32, col : i32, valueIn : vec4<f32>, globalId : vec3<u32>) {
|
|
if (row < uniforms.aShape[1] && col * 4 < uniforms.bShape[2])
|
|
{
|
|
var value = valueIn;
|
|
let batch = i32(globalId.z);
|
|
let outCoord = vec3<i32>(batch, row, col * 4);
|
|
${r}
|
|
${s}
|
|
setOutput(outCoord[0], outCoord[1], outCoord[2], value);
|
|
}
|
|
}
|
|
${this.outputShape[1]>1?N4([this.vecSize,this.workPerThread,1],this.workGroupSize):$ue(this.workGroupSize)}
|
|
|
|
`}};function $x(e,t){let n=t[1]*e[1],s=t[0]*e[0],r=n>s?n:s;return`
|
|
var<workgroup> mm_Asub : array<array<f32, ${r}>, ${n}>;
|
|
var<workgroup> mm_Bsub : array<array<f32, ${s}>, ${r}>;
|
|
${ua()}
|
|
let tileRow = i32(localId.y) * ${e[1]};
|
|
let tileCol = i32(localId.x) * ${e[0]};
|
|
|
|
let globalRow = i32(globalId.y) * ${e[1]};
|
|
let globalCol = i32(globalId.x) * ${e[0]};
|
|
|
|
let numTiles = (uniforms.dimInner - 1) / ${r} + 1;
|
|
|
|
var acc : array<array<f32, ${e[0]}>, ${e[1]}>;
|
|
var ACached : f32;
|
|
var BCached : array<f32, ${e[0]}>;
|
|
|
|
// Without this initialization strange values show up in acc.
|
|
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
|
|
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
|
|
acc[innerRow][innerCol] = 0.0;
|
|
}
|
|
}
|
|
|
|
let ColPerThreadA = ${r} / ${t[0]};
|
|
let tileColA = i32(localId.x) * ColPerThreadA;
|
|
let RowPerThreadB = ${r} / ${t[1]};
|
|
let tileRowB = i32(localId.y) * RowPerThreadB;
|
|
|
|
// Loop over shared dimension.
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
// Load one tile of A into local memory.
|
|
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
|
|
for (var innerCol = 0; innerCol < ColPerThreadA; innerCol = innerCol + 1) {
|
|
let inputRow = tileRow + innerRow;
|
|
let inputCol = tileColA + innerCol;
|
|
|
|
mm_Asub[inputRow][inputCol] = mm_readA(
|
|
globalRow + innerRow,
|
|
t * ${r} + inputCol, globalId);
|
|
}
|
|
}
|
|
// Load one tile of B into local memory.
|
|
for (var innerRow = 0; innerRow < RowPerThreadB; innerRow = innerRow + 1) {
|
|
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
|
|
let inputRow = tileRowB + innerRow;
|
|
let inputCol = tileCol + innerCol;
|
|
|
|
mm_Bsub[inputRow][inputCol] = mm_readB(
|
|
t * ${r} + inputRow,
|
|
globalCol + innerCol, globalId);
|
|
}
|
|
}
|
|
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < ${r}; k = k + 1) {
|
|
for (var inner = 0; inner < ${e[0]}; inner = inner + 1) {
|
|
BCached[inner] = mm_Bsub[k][tileCol + inner];
|
|
}
|
|
|
|
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
|
|
ACached = mm_Asub[tileRow + innerRow][k];
|
|
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
|
|
acc[innerRow][innerCol] = acc[innerRow][innerCol] + ACached * BCached[innerCol];
|
|
}
|
|
}
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
for (var innerRow = 0; innerRow < ${e[1]}; innerRow = innerRow + 1) {
|
|
for (var innerCol = 0; innerCol < ${e[0]}; innerCol = innerCol + 1) {
|
|
|
|
if ((globalCol + innerCol) < uniforms.dimBOuter &&
|
|
(globalRow + innerRow) < uniforms.dimAOuter) {
|
|
mm_write(globalRow + innerRow,
|
|
globalCol + innerCol,
|
|
acc[innerRow][innerCol], globalId);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
`}function Pue(e){return`
|
|
let TileSize = ${e[0]*4};
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
|
|
${ua()}
|
|
let tileCol = i32(localId.x);
|
|
let globalCol = i32(globalId.x);
|
|
let globalRow = i32(globalId.y);
|
|
|
|
let numTiles = (uniforms.dimInner - 1) / TileSize + 1;
|
|
|
|
// Without this initialization strange values show up in acc.
|
|
var acc = 0.0;
|
|
|
|
// Loop over shared dimension.
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
// Load one tile of A into local memory.
|
|
let colA = t * TileSize + tileCol * 4;
|
|
mm_Asub[tileCol] = vec4<f32>(mm_readA(globalRow, colA, globalId),
|
|
mm_readA(globalRow, colA + 1, globalId),
|
|
mm_readA(globalRow, colA + 2, globalId),
|
|
mm_readA(globalRow, colA + 3, globalId));
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < TileSize / 4; k = k + 1) {
|
|
let rowB = t * TileSize + k * 4;
|
|
let BCached = vec4<f32>(mm_readB(rowB, globalCol, globalId),
|
|
mm_readB(rowB + 1, globalCol, globalId),
|
|
mm_readB(rowB + 2, globalCol, globalId),
|
|
mm_readB(rowB + 3, globalCol, globalId));
|
|
|
|
let ACached = mm_Asub[k];
|
|
acc = acc + dot(ACached, BCached);
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
|
|
mm_write(globalRow, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var E4=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=s?e[1]:e[2];this.workGroupSize=Ex(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let c=a!=null,u=i!=null;c&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=c,this.activation=o,this.hasPreluActivationWeights=u;let d=this.outputShape[2],p=this.transposeB?[this.outputShape[0],d,l]:[this.outputShape[0],l,d];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${this.workPerThread}_${s}_${r}_${this.activation}_${this.fitA}_${this.fitB}_${this.outputShape[1]>1}`}getShapeFit(e){let t=this.workGroupSize[1]*this.workPerThread,n=this.workGroupSize[0]*this.workPerThread,s=t>n?t:n;this.outputShape[1]===1&&(s*=4),v.assert(s%this.workGroupSize[0]==0&&s%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let r=[t,s],a=[s,n];return[ca(r,this.aShape.slice(1)),ca(a,e.slice(1))]}getUserCode(){let e;this.transposeA===!1?e=this.fitA?"return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
|
|
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
|
|
}
|
|
return 0.0;`:e=this.fitA?"return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
|
|
return A.numbers[batch* batchASize + col * uniforms.dimAOuter + row];
|
|
}
|
|
return 0.0;`;let t;this.transposeB===!1?t=this.fitB?"return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
|
|
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
|
|
}
|
|
return 0.0;`:t=this.fitB?"return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
|
|
return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];
|
|
}
|
|
return 0.0;`;let n="",s="";if(this.activation){let o=da(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${o}
|
|
}`:n=`
|
|
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
|
|
${o}
|
|
}
|
|
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${n}
|
|
|
|
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
|
|
let batch = i32(globalId.z);
|
|
${e}
|
|
}
|
|
|
|
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
let batch = i32(globalId.z);
|
|
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
|
|
${t}
|
|
}
|
|
|
|
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
|
|
var value = valueIn;
|
|
let batch = i32(globalId.z);
|
|
let outCoord = vec3<i32>(batch, row, col);
|
|
${r}
|
|
${s}
|
|
setOutput(batch, row, col, value);
|
|
}
|
|
${this.outputShape[1]>1?$x([this.workPerThread,this.workPerThread,1],this.workGroupSize):Pue(this.workGroupSize)}
|
|
`}};function Oue(){return`
|
|
var<workgroup> sumValues : array<f32, workGroupSizeX>;
|
|
${ua()}
|
|
let coords = getOutputCoords();
|
|
let batch = coords[0];
|
|
let row = coords[1];
|
|
let col = coords[2];
|
|
var sum = 0.0;
|
|
let Length = uniforms.dimInner;
|
|
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
|
|
let dataA = mm_readA(batch, row, k);
|
|
let dataB = mm_readB(batch, k, col);
|
|
sum = sum + dataA * dataB;
|
|
}
|
|
sumValues[localId.x] = sum;
|
|
workgroupBarrier();
|
|
|
|
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
|
|
currentSize = currentSize / 2u) {
|
|
if (localId.x < currentSize)
|
|
{
|
|
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
|
|
}
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (localId.x == 0u) {
|
|
sum = sumValues[0] + sumValues[1];
|
|
mm_write(batch, row, col, sum);
|
|
}
|
|
}
|
|
`}var Mue=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulReduce_${this.activation}_${t}_${n}`}getUserCode(){let e;this.transposeA===!1?e="return A.numbers[batch * batchASize + row * uniforms.dimInner + col];":e="return A.numbers[batch * batchASize + col * uniforms.dimAOuter + row];";let t;this.transposeB===!1?t="return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];":t="return B.numbers[batch * batchBSize + col * uniforms.dimInner + row];";let n="",s="";if(this.activation){let o=da(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${o}
|
|
}`:n=`
|
|
fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
|
|
${o}
|
|
}
|
|
`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${n}
|
|
|
|
fn mm_readA(batch: i32, row : i32, col : i32) -> f32 {
|
|
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
|
|
${e}
|
|
}
|
|
|
|
fn mm_readB(batch: i32, row : i32, col : i32) -> f32 {
|
|
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
|
|
${t}
|
|
}
|
|
|
|
fn mm_write(batch: i32, row : i32, col : i32, valueIn : f32) {
|
|
var value = valueIn;
|
|
let outCoord = vec3<i32>(batch, row, col);
|
|
${r}
|
|
${s}
|
|
setOutput(batch, row, col, value);
|
|
}
|
|
${Oue()}
|
|
`}};function zue(e){let t=e[1]/2,n=e[0],s=t>n?t:n;return`
|
|
var<workgroup> mm_Asub1 : array<array<f32, ${s}>, ${t}>;
|
|
var<workgroup> mm_Bsub1 : array<array<f32, ${n}>, ${s}>;
|
|
var<workgroup> mm_Asub2 : array<array<f32, ${s}>, ${t}>;
|
|
var<workgroup> mm_Bsub2 : array<array<f32, ${n}>, ${s}>;
|
|
|
|
// If the output size is small for matrix multiplication, avoid to use vec4
|
|
// and handle some elements per thread to optimally utilize the ALU.
|
|
// Introduces two shared memory buffers, some logical threads could handle
|
|
// arithmetic operations and others handle IO operations between barrier api,
|
|
// makes ALUs and load/store units work simultaneously, could improves
|
|
// the performance.
|
|
${ua()}
|
|
let tileRow = i32(localId.y);
|
|
let tileCol = i32(localId.x);
|
|
let globalRow = i32(globalId.y);
|
|
let globalCol = i32(globalId.x);
|
|
|
|
// uniforms.dimInner should be greater than 0.
|
|
let numTiles = (uniforms.dimInner - 1) / ${s} + 1;
|
|
var acc = 0.0;
|
|
|
|
var globalColA = tileCol;
|
|
var globalRowB = tileRow;
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
if (t == 0) {
|
|
if (tileRow < ${t}) {
|
|
// Load one tile of A and B into local memory.
|
|
// globalRow is always greater than or equal tileRow.
|
|
mm_Asub1[tileRow][tileCol] =
|
|
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
|
|
globalColA = globalColA + ${s};
|
|
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
|
|
globalRowB = globalRowB + ${s};
|
|
}
|
|
} else {
|
|
if (tileRow < ${t}) {
|
|
// Load one tile of A and B into local memory.
|
|
// globalRow is always greater than or equal tileRow.
|
|
mm_Asub1[tileRow][tileCol] =
|
|
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
|
|
globalColA = globalColA + ${s};
|
|
mm_Bsub1[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
|
|
globalRowB = globalRowB + ${s};
|
|
} else {
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < ${s}; k = k + 1) {
|
|
let subRow = tileRow - ${t};
|
|
if (subRow < 0) {
|
|
continue;
|
|
}
|
|
acc = acc + mm_Asub2[subRow][k] * mm_Bsub2[k][tileCol];
|
|
}
|
|
}
|
|
}
|
|
workgroupBarrier();
|
|
if (t != 0) {
|
|
t = t + 1;
|
|
}
|
|
|
|
if (t < numTiles) {
|
|
if (tileRow < ${t}) {
|
|
// Load one tile of A and B into local memory.
|
|
// globalRow is always greater than or equal tileRow.
|
|
mm_Asub2[tileRow][tileCol] =
|
|
mm_readA((globalRow - tileRow) / 2 + tileRow, globalColA, globalId);
|
|
globalColA = globalColA + ${s};
|
|
mm_Bsub2[tileRow][tileCol] = mm_readB(globalRowB, globalCol, globalId);
|
|
globalRowB = globalRowB + ${s};
|
|
} else {
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < ${s}; k = k + 1) {
|
|
let subRow = tileRow - ${t};
|
|
if (subRow < 0) {
|
|
continue;
|
|
}
|
|
acc = acc + mm_Asub1[subRow][k] * mm_Bsub1[k][tileCol];
|
|
}
|
|
}
|
|
}
|
|
workgroupBarrier();
|
|
}
|
|
let writeCol = (globalRow - tileRow) / 2 + tileRow - ${t};
|
|
if (tileRow >= ${t} && writeCol >= 0) {
|
|
mm_write(writeCol, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var Lue=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${this.activation}`}getUserCode(){let e=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimInner))) {
|
|
return A.numbers[batch * batchASize + row * uniforms.dimInner + col];
|
|
}
|
|
return 0.0;`,t=`if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
|
|
return B.numbers[batch * batchBSize + row * uniforms.dimBOuter + col];
|
|
}
|
|
return 0.0;`,n="",s="";if(this.activation){let o=da(this.activation,!1);this.hasPreluActivationWeights?n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${o}
|
|
}`:n=`fn activation(a : f32, outCoord : vec3<i32>) -> f32 {
|
|
${o}
|
|
}`,s="value = activation(value, outCoord);"}let r=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${n}
|
|
|
|
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
let batchASize = uniforms.aShape[1] * uniforms.aShape[2];
|
|
let batch = i32(globalId.z);
|
|
${e}
|
|
}
|
|
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
let batch = i32(globalId.z);
|
|
let batchBSize = uniforms.bShape[1] * uniforms.bShape[2];
|
|
${t}
|
|
}
|
|
fn mm_write(row : i32, col : i32, valueIn : f32, globalId : vec3<u32>) {
|
|
if (coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimAOuter, uniforms.dimBOuter))) {
|
|
let batch = i32(globalId.z);
|
|
let outCoord = vec3<i32>(batch, row, col);
|
|
var value = valueIn;
|
|
${r}
|
|
${s}
|
|
setOutput(batch, row, col, value);
|
|
}
|
|
}
|
|
${zue(this.workGroupSize)}
|
|
`}};function qe(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Bue={kernelName:Li,backendName:"webgpu",kernelFunc:qe};function Fx({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),b=ol.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[y,d,h]:[y,h,d],C=s?[x,f,p]:[x,p,f],S=qe({inputs:{x:e},backend:r,attrs:{shape:w}}),E=qe({inputs:{x:t},backend:r,attrs:{shape:C}}),R=[S,E],P=Math.max(y,x),_=d%4==0&&f%4==0&&!n&&!s&&f>=32,$;h*f<=32?$=new Mue([P,h,f],n,s,a,l,o):!n&&!s&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?$=new Lue(w,C,[P,h,f],a,l,o):_?$=new Fue(w,[P,h,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):$=new E4(w,[P,h,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,l,o);let T=[S,E];a&&T.push(a),o&&T.push(o);let O=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],V=r.runWebGPUProgram($,T,e.dtype,O),K=qe({inputs:{x:V},backend:r,attrs:{shape:b}});R.push(V);for(let z of R)r.disposeData(z.dataId);return K}function Wue(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return Fx({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var Vue={kernelName:Io,backendName:"webgpu",kernelFunc:Wue},R4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
|
|
fn binaryOpComplex(
|
|
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
|
|
${Rp(this.op,!1)}
|
|
}
|
|
|
|
${Ke()}
|
|
if(index < uniforms.size) {
|
|
let areal = getARealAtOutCoordsByGlobalIndex(index);
|
|
let aimag = getAImagAtOutCoordsByGlobalIndex(index);
|
|
let breal = getBRealAtOutCoordsByGlobalIndex(index);
|
|
let bimag = getBImagAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
}
|
|
`}},Uue=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=He(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${e}_${this.lastDimensionSize}_${this.useSharedMemoryWithB}`}getUserCode(){let e=this.lastDimensionSize>1?`coords[${this.outputShape.length-1}]`:"0",t=this.useSharedMemoryWithB?`let a = getAAtOutCoordsByCoords(coords);
|
|
let b = sharedBuf[${e}];`:`let a = sharedBuf[${e}];
|
|
let b = getBAtOutCoordsByCoords(coords);`;return`
|
|
fn binaryOperation(a : f32, b : f32) -> f32 {
|
|
${Rp(this.op,!1)}
|
|
}
|
|
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
|
|
${Ke()}
|
|
|
|
// Fill in the shared memory buffer. Here we need a loop to make sure
|
|
// that all data in A|B are uploaded when |sharedMemorySize| is larger
|
|
// than work group size.
|
|
for(var localIndex = i32(localId.x); localIndex < ${this.lastDimensionSize}; localIndex = localIndex + ${this.workGroupSize[0]}) {
|
|
sharedBuf[localIndex] = f32(${this.useSharedMemoryWithB?"B":"A"}.numbers[localIndex]);
|
|
}
|
|
workgroupBarrier();
|
|
|
|
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
if(flatIndex < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(flatIndex);
|
|
|
|
${t}
|
|
setOutputFlat(flatIndex, binaryOperation(a, b));
|
|
}
|
|
}
|
|
}
|
|
`}},Gue=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
|
|
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
|
|
${Rp(this.op,this.isVec4)}
|
|
}
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
let b = getBAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}},_4=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
|
|
fn binaryOperation(a : f32, b : f32) -> f32 {
|
|
${Rp(this.op,!1)}
|
|
}
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
let b = getBAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}};function D4(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4==0)return new Gue(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Uue(e,t,n,a):new _4(e,t,n)}function ar(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Hue={kernelName:Za,backendName:"webgpu",kernelFunc:ar};function vc(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=ar({inputs:{x:s},backend:n}),l=ar({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var jue={kernelName:cd,backendName:"webgpu",kernelFunc:vc},_p=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
|
|
fn unaryOperation(a : f32) -> f32 {
|
|
${bc(this.op,!1)}
|
|
}
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, unaryOperation(a));
|
|
}
|
|
}
|
|
`}};function Cn({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let c=o.tensorMap.get(a.dataId),u=t(c.values,i);return o.makeTensorInfo(a.shape,i,u)}let l=new _p(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function qn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(n&&o.dtype==="complex64"){let d=l.tensorMap.get(o.dataId),p=l.tensorMap.get(i.dataId),h,f;if(e!==jt.MUL)[h,f]=[[d.complexTensorInfos.real,p.complexTensorInfos.real],[d.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:o.shape},b={dataId:x.dataId,dtype:x.dtype,shape:i.shape},w=D4(e,o.shape,i.shape);return l.runWebGPUProgram(w,[A,b],Ln(y.dtype,x.dtype))});else{let g=new R4(jt.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),y=new R4(jt.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),x=[{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(y,x,"float32")}let m=vc({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let c=s||Ln(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||l.shouldExecuteOnCPU([o,i]))&&t!=null){let d=l.tensorMap.get(o.dataId).values,p=l.tensorMap.get(i.dataId).values,h=o.dtype==="string"?N.fromUint8ToStringArray(d):d,f=o.dtype==="string"?N.fromUint8ToStringArray(p):p,[m,g]=t(o.shape,i.shape,h,f,c);return l.makeTensorInfo(g,c,m)}let u=D4(e,o.shape,i.shape);return l.runWebGPUProgram(u,[o,i],c)}}var{addImpl:que,ceilImpl:Xue,concatImpl:Kue,equalImpl:Zue,expImpl:Yue,expm1Impl:Jue,floorImpl:Que,gatherNdImpl:ece,gatherV2Impl:tce,greaterEqualImpl:nce,greaterImpl:sce,lessEqualImpl:rce,lessImpl:ace,logImpl:oce,maxImpl:ice,maximumImpl:lce,minimumImpl:uce,multiplyImpl:cce,negImpl:dce,notEqualImpl:pce,prodImpl:hce,rangeImpl:fce,rsqrtImpl:mce,simpleAbsImpl:gce,sliceImpl:yce,stridedSliceImpl:Ace,stringNGramsImpl:xce,subImpl:bce,tileImpl:vce,topKImpl:wce,transposeImpl:kce,uniqueImpl:Jye}=Nm,Sce=Cn({opType:wt.ABS,cpuKernelImpl:gce}),Ice={kernelName:mi,backendName:"webgpu",kernelFunc:Sce},Cce=qn({opSnippet:jt.ADD,cpuKernelImpl:que,supportsComplex:!0}),Tce={kernelName:Kr,backendName:"webgpu",kernelFunc:Cce},Nce=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e[0],this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="addN"}getUserCode(){let e=[];this.variableNames.forEach(s=>{e.push(`let v${s} = get${s}AtOutCoordsByCoords(coords);`)});let t=this.variableNames.map(s=>`v${s}`).join(" + ");return`
|
|
${Ke()}
|
|
for (var i = 0; i < ${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
if (flatIndex < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(flatIndex);
|
|
${e.join(`
|
|
`)}
|
|
setOutputFlat(flatIndex, ${t});
|
|
}
|
|
}
|
|
}
|
|
`}};function Ece(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return ar({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>Ln(i,l)),a=s.map(i=>i.shape),o=new Nce(a);return n.runWebGPUProgram(o,s,r)}var Rce={kernelName:Da,backendName:"webgpu",kernelFunc:Ece},$4=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32; infinityValue : f32;",this.size=!0;let s=[t];N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=N.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=`
|
|
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
|
|
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
|
|
`,t=(r,a)=>this.outputShape.length===1?r:`${r}[${a}]`,n=r=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${r}]`;return`
|
|
fn DIV_CEIL(a : u32, b : u32) -> u32 {
|
|
return ((a - 1u) / b + 1u);
|
|
}
|
|
|
|
${e}
|
|
|
|
// In order to get a flattened index into the input tensor, we need to
|
|
// add back the index along the reduced dimension to |outputCoords|.
|
|
// This function outputs the offset to the first value along
|
|
// |axis| and the stride to get the next value of the input along |axis|.
|
|
fn getInputCoordInfo(outputIndex : i32) -> vec2<i32>{
|
|
let outputCoords = getCoordsFromFlatIndex(outputIndex);
|
|
var i = ${this.outputShape.length-1};
|
|
|
|
var stride = 1;
|
|
var inputStride = 1;
|
|
var offset = 0;
|
|
|
|
for (var r = 1; r <= ${this.inputShape.length}; r = r + 1) {
|
|
let length = ${n(`${this.inputShape.length} - r`)};
|
|
if (${this.inputShape.length} - r == uniforms.axis) {
|
|
inputStride = stride;
|
|
} else {
|
|
offset = offset + ${t("outputCoords","i")} * stride;
|
|
i = i - 1;
|
|
}
|
|
stride = stride * length;
|
|
}
|
|
|
|
return vec2<i32>(offset, inputStride);
|
|
}
|
|
|
|
fn getInputIndex(coordInfo : vec2<i32>, index : i32) -> i32{
|
|
return coordInfo[0] + coordInfo[1] * index;
|
|
}
|
|
|
|
${Ke()}
|
|
let outputIndex = index / i32(workGroupSizeX);
|
|
let coordInfo = getInputCoordInfo(outputIndex);
|
|
let Length = ${n("uniforms.axis")};
|
|
|
|
var bestIndex = i32(localId.x);
|
|
var bestValue = uniforms.infinityValue;
|
|
|
|
for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
|
|
k = k + i32(workGroupSizeX)) {
|
|
let candidate = f32(x.numbers[getInputIndex(coordInfo, k)]);
|
|
if (!isNanCustom(candidate) && candidate ${this.op} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = k;
|
|
}
|
|
}
|
|
xBestValues[localId.x] = bestValue;
|
|
xBestIndices[localId.x] = bestIndex;
|
|
workgroupBarrier();
|
|
|
|
var reduceSize = min(u32(Length), workGroupSizeX);
|
|
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
|
|
currentSize = reduceSize / 2u) {
|
|
let interval = DIV_CEIL(reduceSize, 2u);
|
|
if (localId.x < currentSize) {
|
|
let candidate = xBestValues[localId.x + interval];
|
|
if (candidate ${this.op} bestValue) {
|
|
bestValue = candidate;
|
|
xBestValues[localId.x] = bestValue;
|
|
xBestIndices[localId.x] = xBestIndices[localId.x + interval];
|
|
}
|
|
}
|
|
reduceSize = interval;
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (localId.x == 0u && outputIndex < uniforms.size) {
|
|
setOutputFlatI32(outputIndex, xBestIndices[localId.x]);
|
|
}
|
|
}
|
|
`}},_ce=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[16,16,1];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout={x:[0],y:[1]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,1,1]),this.shaderKey="transposeShared"}getUserCode(){return`
|
|
let TILE_DIM = ${this.workGroupSize[0]};
|
|
var<workgroup> tile : array<array<f32, ${this.workGroupSize[0]+1}>, ${this.workGroupSize[0]}>;
|
|
${Tx()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(workgroup_id)]] workgroupId : vec3<u32>) {
|
|
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
|
|
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
|
|
let width = uniforms.outShape[0];
|
|
let height = uniforms.outShape[1];
|
|
if (x < width && y < height) {
|
|
tile[localId.y][localId.x] =
|
|
A.numbers[y * width + x];
|
|
}
|
|
workgroupBarrier();
|
|
|
|
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
|
|
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
|
|
if (x < height && y < width) {
|
|
setOutputFlat((y * height + x), tile[localId.x]
|
|
[localId.y]);
|
|
}
|
|
}
|
|
`}},Dce=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=wn(this.outputShape.length),t=$ce(this.newDim);return`
|
|
${Ke()}
|
|
|
|
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
if(flatIndex < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(flatIndex);
|
|
setOutputFlat(flatIndex, A.numbers[getFlatIndex${this.outputShape.length}D(
|
|
${e}(${t}), uniforms.aShape)]);
|
|
}
|
|
}
|
|
}
|
|
`}};function $ce(e){let t=e.length;if(t>4)throw Error(`Transpose for rank ${t} is not yet supported`);let n=new Array(t);for(let s=0;s<e.length;s++)n[e[s]]=`resRC[${s}]`;return n.join()}function Ol(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];if(n.shouldExecuteOnCPU([r])){let d=o.tensorMap.get(r.dataId).values,p=kce(d,r.shape,r.dtype,a,l);return n.makeTensorInfo(l,r.dtype,p)}if(r.shape.length===2&&v.arraysEqual(a,[1,0])){let u=new _ce(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}let c=new Dce(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}var Fce={kernelName:ko,backendName:"webgpu",kernelFunc:Ol};function Pce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Ol({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=new $4(l.shape,o[0],"max"),d=[{type:"int32",data:[o[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var Oce={kernelName:$a,backendName:"webgpu",kernelFunc:Pce};function Mce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Ol({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=new $4(l.shape,o[0],"min"),d=[{type:"int32",data:[o[0]]},{type:"float32",data:[Number.POSITIVE_INFINITY]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var zce={kernelName:du,backendName:"webgpu",kernelFunc:Mce},F4=class{constructor(e,t){this.variableNames=["x"],this.uniforms="stride : vec2<i32>; pad : vec2<i32>; dilation : vec2<i32>; convDims : vec2<i32>; filterDims : vec2<i32>;",this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`pool2D_${t}`,this.poolType=t}getUserCode(){let e="resultValue = max(value, resultValue);";this.poolType==="avg"&&(e="resultValue = resultValue + value; count = count + 1.0;");let t="resultValue";return this.poolType==="avg"&&(t="resultValue / count"),`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let batch = coords[0];
|
|
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
|
|
let xRCorner = xRCCorner.x;
|
|
let xCCorner = xRCCorner.y;
|
|
|
|
var resultValue = ${this.poolType==="avg"?"0.0":"-1.0 / pow(10.0, -20.0)"};
|
|
var count = 0.0;
|
|
|
|
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + uniforms.dilation.x) {
|
|
let xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= uniforms.convDims.x) {
|
|
continue;
|
|
}
|
|
|
|
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + uniforms.dilation.y) {
|
|
let xC = xCCorner + wC;
|
|
if (xC < 0 || xC >= uniforms.convDims.y) {
|
|
continue;
|
|
}
|
|
|
|
let value = getX(batch, xR, xC, coords[3]);
|
|
${e}
|
|
}
|
|
}
|
|
|
|
setOutputFlat(index, ${t});
|
|
}
|
|
}
|
|
`}},P4=class{constructor(e){this.variableNames=["x"],this.uniforms="stride : vec2<i32>;",this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e.outShape,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let batch = coords[0];
|
|
let d = coords[3];
|
|
|
|
let xRCCorner = coords.yz * uniforms.stride;
|
|
let xRCorner = xRCCorner.x;
|
|
let xCCorner = xRCCorner.y;
|
|
|
|
let value = getX(batch, xRCorner, xCCorner, d);
|
|
setOutputFlat(index, value);
|
|
}
|
|
}
|
|
`}};function Lce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=N.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return ar({inputs:{x:r},backend:n});let d,p=[{type:"int32",data:[u.strideHeight,u.strideWidth]}];return u.filterHeight===1&&u.filterWidth===1?d=new P4(u):(d=new F4(u,"avg"),p.push({type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]})),n.runWebGPUProgram(d,[r],r.dtype,p)}var Bce={kernelName:Fa,backendName:"webgpu",kernelFunc:Lce};function Wce(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Fx({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var Vce={kernelName:Pa,backendName:"webgpu",kernelFunc:Wce},Uce=class{constructor(e,t){this.variableNames=["source"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.rank=t.length,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${wn(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=wn(this.rank),t=Gce(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Px[a]} = uniforms.start[${a}] + coords.${Px[a]};`),`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
var sourceLoc : ${e};
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
${n.join(`
|
|
`)}
|
|
setOutputFlat(index, getSource(${t}));
|
|
}
|
|
}
|
|
`}},Px=["x","y","z","w","u","v"];function Gce(e){if(e===1)return"sourceLoc";if(e<=6)return Px.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function wc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Mt.parseSliceParams(r,a,o);if(Mt.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.tensorMap.get(r.dataId),p=yce(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}if(v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);let c=new Uce(i,l),u=[{type:"int32",data:i}];return n.runWebGPUProgram(c,[r],r.dtype,u)}var Hce={kernelName:Gi,backendName:"webgpu",kernelFunc:wc},jce=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((x,A)=>x*A),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=[],f=qe({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Ol({inputs:{x:f},backend:n,attrs:{perm:c}}),g=qe({inputs:{x:m},backend:n,attrs:{shape:u}}),y=wc({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeData(x.dataId)),y},qce={kernelName:gi,backendName:"webgpu",kernelFunc:jce},O4=qn({opSnippet:jt.NOT_EQUAL,dtype:"bool",cpuKernelImpl:pce}),Xce={kernelName:Di,backendName:"webgpu",kernelFunc:O4};function Dp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return ar({inputs:{x:r.complexTensorInfos.real},backend:n})}var Kce={kernelName:xd,backendName:"webgpu",kernelFunc:Dp};function Zce(e,t){let n=new _p(e.shape,wt.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Ox(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return ar({inputs:{x:r},backend:n});let o=Gt(r.shape),i=Ox({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=vc({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(r.dtype==="complex64"){let o=Dp({inputs:{input:r},backend:n}),i=Ox({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=ar({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Zce(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=O4({inputs:{a:r,b:o},backend:n});return n.disposeData(o.dataId),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Yce={kernelName:Oa,backendName:"webgpu",kernelFunc:Ox},Jce=Cn({opType:wt.CEIL,cpuKernelImpl:Xue}),Qce={kernelName:Ma,backendName:"webgpu",kernelFunc:Jce},ede=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workPerThread=4,this.workGroupSize=[64,1,1],this.isVec4=!0,this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
|
|
${Ke()}
|
|
if(index < uniforms.size) {
|
|
let value = getAAtOutCoordsByGlobalIndex(index);
|
|
var clampedValue : vec4<f32>;
|
|
for (var i = 0; i < 4; i = i + 1) {
|
|
if (isNanCustom(value[i])) {
|
|
clampedValue[i] = value[i];
|
|
} else {
|
|
clampedValue[i] = clamp(value[i], uniforms.minVal, uniforms.maxVal);
|
|
}
|
|
}
|
|
|
|
setOutputFlat(index, clampedValue);
|
|
}
|
|
}
|
|
`}},tde=class{constructor(e){this.variableNames=["A"],this.uniforms="minVal : f32; maxVal : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
|
|
${Ke()}
|
|
if(index < uniforms.size) {
|
|
let value = getAAtOutCoordsByGlobalIndex(index);
|
|
if (isNanCustom(value)) {
|
|
setOutputFlat(index, value);
|
|
return;
|
|
}
|
|
setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal));
|
|
}
|
|
}
|
|
`}};function nde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4==0?i=new ede(r.shape):i=new tde(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var sde={kernelName:Zr,backendName:"webgpu",kernelFunc:nde},rde=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.offsetLength=e.length-1;for(let t=0;t<this.offsetLength;t++)this.uniforms+=`offset${t} : i32;`;this.shaderKey="concat"}getUserCode(){let e=[];if(this.offsetLength>0){e.push("if (yC < uniforms.offset0){ setOutput(coords.x, coords.y, getT0(yR, yC)); }");for(let r=1;r<this.offsetLength;r++)e.push(`elseif (yC < uniforms.offset${[r]}){ setOutput(coords.x, coords.y, getT${r}(yR, yC - uniforms.offset${r-1})); }`);let n=this.offsetLength,s=this.offsetLength-1;e.push(`else { setOutput(coords.x, coords.y, getT${n}(yR, yC - uniforms.offset${s})); }`)}else e.push("setOutput(coords.x, coords.y, getT0(yR, yC));");return`
|
|
${Ke()}
|
|
for(var i = 0; i < ${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
if(flatIndex < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(flatIndex);
|
|
let yR = coords.x;
|
|
let yC = coords.y;
|
|
|
|
${e.join(`
|
|
`)}
|
|
}
|
|
}
|
|
}
|
|
`}};function s0(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return ar({inputs:{x:r.complexTensorInfos.imag},backend:n})}var ade={kernelName:md,backendName:"webgpu",kernelFunc:s0};function Mx(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(x=>Dp({inputs:{input:x},backend:n})),f=e.map(x=>s0({inputs:{input:x},backend:n})),m=Mx(h,t,n),g=Mx(f,t,n),y=vc({inputs:{real:m,imag:g},backend:n});return h.forEach(x=>n.disposeData(x.dataId)),f.forEach(x=>n.disposeData(x.dataId)),n.disposeData(m.dataId),n.disposeData(g.dataId),y}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let h=e.map(b=>{let w=v.sizeFromShape(b.shape.slice(t));return qe({inputs:{x:b},backend:n,attrs:{shape:[-1,w]}})}),f=h.map(b=>({vals:n.readSync(b.dataId),shape:b.shape})),m=N.computeOutShape(h.map(b=>b.shape),1),g=h[0].shape[0]===1,y=Kue(f,m,s,g),x=N.computeOutShape(e.map(b=>b.shape),t),A=n.makeTensorInfo(x,s,y);return h.forEach(b=>n.disposeData(b.dataId)),A}let{tensors2D:a,outShape:o}=ode(e,t,n),i=a.map(h=>h.shape),l=new rde(i),c=[],u=new Array(i.length-1);if(u.length>0){u[0]=i[0][1],c.push({type:"int32",data:[u[0]]});for(let h=1;h<u.length;h++)u[h]=u[h-1]+i[h][1],c.push({type:"int32",data:[u[h]]})}let d=n.runWebGPUProgram(l,a,a[0].dtype,c);a.forEach(h=>n.disposeData(h.dataId));let p=qe({inputs:{x:d},backend:n,attrs:{shape:o}});return n.disposeData(d.dataId),p}function ode(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>qe({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function M4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=N.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return ar({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return N.assertParamsConsistent(l,a),Mx(i,a,n)}var ide={kernelName:yi,backendName:"webgpu",kernelFunc:M4},lde=class{constructor(e,t){this.variableNames=["A"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; outWidth : i32; itemsPerBlockRow : i32;
|
|
inChannels : i32;`,this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.isChannelsLast=t,this.shaderKey=`im2col_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?0:1,t=this.isChannelsLast?1:2;return`
|
|
${Ke()}
|
|
|
|
for(var i = 0; i<${this.workPerThread}; i = i + 1) {
|
|
let flatIndex = index * ${this.workPerThread} + i;
|
|
|
|
let rc = getCoordsFromFlatIndex(flatIndex);
|
|
|
|
if(flatIndex < uniforms.size) {
|
|
let blockIndex = rc[0];
|
|
let pos = rc[1];
|
|
|
|
let offsetY = blockIndex / uniforms.outWidth * uniforms.stride[1] - uniforms.pad[1];
|
|
let d0 = offsetY + uniforms.dilation[1] * pos / uniforms.itemsPerBlockRow;
|
|
var value = 0.0;
|
|
if(d0 < uniforms.aShape[${e}] && d0 >= 0) {
|
|
let offsetX = (blockIndex % uniforms.outWidth) * uniforms.stride[0] -
|
|
uniforms.pad[0];
|
|
let d1 = offsetX + uniforms.dilation[0] * ((pos %
|
|
uniforms.itemsPerBlockRow) / uniforms.inChannels);
|
|
let ch = pos % uniforms.inChannels;
|
|
if(d1 < uniforms.aShape[${t}] && d1 >= 0) {
|
|
value = getA(d0, d1, ch);
|
|
}
|
|
}
|
|
setOutputFlat(flatIndex, value);
|
|
}
|
|
}
|
|
}
|
|
`}};function z4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=n.dataFormat==="channelsLast",u=!1,d=!1,p=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],h=qe({inputs:{x:e},backend:s,attrs:{shape:[1,p,n.inChannels]}}),f=qe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),m=Fx({a:h,b:f,transposeA:u,transposeB:d,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=qe({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function ude({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,strideWidth:d,strideHeight:p,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:x}=n,A=x==="channelsLast",b=l*c*u,w=m*f,C=[w,b],S=!1,E=!1,R=[],P=qe({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),_=qe({inputs:{x:t},backend:s,attrs:{shape:[1,b,-1]}});R.push(P),R.push(_);let $=new lde(C,A),T=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[d,p]},{type:"int32",data:[g,y]},{type:"int32",data:[f]},{type:"int32",data:[u*l]},{type:"int32",data:[u]}],O=s.runWebGPUProgram($,[P],P.dtype,T),V=qe({inputs:{x:O},backend:s,attrs:{shape:[1,C[0],C[1]]}});R.push(O),R.push(V);let K=[1,C[0],C[1]],z=new E4(K,[1,w,n.outChannels],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),S,E),j=K[1],W=K[2],Q=n.outChannels,te=[{type:"int32",data:[j]},{type:"int32",data:[Q]},{type:"int32",data:[W]}],ae=s.runWebGPUProgram(z,[V,_],V.dtype,te),Z=A?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],oe=qe({inputs:{x:ae},backend:s,attrs:{shape:Z}});R.push(ae);for(let re of R)s.disposeData(re.dataId);return oe}var L4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.uniforms=`filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
|
|
dimAOuter : i32; dimBOuter : i32; dimInner : i32;`,this.isVec4=!0,this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=[8,8,1];let a=[4,4,1];this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,a),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.hasLeakyreluAlpha=r,this.addBias&&this.variableNames.push("bias"),this.hasPreluActivationWeights&&this.variableNames.push("preluActivationWeights"),this.hasLeakyreluAlpha&&this.variableNames.push("leakyreluAlpha"),[this.fitA,this.fitB]=this.getShapeFit(a),this.shaderKey=`conv2DMMVec4_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(e){let t=this.workGroupSize[1]*e[1],n=this.workGroupSize[0]*e[0],s=n,r=[t,s],a=[s,n],o=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],l=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ca(r,[o,l]),ca(a,[l,i])]}getSampleAWithRemainder(e){return`let flatIndex${e} = getFlatIndex4D(coord, uniforms.xShape);
|
|
let divBy4Remainder${e} = flatIndex${e} % 4;
|
|
let divBy4Index${e} = flatIndex${e} / 4;
|
|
let curData${e} = x.numbers[divBy4Index${e}];
|
|
if (divBy4Remainder${e} == 0) {
|
|
temp = curData${e};
|
|
} else {
|
|
// TODO: This could end up being a redundant load with another one in
|
|
// the same shader invocation. Perhaps there's an opportunity for
|
|
// optimization
|
|
let nextData${e} = x.numbers[divBy4Index${e} + 1];
|
|
if (divBy4Remainder${e} == 1) {
|
|
temp = vec4<f32>(curData${e}.yzw, nextData${e}.x);
|
|
} elseif (divBy4Remainder${e} == 2) {
|
|
temp = vec4<f32>(curData${e}.zw, nextData${e}.xy);
|
|
} elseif (divBy4Remainder${e} == 3) {
|
|
temp = vec4<f32>(curData${e}.w, nextData${e}.xyz);
|
|
}
|
|
}
|
|
`}getUserCode(){let t=N4([4,4,1],this.workGroupSize),r=`let outRow = r / uniforms.outShape[2];
|
|
let outCol = r % uniforms.outShape[2];
|
|
let WRow = c / (uniforms.filterDims[1] * uniforms.xShape[3]);
|
|
let WCol = c / uniforms.xShape[3] % uniforms.filterDims[1];
|
|
let inChCoord = c % uniforms.xShape[3];
|
|
var coord = vec4<i32>(
|
|
batch,
|
|
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
|
|
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
|
|
inChCoord);
|
|
var resData = vec4<f32>(0.0);
|
|
${this.convInfo.inChannels%4===0?`// The bounds checking is always needed since we use it to pad zero for
|
|
// the 'same' padding type.
|
|
if (coordsInBounds4D(coord, uniforms.xShape)) {
|
|
resData = x.numbers[getFlatIndex4D(coord, uniforms.xShape) / 4];
|
|
} else {
|
|
resData = vec4<f32>(0.0); }`:`var temp = vec4<f32>(0.0);
|
|
${this.getSampleAWithRemainder(1)}
|
|
resData = temp;
|
|
if (WCol == (uniforms.filterDims[1] - 1)) {
|
|
coord = vec4<i32>(
|
|
coord.x, coord.y + 1, coord.z + 1 - uniforms.filterDims[1], 0);
|
|
${this.getSampleAWithRemainder(2)}
|
|
if (inChCoord == 0) {
|
|
resData = vec4<f32>(resData.xyz, temp.x);
|
|
} elseif (inChCoord == 1) {
|
|
resData = vec4<f32>(resData.xy, temp.xy);
|
|
} else {
|
|
resData = vec4<f32>(resData.x, temp.xyz);
|
|
}
|
|
}
|
|
`}
|
|
return resData;`,a=this.fitA?`${r}`:`if (r < uniforms.dimAOuter && c < uniforms.dimInner) {
|
|
${r}
|
|
}
|
|
return vec4<f32>(0.0);
|
|
`,o=this.fitB?"return W.numbers[row * uniforms.dimBOuter / 4 + col];":`if(coordsInBounds2D(vec2<i32>(row, col * 4), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
|
|
return W.numbers[row * uniforms.dimBOuter / 4 + col];
|
|
}
|
|
return vec4<f32>(0.0);
|
|
`,i="",l="";if(this.activation){let d=da(this.activation,this.isVec4);if(this.hasPreluActivationWeights)i=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${d}
|
|
}`;else{if(this.hasLeakyreluAlpha)throw i=`fn activation(a: vec4<f32>) -> vec4<f32> {
|
|
let b = getLeakyreluAlphaAtOutCoords();
|
|
${d}
|
|
}`,new Error("Leakyrelu is not supported.");i=`
|
|
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
|
|
${d}
|
|
}`}l="value = activation(value, outCoord);"}let c=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${i}
|
|
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
|
|
let r = row;
|
|
let c = col * 4;
|
|
var batch = i32(globalId.z);
|
|
${a}
|
|
}
|
|
|
|
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> vec4<f32> {
|
|
${o}
|
|
}
|
|
|
|
fn mm_write(row : i32, col : i32, valueInput : vec4<f32>, globalId : vec3<u32>) {
|
|
var batch = i32(globalId.z);
|
|
var value = valueInput;
|
|
if (row < uniforms.dimAOuter && col * 4 < uniforms.dimBOuter)
|
|
{
|
|
let outCoord = vec4<i32>(
|
|
batch,
|
|
row / uniforms.outShape[2],
|
|
row % uniforms.outShape[2],
|
|
col * 4);
|
|
${c}
|
|
${l}
|
|
setOutput(outCoord[0], outCoord[1], outCoord[2], outCoord[3],
|
|
value);
|
|
}
|
|
}
|
|
${t}
|
|
`}},B4=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Nx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Rx(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ca(s,[a,i]),ca(r,[i,o])]}getUserCode(){let e=$x(this.elementsPerThread,this.workGroupSize),t=`
|
|
let outRow = row / uniforms.outShape[2];
|
|
let outCol = row % uniforms.outShape[2];
|
|
|
|
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
|
|
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
|
|
let coord = vec4<i32>(
|
|
batch,
|
|
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
|
|
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
|
|
col % uniforms.xShape[3]);
|
|
// The bounds checking is always needed since we use it to pad zero for the
|
|
// 'same' padding type.
|
|
if(coordsInBounds4D(coord, uniforms.xShape)) {
|
|
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
|
|
}
|
|
return 0.0;`,n=this.fitA?`${t}`:`if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
|
|
${t}
|
|
}
|
|
return 0.0;
|
|
`,s=this.fitB?"return W.numbers[row * uniforms.dimBOuter + col];":`if(coordsInBounds2D(vec2<i32>(row, col), vec2<i32>(uniforms.dimInner, uniforms.dimBOuter))) {
|
|
return W.numbers[row * uniforms.dimBOuter + col];
|
|
}
|
|
return 0.0;
|
|
`,r="",a="";if(this.activation){let l=da(this.activation,!1);this.hasPreluActivationWeights?r=`fn activation(a: f32, outCoord : vec4<i32>) -> f32 {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${l}
|
|
}`:r=`
|
|
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
|
|
${l}
|
|
}
|
|
`,a="value = activation(value, outCoord);"}let o=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${r}
|
|
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
var batch = i32(globalId.z);
|
|
${n}
|
|
}
|
|
|
|
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
${s}
|
|
}
|
|
|
|
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
|
|
var batch = i32(globalId.z);
|
|
var value = valueInput;
|
|
let outCoord = vec4<i32>(
|
|
batch,
|
|
row / uniforms.outShape[2],
|
|
row % uniforms.outShape[2],
|
|
col);
|
|
${o}
|
|
${a}
|
|
result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
|
|
}
|
|
${e}
|
|
`}},W4=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=da(this.activation);this.hasPreluActivationWeights?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32{
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${r}
|
|
}`:e=`
|
|
fn activation(a : f32, outCoord : vec4<i32>) -> f32{
|
|
${r}
|
|
}
|
|
`,t="value = activation(value, outCoord);"}let n=this.addBias?"value = value + getBiasAtOutCoordsByCoords(outCoord);":"";return`
|
|
${e}
|
|
fn readInp(batch : i32, row : i32, col : i32, chan : i32) -> f32 {
|
|
let coord = vec4<i32>(batch, row, col, chan);
|
|
if(coordsInBounds4D(coord, uniforms.xShape)) {
|
|
return getX(batch, row, col, chan);
|
|
}
|
|
return 0.0;
|
|
}
|
|
|
|
fn readFilt(row : i32, col : i32, xChannel : i32, outChannel : i32) -> f32{
|
|
let coord = vec4<i32>(row, col, xChannel, outChannel);
|
|
if(coordsInBounds4D(coord, uniforms.wShape)) {
|
|
return getW(row, col, xChannel, outChannel);
|
|
}
|
|
return 0.0;
|
|
}
|
|
|
|
fn writeResult(batch : i32, row : i32, col : i32, chan : i32, value : f32) {
|
|
let coord = vec4<i32>(batch, row, col, chan);
|
|
if (coordsInBounds4D(coord, uniforms.outShape)) {
|
|
${n}
|
|
${t}
|
|
setOutput(batch, row, col, chan, value);
|
|
}
|
|
}
|
|
|
|
${ua()}
|
|
let coords = getOutputCoords();
|
|
let batch = coords[0];
|
|
let outChannel = coords[3];
|
|
|
|
var acc = 0.0;
|
|
|
|
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
|
|
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
|
|
for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
|
|
let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
|
|
let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
|
|
let v = readInp(batch, coordRow, coordCol, xChannel);
|
|
let f = readFilt(row, col, xChannel, outChannel);
|
|
acc = acc + v * f;
|
|
}
|
|
}
|
|
}
|
|
|
|
writeResult(batch, coords[1], coords[2], outChannel, acc);
|
|
}
|
|
`}};function cde(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dataFormat:l,dilations:c,dimRoundingMode:u}=n,d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d);if(p.filterHeight===1&&p.filterWidth===1&&p.dilationHeight===1&&p.dilationWidth===1&&p.strideHeight===1&&p.strideWidth===1&&(p.padInfo.type==="SAME"||p.padInfo.type==="VALID"))return z4({x:r,filter:a,convInfo:p,backend:s});if(Y().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&r.shape[0]===1)return ude({x:r,filter:a,convInfo:p,backend:s});let h,f=[p.padInfo.top,p.padInfo.left],m=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[...f]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.dilationHeight,p.dilationWidth]}],g=Y().getBool("WEBGPU_USE_NAIVE_CONV2D");if(g?h=new W4(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new L4(p):h=new B4(p),!g){let y=p.outShape[1]*p.outShape[2],x=p.outShape[3],A=p.filterHeight*p.filterWidth*p.inShape[3];m.push({type:"int32",data:[y]},{type:"int32",data:[x]},{type:"int32",data:[A]})}return s.runWebGPUProgram(h,[r,a],r.dtype,m)}var dde={kernelName:za,backendName:"webgpu",kernelFunc:cde},pde=class{constructor(e){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.inShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Nx(this.dispatchLayout,this.outputShape),this.elementsPerThread=Rx(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),this.shaderKey=`conv2DDerInputMM_${this.elementsPerThread}`}getUserCode(){return`
|
|
fn mm_readA(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
var batch = i32(globalId.z);
|
|
if (row < uniforms.dimAOuter && col < uniforms.dimInner) {
|
|
|
|
let outRow = row / uniforms.outShape[2];
|
|
let outCol = row % uniforms.outShape[2];
|
|
|
|
let WRow = col / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
|
|
let WCol = col / uniforms.outBackprop[3] % uniforms.filterDims[1];
|
|
let xR = f32(outRow - uniforms.pads[0] + WRow) / f32(uniforms.stride[0]);
|
|
let xC = f32(outCol - uniforms.pads[1] + WCol) / f32(uniforms.stride[1]);
|
|
if (xR < 0.0 || xR >= f32(uniforms.outBackprop[1]) || fract(xR) > 0.0) {
|
|
return 0.0;
|
|
}
|
|
if (xC < 0.0 || xC >= f32(uniforms.outBackprop[2]) || fract(xC) > 0.0) {
|
|
return 0.0;
|
|
}
|
|
let coord = vec4<i32>(
|
|
batch,
|
|
i32(xR),
|
|
i32(xC),
|
|
col % uniforms.outBackprop[3]);
|
|
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
|
|
}
|
|
return 0.0;
|
|
}
|
|
|
|
fn mm_readB(row : i32, col : i32, globalId : vec3<u32>) -> f32 {
|
|
let coordX = uniforms.filterDims.x - 1 -
|
|
row / (uniforms.filterDims[1] * uniforms.outBackprop[3]);
|
|
let coordY = uniforms.filterDims.y - 1 -
|
|
(row / uniforms.outBackprop[3]) % uniforms.filterDims[1];
|
|
if (row < uniforms.dimInner && col < uniforms.dimBOuter &&
|
|
coordX >= 0 && coordY >= 0) {
|
|
let coord = vec4<i32>(coordX, coordY, col,
|
|
row % uniforms.outBackprop[3]);
|
|
return W.numbers[getFlatIndex4D(coord, uniforms.wShape)];
|
|
}
|
|
return 0.0;
|
|
}
|
|
|
|
fn mm_write(row : i32, col : i32, valueInput : f32, globalId : vec3<u32>) {
|
|
var batch = i32(globalId.z);
|
|
var value = valueInput;
|
|
let outCoord = vec4<i32>(
|
|
batch,
|
|
row / uniforms.outShape[2],
|
|
row % uniforms.outShape[2],
|
|
col);
|
|
result.numbers[getFlatIndex4D(outCoord, uniforms.outShape)] = value;
|
|
}
|
|
|
|
${$x(this.elementsPerThread,this.workGroupSize)}
|
|
`}},hde=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
|
|
${Ke()} {
|
|
if(index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let batch = coords[0];
|
|
let d1 = coords[${n}];
|
|
|
|
let dyCorner = vec2<i32>(coords[${e}]), coords[${t}]) - uniforms.pads;
|
|
let dyRCorner = dyCorner.x;
|
|
let dyCCorner = dyCorner.y;
|
|
|
|
// Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
var dotProd = 0.0;
|
|
for (var wR = 0; wR < uniforms.filterDims.x; wR = wR + 1) {
|
|
let dyR = (f32(dyRCorner) + f32(wR)) / f32(uniforms.stride.x);
|
|
let wRPerm = uniforms.filterDims.x - 1 - wR;
|
|
if (dyR < 0.0 || dyR >= f32(uniforms.outBackprop[1]) || fract(dyR) > 0.0 ||
|
|
wRPerm < 0) {
|
|
continue;
|
|
}
|
|
let idyR = dyR;
|
|
|
|
for (var wC = 0; wC < uniforms.filterDims.y; wC = wC + 1) {
|
|
let dyC = (f32(dyCCorner) + f32(wC)) / f32(uniforms.stride.y);
|
|
let wCPerm = uniforms.filterDims.y - 1 - wC;
|
|
if (dyC < 0.0 || dyC >= f32(uniforms.outBackprop[2]) ||
|
|
fract(dyC) > 0.0 || wCPerm < 0) {
|
|
continue;
|
|
}
|
|
let idyC = dyC;
|
|
|
|
for (var d2 = 0; d2 < uniforms.outBackprop[3]; d2 = d2 + 1) {
|
|
if (${this.isChannelsLast}) {
|
|
let xValue = getDy(batch, idyR, idyC, d2);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
} else {
|
|
let xValue = getDy(batch, d2, idyR, idyC);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutputFlat(index, dotProd);
|
|
}
|
|
}
|
|
`}};function fde(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=N.convertConv2DDataFormat(c),p=N.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Y().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new hde(p);else{f=new pde(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var mde={kernelName:La,backendName:"webgpu",kernelFunc:fde},gde=Cn({opType:wt.COS}),yde={kernelName:Ba,backendName:"webgpu",kernelFunc:gde},Ade=Cn({opType:wt.COSH}),xde={kernelName:Wa,backendName:"webgpu",kernelFunc:Ade},bde=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${this.methodId}_${this.cropHeightBiggerThan1}_${this.cropWidthBiggerThan1}`}getUserCode(){let[e,t]=["f32(uniforms.imageShape[1] - 1)","f32(uniforms.imageShape[2] - 1)"],[n,s,r]=this.cropHeightBiggerThan1?[`(${e} / f32(uniforms.outShape[1] - 1))`,"(y2-y1) * height_ratio",`y1*${e} + f32(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${e}`],[a,o,i]=this.cropWidthBiggerThan1?[`(${t} / f32(uniforms.outShape[2] - 1))`,"(x2-x1) * width_ratio",`x1*${t} + f32(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${t}`];return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let height_ratio = f32(${n});
|
|
let width_ratio = f32(${a});
|
|
let b = coords[0];
|
|
let y = coords[1];
|
|
let x = coords[2];
|
|
let d = coords[3];
|
|
// get box vals
|
|
let y1 = getBoxes(b, 0);
|
|
let x1 = getBoxes(b, 1);
|
|
let y2 = getBoxes(b, 2);
|
|
let x2 = getBoxes(b, 3);
|
|
// get image in batch index
|
|
let bInd = i32(round(getBoxInd(b)));
|
|
if(bInd < 0 || bInd >= uniforms.outShape[0]) {
|
|
return;
|
|
}
|
|
let height_scale = ${s};
|
|
let width_scale = ${o};
|
|
let in_y = ${r};
|
|
if( in_y < 0.0 || in_y > ${e} ) {
|
|
setOutputFlat(index, uniforms.extrapolationValue);
|
|
return;
|
|
}
|
|
let in_x = ${i};
|
|
if( in_x < 0.0 || in_x > ${t} ) {
|
|
setOutputFlat(index, uniforms.extrapolationValue);
|
|
return;
|
|
}
|
|
let sourceFracIndexCR = vec2<f32>(in_x,in_y);
|
|
if(${this.methodId} == 1) {
|
|
// Compute the four integer indices.
|
|
let sourceFloorCR = vec2<i32>(sourceFracIndexCR);
|
|
let sourceCeilCR = vec2<i32>(ceil(sourceFracIndexCR));
|
|
let topLeft = getImage(bInd, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
let bottomLeft = getImage(bInd, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
let topRight = getImage(bInd, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
let bottomRight = getImage(bInd, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
let fracCR = sourceFracIndexCR - vec2<f32>(sourceFloorCR);
|
|
let top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
let newValue = top + (bottom - top) * fracCR.y;
|
|
setOutputFlat(index, newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
let sourceNearestCR = vec2<i32>(floor(
|
|
sourceFracIndexCR + vec2<f32>(0.5,0.5)));
|
|
let newValue = getImage(
|
|
bInd, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutputFlat(index, newValue);
|
|
}
|
|
}
|
|
}
|
|
`}},vde=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new bde(r.shape[3],a.shape,i,l),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(u,[r,a,o],"float32",d)},wde={kernelName:xi,backendName:"webgpu",kernelFunc:vde},kde=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let b = coords[0];
|
|
let h = ${this.getHeightCoordString()};
|
|
let w = ${this.getWidthCoordString()};
|
|
let d = ${this.getDepthCoordString()};
|
|
|
|
let in_h = h / uniforms.blockSize;
|
|
let offset_h = h % uniforms.blockSize;
|
|
let in_w = w / uniforms.blockSize;
|
|
let offset_w = w % uniforms.blockSize;
|
|
let offset_d = (offset_h * uniforms.blockSize + offset_w) *
|
|
${this.getOutputDepthSize()};
|
|
let in_d = d + offset_d;
|
|
|
|
let rlt = ${this.getInputSamplingString()};
|
|
setOutputFlat(index, rlt);
|
|
}
|
|
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Sde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new kde(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Ide={kernelName:bi,backendName:"webgpu",kernelFunc:Sde},V4=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=da(this.activation,this.isVec4);this.hasPreluActivation?e=`fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${r}
|
|
}`:e=`
|
|
fn activation(a : vec4<f32>, outCoord : vec4<i32>) -> vec4<f32> {
|
|
${r}
|
|
}
|
|
`,t="dotProd[i] = activation(dotProd[i], coords);"}let n=this.addBias?"dotProd[i] = dotProd[i] + getBiasAtOutCoordsByCoords(coords);":"";return`
|
|
${e}
|
|
|
|
${Tx()}
|
|
fn main([[builtin(global_invocation_id)]] globalId: vec3<u32>) {
|
|
let batch = 0;
|
|
let r = i32(globalId.x);
|
|
let c = i32(globalId.y) * 4;
|
|
let d2 = i32(globalId.z) * 4;
|
|
let xRCCorner = vec2<i32>(r, c) * uniforms.stride - uniforms.pad;
|
|
let d1 = d2;
|
|
let q = 0;
|
|
|
|
let xRCorner = xRCCorner.x;
|
|
let xCCorner = xRCCorner.y;
|
|
|
|
var wVals : array<vec4<f32>, 9>;
|
|
wVals[0] = getW(0, 0, d1, q);
|
|
wVals[1] = getW(0, 1, d1, q);
|
|
wVals[2] = getW(0, 2, d1, q);
|
|
wVals[3] = getW(1, 0, d1, q);
|
|
wVals[4] = getW(1, 1, d1, q);
|
|
wVals[5] = getW(1, 2, d1, q);
|
|
wVals[6] = getW(2, 0, d1, q);
|
|
wVals[7] = getW(2, 1, d1, q);
|
|
wVals[8] = getW(2, 2, d1, q);
|
|
|
|
var xVals : array<array<vec4<f32>, 6>, 3>;
|
|
for (var wR = 0; wR < 3; wR = wR + 1) {
|
|
let xR = xRCorner + wR * uniforms.dilation[0];
|
|
for (var wC = 0; wC < 6; wC = wC + 1) {
|
|
let xC = xCCorner + wC * uniforms.dilation[1];
|
|
if (xR < 0 || xR >= uniforms.inDims[0] || xC < 0 || xC >= uniforms.inDims[1]) {
|
|
xVals[wR][wC] = vec4<f32>(0.0);
|
|
} else {
|
|
xVals[wR][wC] = getX(batch, xR, xC, d1);
|
|
}
|
|
}
|
|
}
|
|
|
|
var dotProd : array<vec4<f32>, 4>;
|
|
dotProd[0] = vec4<f32>(0.0);
|
|
dotProd[1] = vec4<f32>(0.0);
|
|
dotProd[2] = vec4<f32>(0.0);
|
|
dotProd[3] = vec4<f32>(0.0);
|
|
|
|
for (var wR = 0; wR < 3; wR = wR + 1) {
|
|
for (var wC = 0; wC < 3; wC = wC + 1) {
|
|
let indexW = wR * 3 + wC;
|
|
dotProd[0] = dotProd[0] + xVals[wR][0 + wC] * wVals[indexW];
|
|
dotProd[1] = dotProd[1] + xVals[wR][1 + wC] * wVals[indexW];
|
|
dotProd[2] = dotProd[2] + xVals[wR][2 + wC] * wVals[indexW];
|
|
dotProd[3] = dotProd[3] + xVals[wR][3 + wC] * wVals[indexW];
|
|
}
|
|
}
|
|
|
|
for (var i = 0; i < 4; i = i + 1) {
|
|
let coords = vec4<i32>(batch, r, c + i, d2);
|
|
if (coordsInBounds4D(coords, uniforms.outShape)) {
|
|
${n}
|
|
${t}
|
|
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
|
|
}
|
|
}
|
|
}
|
|
`}},U4=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
|
|
inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;
|
|
channelMul : i32;`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=da(this.activation,!1);this.hasPreluActivation?e=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${r}
|
|
}`:e=`
|
|
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
|
|
${r}
|
|
}
|
|
`,t="dotProd = activation(dotProd, coords);"}let n=this.addBias?"dotProd = dotProd + getBiasAtOutCoordsByCoords(coords);":"";return`
|
|
${e}
|
|
|
|
fn writeResult(batch : i32, row : i32, col : i32, chan : i32,
|
|
value : f32) {
|
|
let coord = vec4<i32>(batch, row, col, chan);
|
|
if (coordsInBounds4D(coord, uniforms.outShape)) {
|
|
setOutput(batch, row, col, chan, value);
|
|
}
|
|
}
|
|
|
|
${ua()}
|
|
let coords = getOutputCoords();
|
|
let batch = coords[0];
|
|
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
|
|
let d2 = coords[3];
|
|
let d1 = d2 / uniforms.channelMul;
|
|
let q = d2 - d1 * uniforms.channelMul;
|
|
|
|
let inputRowStart = xRCCorner.x;
|
|
let inputColStart = xRCCorner.y;
|
|
let inputRowEnd = inputRowStart + uniforms.filterHeight *
|
|
uniforms.dilation[0];
|
|
let inputColEnd = inputColStart + uniforms.filterWidth *
|
|
uniforms.dilation[1];
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
var dotProd = 0.0;
|
|
|
|
// Extract if checking out of for loop for performance.
|
|
if (inputRowStart >= 0 && inputColStart >= 0 &&
|
|
inputRowEnd < uniforms.inDims[0] &&
|
|
inputColEnd < uniforms.inDims[1]) {
|
|
// Here using a constant value |this.convInfo.filterHeight| instead
|
|
// of uniform value is in order to loop unrolling.
|
|
for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
|
|
let xR = inputRowStart + wR * uniforms.dilation[0];
|
|
|
|
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
|
|
let xC = inputColStart + wC * uniforms.dilation[1];
|
|
|
|
let xVal = getX(batch, xR, xC, d1);
|
|
let wVal = getW(wR, wC, d1, q);
|
|
dotProd = dotProd + xVal * wVal;
|
|
}
|
|
}
|
|
} else {
|
|
for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
|
|
let xR = inputRowStart + wR * uniforms.dilation[0];
|
|
|
|
if (xR < 0 || xR >= uniforms.inDims[0]) {
|
|
continue;
|
|
}
|
|
|
|
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
|
|
let xC = inputColStart + wC * uniforms.dilation[1];
|
|
|
|
if (xC < 0 || xC >= uniforms.inDims[1]) {
|
|
continue;
|
|
}
|
|
|
|
let xVal = getX(batch, xR, xC, d1);
|
|
let wVal = getW(wR, wC, d1, q);
|
|
dotProd = dotProd + xVal * wVal;
|
|
}
|
|
}
|
|
}
|
|
|
|
${n}
|
|
${t}
|
|
writeResult(batch, coords[1], coords[2], d2, dotProd);
|
|
}
|
|
`}};function Cde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]);let d=N.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}],h;return d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?h=new V4(d):(h=new U4(d),p.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.outChannels/d.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,p)}var Tde={kernelName:Va,backendName:"webgpu",kernelFunc:Cde},G4=qn({opSnippet:jt.MUL,cpuKernelImpl:cce,supportsComplex:!0}),Nde={kernelName:oo,backendName:"webgpu",kernelFunc:G4},Ede=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
|
|
if (isNanCustom(candidate)) {
|
|
bestValue = uniforms.NAN;
|
|
} elseif (!isNanCustom(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
|
|
{ bestValue = candidate; }`,t="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let n=this.reduceType==="mean"?"setOutputFlat(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputFlat(outputIndex, bestValue);";return`
|
|
fn DIV_CEIL(a : u32, b : u32) -> u32 {
|
|
return ((a - 1u) / b + 1u);
|
|
}
|
|
|
|
${`
|
|
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
|
|
`}
|
|
fn getOffset(outputIndex : i32) -> i32 {
|
|
let outputCoords = getCoordsFromFlatIndex(outputIndex);
|
|
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
|
|
return offset;
|
|
}
|
|
${Ke()}
|
|
let outputIndex = index / i32(workGroupSizeX);
|
|
let offset = getOffset(outputIndex);
|
|
var bestValue = ${t};
|
|
let Length = uniforms.reduceSize;
|
|
let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);
|
|
for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
|
|
k = k + i32(workGroupSizeX)) {
|
|
let candidate = f32(x.numbers[offset + k]);
|
|
${e}
|
|
}
|
|
xBestValues[localId.x] = bestValue;
|
|
workgroupBarrier();
|
|
|
|
var reduceSize = min(u32(Length), workGroupSizeX);
|
|
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
|
|
currentSize = reduceSize / 2u) {
|
|
let interval = DIV_CEIL(reduceSize, 2u);
|
|
if (localId.x < currentSize) {
|
|
let candidate = xBestValues[localId.x + interval];
|
|
${e}
|
|
xBestValues[localId.x] = bestValue;
|
|
}
|
|
reduceSize = interval;
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (localId.x == 0u && outputIndex < uniforms.size) {
|
|
${n}
|
|
}
|
|
}
|
|
`}};function $p(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,c=N.getAxesPermutation(l,a),u=e;c!=null&&(u=Ol({inputs:{x:e},attrs:{perm:c},backend:r}),l=N.getInnerMostAxes(l.length,a),o.push(u)),N.assertAxesAreInnerMostDims(s,l,a);let[d,p]=N.computeOutAndReduceShapes(u.shape,l),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([u])){let m=r.tensorMap.get(u.dataId).values;switch(s){case"max":let g=ice(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=hce(u.shape,u.dtype,m,l);f=r.makeTensorInfo(x,A,y);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(p),y=v.sizeFromShape(u.shape)/m,x={windowSize:m,inSize:m,batchSize:y,outSize:1},A=s==="mean"?"float32":Pd(e.dtype),b=[{type:"int32",data:[m]}],w=new Ede(x,s),C=r.runWebGPUProgram(w,[u],A,b);o.push(C),f=qe({inputs:{x:C},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function zx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return $p(r,a,o,"sum",n)}var Rde={kernelName:Ao,backendName:"webgpu",kernelFunc:zx};function _de(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=N.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:y,expandDims:x}=N.getEinsumPermutation(h,l[g]),A;N.isIdentityPermutation(y)?A=a[g]:(A=Ol({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(A.shape,b)||(A=qe({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),p===null?p=A:(p=G4({inputs:{a:A,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=zx({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeData(m.dataId);return p}var Dde={kernelName:fd,backendName:"webgpu",kernelFunc:_de},$de=Cn({opType:wt.ELU}),Fde={kernelName:Ga,backendName:"webgpu",kernelFunc:$de},Pde=qn({opSnippet:jt.EQUAL,dtype:"bool",cpuKernelImpl:Zue}),Ode={kernelName:vi,backendName:"webgpu",kernelFunc:Pde},H4=Cn({opType:wt.EXP,cpuKernelImpl:Yue,dtype:"float32"}),Mde={kernelName:Ha,backendName:"webgpu",kernelFunc:H4};function Lx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),qe({inputs:{x:a},backend:s,attrs:{shape:i}})}var zde={kernelName:wi,backendName:"webgpu",kernelFunc:Lx},Lde=Cn({opType:wt.EXPM1,cpuKernelImpl:Jue}),Bde={kernelName:ki,backendName:"webgpu",kernelFunc:Lde},Wde=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
setOutputFlat(index, uniforms.value);
|
|
}
|
|
}
|
|
`}};function kc(e){let{backend:t,attrs:n}=e,{shape:s,value:r}=n,{dtype:a}=n;if(a=a||v.inferDtype(r),a==="string"){let o=v.getArrayFromDType(a,v.sizeFromShape(s));return o.fill(r),t.makeTensorInfo(s,a,o)}else{let o=new Wde(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var Vde={kernelName:Au,backendName:"webgpu",kernelFunc:kc},Ude=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let coordX = uniforms.xShape[2] - coords[2] - 1;
|
|
let outputValue = getX(coords[0], coords[1], coordX, coords[3]);
|
|
setOutputFlat(index, outputValue);
|
|
}
|
|
}
|
|
`}},Gde={kernelName:Si,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Ude(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Hde=Cn({opType:wt.FLOOR,cpuKernelImpl:Que}),jde={kernelName:ja,backendName:"webgpu",kernelFunc:Hde},qde=qn({opSnippet:jt.INT_DIV,dtype:"int32"}),Xde={kernelName:qa,backendName:"webgpu",kernelFunc:qde},Kde=(e,t,n,s,r)=>{let a=[s,...n];return r&&a.push(r),e.createBindGroup({layout:t,entries:a.map((o,i)=>({binding:i,resource:o}))})},j4=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=Ile(s,o,t,a),l=e.createShaderModule({code:i,label:t.constructor.name});return e.createComputePipeline({layout:n,compute:{module:l,entryPoint:"main"},label:t.constructor.name})};function q4(e,t,n,s="",r=""){return e.shaderKey+"_"+(e.workGroupSize?e.workGroupSize.join(","):"")+t.map(o=>o.length).join(",")+n.join(",")+e.variableNames.join(",")+s+r}function X4(e){let{externalImage:t,backend:n,attrs:s,outShape:r,useImport:a}=e,{numChannels:o}=s,i=v.sizeFromShape(r),l=v.computeStrides(r),c=n.makeTensorInfo(r,"int32"),u=n.getFromPixelsProgram(a?"import":"copyExternal");u.updateOutputShape(r);let d=[c.shape],p=[c.dtype,a?"import":"copyExternal"],h=q4(u,d,p),f=u.getLayout(n.device),m=n.getAndSavePipeline(h,()=>j4(n.device,u,f.pipelineLayout,[],c,!0));u.setPipeline(m),a||n.queue.copyExternalImageToTexture({source:t,origin:{x:0,y:0}},{texture:u.makeInputTexture(n.device,r[1],r[0])},[r[1],r[0]]);let g=n.tensorMap.get(c.dataId);g.bufferInfo.buffer=n.acquireBuffer(g.bufferInfo.byteSize);let y=[i,o,...l,...u.dispatch];u.setUniform(n.device,y);let x;if(a){let A={source:t};x=n.device.importExternalTexture(A)}else x=u.inputTexture.createView();return n.runFromPixelsProgram(u,g.bufferInfo.buffer,f,x,c.dataId),c}var Zde={kernelName:Cd,backendName:"webgpu",kernelFunc:Yde},Sc;function Yde(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s;if(r==null)throw new Error("pixels passed to tf.browser.fromPixels() can not be null");let o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,l=typeof HTMLCanvasElement!="undefined"&&r instanceof HTMLCanvasElement||typeof OffscreenCanvas!="undefined"&&r instanceof OffscreenCanvas,c=typeof ImageBitmap!="undefined"&&r instanceof ImageBitmap,[u,d]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],p=[d,u,a];if(Y().getBool("WEBGPU_USE_IMPORT")&&o)return X4({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!0});if((o||i)&&(Sc==null&&(Sc=document.createElement("canvas").getContext("2d")),Sc.canvas.width=u,Sc.canvas.height=d,Sc.drawImage(r,0,0,u,d),r=Sc.canvas),c||l||o||i)return X4({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!1});let h=r.data,f=h;if(a!=null&&a!==4){f=new Uint8Array(r.width*r.height*a);let y=h.length,x=0;for(let A=0;A<y;A++)A%4<a&&(f[x++]=h[A])}let m=n.makeTensorInfo(p,"int32"),g=n.tensorMap.get(m.dataId);return g.values=new Int32Array(f),n.maybeReleaseBuffer(m.dataId),n.uploadToGPU(m.dataId),m}var Jde=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(N.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale")),this.offsetShape=s,this.scaleShape=r,this.shaderKey="batchNorm"}getUserCode(){let e="0.0";this.offsetShape!=null&&(e="getOffsetAtOutCoordsByGlobalIndex(index)");let t="1.0";return this.scaleShape!=null&&(t="getScaleAtOutCoordsByGlobalIndex(index)"),`
|
|
${Ke()}
|
|
if (index < uniforms.size)
|
|
{
|
|
let xValue = getXAtOutCoordsByGlobalIndex(index);
|
|
let meanValue = getMeanAtOutCoordsByGlobalIndex(index);
|
|
let varianValue = getVarianceAtOutCoordsByGlobalIndex(index);
|
|
let offsetValue = ${e};
|
|
let scaleValue = ${t};
|
|
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
|
|
setOutputFlat(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
|
|
}
|
|
}
|
|
`}},Qde={kernelName:Xa,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:l}=t,c=n,u=[s,o,i],d=null;a!=null&&(d=a.shape,u.push(a));let p=null;r!=null&&(p=r.shape,u.push(r));let h=new Jde(s.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[l]}];return c.runWebGPUProgram(h,u,s.dtype,f)}};function epe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(u),g=N.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),y=o!=null,x=i!=null,A;if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))return z4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});let b=Y().getBool("WEBGPU_USE_NAIVE_CONV2D"),w=g.inChannels%4==0&&g.outChannels%4==0,C=[g.padInfo.top,g.padInfo.left],S=[{type:"int32",data:[g.filterHeight,g.filterWidth]},{type:"int32",data:[...C]},{type:"int32",data:[g.strideHeight,g.strideWidth]},{type:"int32",data:[g.dilationHeight,g.dilationWidth]}];if(b)A=new W4(g,y,h,x);else{w?A=new L4(g,y,h,x):A=new B4(g,y,h,x);let R=g.outShape[1]*g.outShape[2],P=g.outShape[3],_=g.filterHeight*g.filterWidth*g.inShape[3];S.push({type:"int32",data:[R]},{type:"int32",data:[P]},{type:"int32",data:[_]})}let E=[r,a];return y&&E.push(o),x&&E.push(i),n.runWebGPUProgram(A,E,r.dtype,S)}var tpe={kernelName:Co,backendName:"webgpu",kernelFunc:epe};function npe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dimRoundingMode:d,activation:p}=s,h=u;h==null&&(h=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let f=N.computeConv2DInfo(r.shape,a.shape,l,h,c,d,!0),m=[r,a],g=o!=null,y=i!=null;g&&m.push(o),y&&m.push(i);let x=[{type:"int32",data:[f.padInfo.top,f.padInfo.left]},{type:"int32",data:[f.strideHeight,f.strideWidth]},{type:"int32",data:[f.dilationHeight,f.dilationWidth]},{type:"int32",data:[f.inHeight,f.inWidth]}],A;return f.batchSize===1&&f.inHeight===f.outHeight&&f.inWidth===f.outWidth&&f.strideHeight===1&&f.strideWidth===1&&f.filterHeight===f.filterWidth&&f.inChannels===f.outChannels&&f.filterHeight===3&&f.inChannels%4==0?A=new V4(f,g,p,y):(A=new U4(f,g,p,y),x.push({type:"int32",data:[f.filterHeight]},{type:"int32",data:[f.filterWidth]},{type:"int32",data:[f.outChannels/f.inChannels]})),n.runWebGPUProgram(A,m,"float32",x)}var spe={kernelName:To,backendName:"webgpu",kernelFunc:npe},rpe=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`gathernd_${e}`,this.sliceDim=e,this.uniforms=`sliceDim : i32; strides : ${wn(e)};`}getUserCode(){let e;return this.sliceDim>1?e="uniforms.strides[j]":e="uniforms.strides",`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
var flattenIndex = 0;
|
|
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
|
|
let indexTemp = i32(round(getIndices(coords[0], j)));
|
|
let strideNum = ${e};
|
|
flattenIndex = flattenIndex + indexTemp * strideNum;
|
|
}
|
|
|
|
setOutputFlat(index, getA(flattenIndex, coords[1]));
|
|
}
|
|
}
|
|
`}};function ape(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=N.prepareAndValidate(s,r),p=qe({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=qe({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let x=n.readSync(r.dataId),A=n.bufferSync(s),b=ece(x,A,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new rpe(o,[c,u]),m=[{type:"int32",data:[o]},{type:"int32",data:d}],g=n.runWebGPUProgram(f,[h,p],h.dtype,m),y=qe({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(p.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),y}var ope={kernelName:Ci,backendName:"webgpu",kernelFunc:ape},ipe=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.slice(),this.aShape=e,this.outputShape=t,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=lpe(this.aShape,"i32");return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function lpe(e,t="int"){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e.length;r++)r===2?s.push(`${t}(getIndices(resRC.x, resRC.z))`):s.push(`${n[r]}`);return s.join()}function K4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=N.segment_util.collectGatherOpShapeInfo(r,a,l,i),u=v.sizeFromShape(a.shape),d=[],p=qe({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=qe({inputs:{x:a},backend:n,attrs:{shape:[c.batchSize,u/c.batchSize]}});d.push(p),d.push(h);let f=[c.batchSize,c.outerSize,u/c.batchSize,c.sliceSize];if(n.shouldExecuteOnCPU([r,a])){let A=n.tensorMap.get(h.dataId).values,b=ze(h.shape,h.dtype,A),C=n.tensorMap.get(p.dataId).values,S=ze(p.shape,p.dtype,C),E=tce(S,b,f);return d.forEach(R=>n.disposeData(R.dataId)),n.makeTensorInfo(c.outputShape,E.dtype,E.values)}let m=new ipe(p.shape,f),g=n.runWebGPUProgram(m,[p,h],p.dtype);d.push(g);let y=qe({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return d.forEach(x=>n.disposeData(x.dataId)),y}var upe={kernelName:Ii,backendName:"webgpu",kernelFunc:K4},cpe=qn({opSnippet:jt.GREATER,cpuKernelImpl:sce,dtype:"bool"}),dpe={kernelName:Ti,backendName:"webgpu",kernelFunc:cpe},ppe=qn({opSnippet:jt.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:nce}),hpe={kernelName:Ka,backendName:"webgpu",kernelFunc:ppe},fpe=qn({opSnippet:jt.LESS,dtype:"bool",cpuKernelImpl:ace}),mpe={kernelName:Ni,backendName:"webgpu",kernelFunc:fpe},gpe=qn({opSnippet:jt.LESS_EQUAL,dtype:"bool",cpuKernelImpl:rce}),ype={kernelName:Ei,backendName:"webgpu",kernelFunc:gpe},Ape=Cn({opType:wt.LOG,cpuKernelImpl:oce}),xpe={kernelName:Ja,backendName:"webgpu",kernelFunc:Ape},bpe=qn({opSnippet:jt.LOGICAL_AND,dtype:"bool"}),vpe={kernelName:Ri,backendName:"webgpu",kernelFunc:bpe},wpe=Cn({opType:wt.LOGICAL_NOT}),kpe={kernelName:ku,backendName:"webgpu",kernelFunc:wpe};function Z4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return $p(r,a,o,"max",n)}var Spe={kernelName:Qa,backendName:"webgpu",kernelFunc:Z4},Ipe=qn({opSnippet:jt.MAX,cpuKernelImpl:lce}),Cpe={kernelName:eo,backendName:"webgpu",kernelFunc:Ipe};function Tpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=N.computePool2DInfo(r.shape,a,o,c,i,l),d,p=[];if(u.filterHeight===1&&u.filterWidth===1){if(v.arraysEqual(u.inShape,u.outShape))return ar({inputs:{x:r},backend:n});d=new P4(u),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else d=new F4(u,"max"),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]},{type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]});return n.runWebGPUProgram(d,[r],r.dtype,p)}var Npe={kernelName:to,backendName:"webgpu",kernelFunc:Tpe};function Epe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return $p(r,o,a,"mean",n)}var Rpe={kernelName:no,backendName:"webgpu",kernelFunc:Epe};function _pe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return $p(r,a,o,"min",n)}var Dpe={kernelName:so,backendName:"webgpu",kernelFunc:_pe},$pe=qn({opSnippet:jt.MIN,cpuKernelImpl:uce}),Fpe={kernelName:ro,backendName:"webgpu",kernelFunc:$pe},Ppe=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${r} : vec2<i32>;`}),this.offset=n==="reflect"?0:1,this.shaderKey=`mirrorPad_${n}`}getUserCode(){let e=this.xShape.length,t=this.xShape.map((l,c)=>`uniforms.pad${c}[0]`).join(","),n=this.xShape.map((l,c)=>`uniforms.pad${c}[0] + uniforms.xShape${e>1?`[${c}]`:""}`).join(","),s=e===1?"start":"start[i]",r=e===1?"end":"end[i]",a=e===1?"outC":"outC[i]",o=wn(e),i=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let start = ${o}(${t});
|
|
let end = ${o}(${n});
|
|
var outC = getCoordsFromFlatIndex(index);
|
|
for (var i = 0; i < ${e}; i = i + 1) {
|
|
if (${a} < ${s}) {
|
|
${a} = ${s} * 2 - ${a} - ${this.offset};
|
|
} elseif(${a} >= ${r}) {
|
|
${a} = (${r} - 1) * 2 - ${a} + ${this.offset};
|
|
}
|
|
}
|
|
let coords = outC - start;
|
|
setOutputFlat(index, getX(${i}));
|
|
}
|
|
}
|
|
`}},Ope={kernelName:ao,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s}=e,{paddings:r,mode:a}=t,o=n,i=r.map(u=>({type:"int32",data:[u[0],u[1]]})),l=new Ppe(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function Mpe(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=dce(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new _p(s.shape,wt.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var zpe={kernelName:_i,backendName:"webgpu",kernelFunc:Mpe};function Lpe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=er.nonMaxSuppressionV3Impl(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Bpe={kernelName:$i,backendName:"webgpu",kernelFunc:Lpe};function Wpe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:y}=er.nonMaxSuppressionV5Impl(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Vpe={kernelName:Fi,backendName:"webgpu",kernelFunc:Wpe};function r0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Dp({inputs:{input:s},backend:n}),a=r0({inputs:{x:r},backend:n}),o=s0({inputs:{input:s},backend:n}),i=r0({inputs:{x:o},backend:n}),l=vc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return kc({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Upe={kernelName:Qi,backendName:"webgpu",kernelFunc:r0};function Y4(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=Dp({inputs:{input:s},backend:n}),a=Y4({inputs:{x:r},backend:n}),o=s0({inputs:{input:s},backend:n}),i=r0({inputs:{x:o},backend:n}),l=vc({inputs:{real:a,imag:i},backend:n});return n.disposeData(r.dataId),n.disposeData(a.dataId),n.disposeData(o.dataId),n.disposeData(i.dataId),l}else return kc({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Gpe={kernelName:Pi,backendName:"webgpu",kernelFunc:Y4};function Hpe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Lx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=Lx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=M4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var jpe={kernelName:Mi,backendName:"webgpu",kernelFunc:Hpe},qpe=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${s} : vec2<i32>;`}),this.xShape=e,this.shaderKey="pad"}getUserCode(){let e=this.xShape.length,t=wn(e),n=this.xShape.map((u,d)=>`uniforms.pad${d}[0]`).join(","),s=this.xShape.map((u,d)=>`uniforms.pad${d}[0] + uniforms.xShape${e>1?`[${d}]`:""}`).join(","),r=e>1?`${t}(${n})`:`${n}`,a=e>1?`${t}(${s})`:`${s}`,o=e>1?"any(outC < start)":"outC < start",i=e>1?"any(outC >= end)":"outC >= end",l=e>1?["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,e):"coords";return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let start = ${r};
|
|
let end = ${a};
|
|
let outC = getCoordsFromFlatIndex(index);
|
|
|
|
if (${o} || ${i}) {
|
|
setOutputFlat(index, uniforms.constantValue);
|
|
} else {
|
|
let coords = outC - start;
|
|
setOutputFlat(index, getX(${l}));
|
|
}
|
|
}
|
|
}
|
|
`}},J4=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(c=>v.arraysEqual(c,[0,0])))return ar({inputs:{x:r},backend:n});if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return kc({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(c=>i.push({type:"int32",data:[c[0],c[1]]}));let l=new qpe(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},Xpe={kernelName:io,backendName:"webgpu",kernelFunc:J4},Kpe=qn({opSnippet:jt.POW}),Zpe={kernelName:lo,backendName:"webgpu",kernelFunc:Kpe};function Ype(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new _4(jt.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Jpe={kernelName:uo,backendName:"webgpu",kernelFunc:Ype};function Qpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return $p(r,a,o,"prod",n)}var ehe={kernelName:zi,backendName:"webgpu",kernelFunc:Qpe},the=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=fce(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},nhe={kernelName:Cu,backendName:"webgpu",kernelFunc:the},Q4=qn({opSnippet:jt.DIV}),she={kernelName:Ua,backendName:"webgpu",kernelFunc:Q4},rhe=Cn({opType:wt.RELU}),ahe={kernelName:co,backendName:"webgpu",kernelFunc:rhe},ohe=Cn({opType:wt.RELU6}),ihe={kernelName:ho,backendName:"webgpu",kernelFunc:ohe};function lhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=[{type:"float32",data:[a]}],i=new _p(r.shape,wt.LEAKYRELU);return i.uniforms="alpha : f32;",n.runWebGPUProgram(i,[r],"float32",o)}var uhe={kernelName:Ya,backendName:"webgpu",kernelFunc:lhe},che=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; halfPixelCenters : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="resizeBilinear"}getUserCode(){return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let b = coords[0];
|
|
let d = coords[3];
|
|
let rc = coords.yz;
|
|
|
|
let effectiveInSize = vec2<f32>(
|
|
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
|
|
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
|
|
|
|
let effectiveOutSize = vec2<f32>(
|
|
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
|
|
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
|
|
|
|
let effectiveInputOverOutputRatioRC =
|
|
effectiveInSize / effectiveOutSize;
|
|
|
|
// Fractional source index
|
|
let sourceFracIndexRC =
|
|
(vec2<f32>(rc) + vec2<f32>(uniforms.halfPixelCenters)) *
|
|
effectiveInputOverOutputRatioRC - vec2<f32>(uniforms.halfPixelCenters);
|
|
|
|
// Compute the four integer indices.
|
|
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
|
|
let sourceCeilRC = vec2<i32>(
|
|
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
|
|
|
|
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
|
|
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
|
|
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
|
|
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
|
|
|
|
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
|
|
|
|
let top = topLeft + (topRight - topLeft) * fracRC.y;
|
|
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
|
|
let newValue = top + (bottom - top) * fracRC.x;
|
|
|
|
setOutputFlat(index, newValue);
|
|
}
|
|
}
|
|
`}};function dhe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,c]=o,u=a&&l>1?1:0,d=a&&c>1?1:0,h=[{type:"float32",data:[u,d]},{type:"float32",data:[i?.5:0]}],f=new che(r.shape,l,c);return n.runWebGPUProgram(f,[r],"float32",h)}var phe={kernelName:po,backendName:"webgpu",kernelFunc:dhe},hhe=class{constructor(e,t,n,s){this.variableNames=["x"],this.uniforms="adjustHeightWidth : vec2<f32>; roundBase : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.halfPixelCenters=s,this.shaderKey=`resizeNearest_${s}`}getUserCode(){let e;return this.halfPixelCenters?e="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":e="vec2<f32>(rc) * effectiveInputOverOutputRatioRC",`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let b = coords[0];
|
|
let d = coords[3];
|
|
let rc = coords.yz;
|
|
|
|
let effectiveInSize = vec2<f32>(
|
|
f32(uniforms.xShape.y) - uniforms.adjustHeightWidth[0],
|
|
f32(uniforms.xShape.z) - uniforms.adjustHeightWidth[1]);
|
|
|
|
let effectiveOutSize = vec2<f32>(
|
|
f32(uniforms.outShape.y) - uniforms.adjustHeightWidth[0],
|
|
f32(uniforms.outShape.z) - uniforms.adjustHeightWidth[1]);
|
|
|
|
let effectiveInputOverOutputRatioRC =
|
|
effectiveInSize / effectiveOutSize;
|
|
|
|
// Fractional source index
|
|
let sourceFracIndexRC = ${e};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
let inputShapeRC = vec2<f32>(f32(uniforms.xShape.y), f32(uniforms.xShape.z));
|
|
let sourceNearestRC = vec2<i32>(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + uniforms.roundBase)));
|
|
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutputFlat(index, newValue);
|
|
}
|
|
}
|
|
`}};function fhe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=a&&l>1?1:0,d=a&&c>1?1:0,h=[{type:"float32",data:[u,d]},{type:"float32",data:[a?.5:0]}],f=new hhe(r.shape,l,c,o);return n.runWebGPUProgram(f,[r],r.dtype,h)}var mhe={kernelName:Nu,backendName:"webgpu",kernelFunc:fhe},ghe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`centerX : f32; centerY : f32; sinRadians : f32;
|
|
cosRadians : f32;`,this.shaderKey="rotate",this.outputShape=e,typeof t=="number"?(this.uniforms+=" fillValue : f32;",this.fillSnippet="var outputValue = uniforms.fillValue;",this.shaderKey+="_float"):(this.uniforms+=" fillValue : vec3<f32>;",this.fillSnippet="var outputValue = uniforms.fillValue[coords[3]];",this.shaderKey+="_vec3")}getUserCode(){return`
|
|
${Ke()}
|
|
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let coordXFloat = (f32(coords[2]) - uniforms.centerX) *
|
|
uniforms.cosRadians - (f32(coords[1]) - uniforms.centerY) *
|
|
uniforms.sinRadians;
|
|
let coordYFloat = (f32(coords[2]) - uniforms.centerX) *
|
|
uniforms.sinRadians + (f32(coords[1]) - uniforms.centerY) *
|
|
uniforms.cosRadians;
|
|
let coordX = i32(round(coordXFloat + uniforms.centerX));
|
|
let coordY = i32(round(coordYFloat + uniforms.centerY));
|
|
${this.fillSnippet}
|
|
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
|
|
coordY < uniforms.xShape[1]) {
|
|
outputValue = getX(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutputFlat(index, outputValue);
|
|
}
|
|
}
|
|
`}},yhe={kernelName:el,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new ghe(s.shape,a),[c,u]=N.getImageCenter(o,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,d)}},Ahe=Cn({opType:wt.RSQRT,cpuKernelImpl:mce}),xhe={kernelName:fo,backendName:"webgpu",kernelFunc:Ahe},bhe=class{constructor(e,t,n,s,r,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=He(e),this.dispatch=Oe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${n}_${s}_${this.sliceDimGreaterThanOne}_${o}`;let i=wn(r.length);this.uniforms=`sliceDim : i32; strides: ${i}; size: i32;`,this.updatesRank=s,this.indicesRank=n}getUserCode(){let e="";this.indicesRank===1?e="coords[0]":this.indicesRank===2&&(e="coords[0], j");let t=`getIndices(${e})`,n=this.sliceDimGreaterThanOne?"uniforms.strides[j]":"uniforms.strides",s="",r="",a="";this.updatesRank===1?(s="coords[0]",r="flattenedIndex",a=`
|
|
fn getUpdatesCoordsFromFlatIndex(index : i32) -> i32 {
|
|
return index;
|
|
}
|
|
`):this.updatesRank===2&&(s="coords[0], coords[1]",r="vec2<i32>(flattenedIndex, coords[1])",a=`
|
|
fn getUpdatesCoordsFromFlatIndex(index : i32) -> vec2<i32> {
|
|
let d0 = index / uniforms.updatesShape[1];
|
|
let d1 = index - d0 * uniforms.updatesShape[1];
|
|
return vec2<i32>(d0, d1);
|
|
}
|
|
`);let o=`getUpdates(${s})`,i=this.type==="int32"?"atomicAdd(&(result.numbers[flatIndex]), i32(updateValue));":`
|
|
var assumed = atomicLoad(&(result.numbers[flatIndex]));
|
|
var success = 0;
|
|
for (; success == 0;) {
|
|
let new = bitcast<f32>(assumed) + updateValue;
|
|
let newI32 = bitcast<i32>(new);
|
|
let resValue = atomicCompareExchangeWeak(&(result.numbers[flatIndex]), assumed, newI32);
|
|
assumed = resValue[0];
|
|
success = resValue[1];
|
|
}
|
|
`;return`
|
|
${a}
|
|
|
|
${Ke()}
|
|
|
|
if (index < uniforms.size) {
|
|
let coords = getUpdatesCoordsFromFlatIndex(index);
|
|
var flattenedIndex = 0;
|
|
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
|
|
let indexInside = i32(round(${t}));
|
|
flattenedIndex = flattenedIndex + indexInside * ${n};
|
|
}
|
|
let updateValue = ${o};
|
|
let flatIndex = getOutputFlatIndex(${r});
|
|
|
|
${i}
|
|
}
|
|
}`}};function vhe(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=N.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=qe({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=qe({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=f.dtype,g=kc({backend:n,attrs:{shape:p,value:0,dtype:m}}),y=v.sizeFromShape(f.shape),x=[{type:"int32",data:[i]},{type:"int32",data:u},{type:"int32",data:[y]}],A=new bhe(f.shape,i,h.shape.length,f.shape.length,u,p,m),b=n.runWebGPUProgram(A,[f,h],m,x,g),w=qe({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(b.dataId),w}var whe={kernelName:Vi,backendName:"webgpu",kernelFunc:vhe},khe=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.cRank=e,this.rank=n,this.shaderKey="select"}getUserCode(){let e,t;if(this.rank>4)throw Error(`Where for rank ${this.rank} is not yet supported`);if(this.rank===1)t="resRC",e="resRC";else{let s=["resRC.x","resRC.y","resRC.z","resRC.w"],r=[],a=[];for(let o=0;o<this.outputShape.length;o++)a.push(`${s[o]}`),o<this.cRank&&r.push(`${s[o]}`);e=r.join(),t=a.join()}return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
let cVal = getC(${e});
|
|
if (cVal >= 1.0) {
|
|
setOutputFlat(index, getA(${t}));
|
|
} else {
|
|
setOutputFlat(index, getB(${t}));
|
|
}
|
|
}
|
|
}
|
|
`}};function She(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new khe(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],Ln(r.dtype,a.dtype))}var Ihe={kernelName:Ui,backendName:"webgpu",kernelFunc:She},Che=Cn({opType:wt.SIGMOID}),The={kernelName:go,backendName:"webgpu",kernelFunc:Che},Nhe=Cn({opType:wt.SIN}),Ehe={kernelName:mo,backendName:"webgpu",kernelFunc:Nhe},Rhe=Cn({opType:wt.SINH}),_he={kernelName:Hi,backendName:"webgpu",kernelFunc:Rhe},e6=qn({opSnippet:jt.SUB,cpuKernelImpl:bce,supportsComplex:!0}),Dhe={kernelName:vo,backendName:"webgpu",kernelFunc:e6};function $he(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=Z4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,o),c=qe({inputs:{x:i},backend:n,attrs:{shape:l}}),u=e6({inputs:{a:r,b:c},backend:n}),d=H4({inputs:{x:u},backend:n}),p=zx({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=qe({inputs:{x:p},backend:n,attrs:{shape:l}}),f=Q4({inputs:{a:d,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(c.dataId),n.disposeData(u.dataId),n.disposeData(d.dataId),n.disposeData(p.dataId),n.disposeData(h.dataId),f}var Fhe={kernelName:xo,backendName:"webgpu",kernelFunc:$he},Phe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<r.shape.length;++y)l.push([0,0]);let c=[],u=J4({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,a,i,!1),p=N.getPermuted(d.length,a.length,!1),h=N.getReshapedPermuted(u.shape,a,i,!1),f=qe({inputs:{x:u},backend:n,attrs:{shape:d}}),m=Ol({inputs:{x:f},backend:n,attrs:{perm:p}}),g=qe({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeData(y.dataId)),g},Ohe={kernelName:ji,backendName:"webgpu",kernelFunc:Phe},Mhe=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${n}_${s}_${i}`;let l=wn(r.length);this.uniforms=`updateSize : i32; sliceDim : i32; strides: ${l};`;let c="";n===1?c="i":n===2&&(c="i, j"),this.indicesSnippet=`getIndices(${c})`;let u="";s===1?u="i":s===2&&(u="i, coords[1]"),this.updatesSnippet=`getUpdates(${u})`,this.strideString=i?"uniforms.strides[j]":"uniforms.strides"}getUserCode(){return`
|
|
${Ke()}
|
|
|
|
let globalIndex = index * ${this.workPerThread};
|
|
if (globalIndex < uniforms.size) {
|
|
var sum = vec4<f32>(0.0);
|
|
var found = vec4<bool>(false);
|
|
for (var i = 0; i < uniforms.updateSize; i = i + 1) {
|
|
var flattenedIndex = 0;
|
|
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
|
|
let indexInside = i32(round(${this.indicesSnippet}));
|
|
flattenedIndex = flattenedIndex + indexInside * ${this.strideString};
|
|
}
|
|
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
|
|
let curIndex = globalIndex + innerIndex;
|
|
let coords = getCoordsFromFlatIndex(curIndex);
|
|
if (flattenedIndex == coords[0]) {
|
|
sum[innerIndex] = sum[innerIndex] + ${this.updatesSnippet};
|
|
found[innerIndex] = true;
|
|
}
|
|
}
|
|
}
|
|
for (var innerIndex = 0; innerIndex < ${this.workPerThread}; innerIndex = innerIndex + 1) {
|
|
let curIndex = globalIndex + innerIndex;
|
|
if (curIndex < uniforms.size)
|
|
{
|
|
setOutputFlat(curIndex, mix(getDefaultValue(), sum[innerIndex], f32(found[innerIndex])));
|
|
}
|
|
}
|
|
}
|
|
}`}};function zhe(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=N.calculateShapes(a,r,i),p=!1,h=[{type:"int32",data:[c]},{type:"int32",data:[l]},{type:"int32",data:u}],f=new Mhe(c,l,r.shape.length,a.shape.length,u,[d,1],p),m=n.runWebGPUProgram(f,[a,r,o],a.dtype,h),g=qe({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),g}var Lhe={kernelName:kd,backendName:"webgpu",kernelFunc:zhe};function Bhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=N.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=wc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var Whe={kernelName:qi,backendName:"webgpu",kernelFunc:Bhe},Vhe=Cn({opType:wt.SQRT}),Uhe={kernelName:yo,backendName:"webgpu",kernelFunc:Vhe},Ghe={kernelName:$u,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new _p(n.shape,wt.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},Hhe=qn({opSnippet:jt.SQUARED_DIFFERENCE}),jhe={kernelName:bo,backendName:"webgpu",kernelFunc:Hhe},qhe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let t=wn(this.outputShape.length);this.uniforms=`begin : ${t}; strides : ${t}; `,this.shaderKey="stridedSlice"}getUserCode(){let e=this.outputShape.length,t="";if(e===1)t="coords * uniforms.strides + uniforms.begin";else{let s=0;t=this.outputShape.map((r,a)=>(s++,this.outputShape.length===1?`coords * uniforms.strides[${a}] + uniforms.begin[${a}]`:`coords[${s-1}] * uniforms.strides[${a}] + uniforms.begin[${a}]`)).join(",")}return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getX(${t}));
|
|
}
|
|
}
|
|
`}};function Xhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Mt.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=qe({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Mt.computeOutShape(x,A,b),S=wc({inputs:{x:r},backend:n,attrs:{begin:x,size:C}});w=qe({inputs:{x:S},backend:n,attrs:{shape:f}}),n.disposeData(S.dataId)}else if(n.shouldExecuteOnCPU([r])){let S=n.readSync(r.dataId),E=ze(r.shape,r.dtype,S),R=Ace(h,E,b,x);w=n.makeTensorInfo(f,r.dtype,R.values)}else{let S=new qhe(h),E=[{type:"int32",data:x},{type:"int32",data:b}],R=n.runWebGPUProgram(S,[r],r.dtype,E);w=qe({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeData(R.dataId)}return w}var Khe={kernelName:Xi,backendName:"webgpu",kernelFunc:Xhe};function Zhe(e){let{inputs:t,backend:n,attrs:s}=e,{separator:r,nGramWidths:a,leftPad:o,rightPad:i,padWidth:l,preserveShortSequences:c}=s,{data:u,dataSplits:d}=t,p=n.readSync(u.dataId),h=n.readSync(d.dataId),[f,m]=xce(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Yhe={kernelName:Sd,backendName:"webgpu",kernelFunc:Zhe},Jhe=Cn({opType:wt.TANH}),Qhe={kernelName:wo,backendName:"webgpu",kernelFunc:Jhe},efe=class{constructor(e,t){this.variableNames=["A"],this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=tfe(this.rank,"uniforms.");return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function tfe(e,t=""){if(e>=5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`(resRC % ${t}aShape)`;let n=["resRC.x","resRC.y","resRC.z","resRC.w"],s=[];for(let r=0;r<e;r++)s.push(`(${n[r]} % ${t}aShape[${r}])`);return s.join()}function nfe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reps:a}=s;if(n.shouldExecuteOnCPU([r])||r.dtype==="string"||r.shape.length>=5){let l=n.readSync(r.dataId),c=r.dtype==="string"?l.map(p=>v.decodeString(p)):l,u=ze(r.shape,r.dtype,c),d=vce(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new efe(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var sfe={kernelName:Yr,backendName:"webgpu",kernelFunc:nfe},rfe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32;
|
|
dir : i32; inc : i32;`,this.shaderKey="swap"}getUserCode(){return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let outC = getCoordsFromFlatIndex(index);
|
|
let batch = outC[0];
|
|
let elemIdx = outC[1];
|
|
// We compare elements pair-wise within a group of size 2 * inc.
|
|
// The comparing rule for each group alternates between ascending
|
|
// and descending. Within each group, we compare each pair at
|
|
// positions i and i+inc. To decide whether an element at position i
|
|
// is x0 or x1, we mod it by 2 * inc, if the result is smaller than
|
|
// inc, it is in the first half of the group, we denote it as x0,
|
|
// otherwise we denote it as x1.
|
|
// For example, as shown in the Bitonic top K paper referenced
|
|
// above, Figure5(a) shows that element[1] is in the second half of
|
|
// the group when group size is 2, but it is in the first half of
|
|
// the group when group size is 4.
|
|
let isFirstInPair = elemIdx % (2 * uniforms.inc) < uniforms.inc;
|
|
var i = 0;
|
|
if (isFirstInPair) {
|
|
i = elemIdx;
|
|
} else {
|
|
i = elemIdx - uniforms.inc;
|
|
}
|
|
|
|
var i0 = 0;
|
|
if (uniforms.firstPass == 1) {
|
|
i0 = i;
|
|
} else {
|
|
i0 = i32(getIndices(batch, i));
|
|
}
|
|
|
|
var i1 = 0;
|
|
if (uniforms.firstPass == 1) {
|
|
i1 = i + uniforms.inc;
|
|
} else {
|
|
i1 = i32(getIndices(batch, i + uniforms.inc));
|
|
}
|
|
|
|
var x0 = f32(0.0);
|
|
var x1 = f32(0.0);
|
|
if (i0 < uniforms.inputSize) {
|
|
x0 = getX(batch, i0);
|
|
} else {
|
|
x0 = uniforms.negativeInf;
|
|
}
|
|
if (i1 < uniforms.inputSize) {
|
|
x1 = getX(batch, i1);
|
|
} else {
|
|
x1 = uniforms.negativeInf;
|
|
}
|
|
|
|
let reverse = elemIdx % (2 * uniforms.dir) >= uniforms.dir;
|
|
let isGreater = x0 > x1 || (x0 == x1 && i1 > i0);
|
|
if (reverse == isGreater) {
|
|
// Elements in opposite order of direction
|
|
let iTemp = i0;
|
|
i0 = i1;
|
|
i1 = iTemp;
|
|
}
|
|
if (isFirstInPair) {
|
|
setOutputFlat(index, f32(i0));
|
|
} else {
|
|
setOutputFlat(index, f32(i1));
|
|
}
|
|
}
|
|
}
|
|
`}},afe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32; firstPass : i32; k : i32;",this.shaderKey="merge"}getUserCode(){return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let outC = getCoordsFromFlatIndex(index);
|
|
let batch = outC[0];
|
|
let elemIdx = outC[1];
|
|
// The output size is half of the previous size.
|
|
// If the previous sequence is | | | | _ _ _ _ | | | | _ _ _ _
|
|
// (k=4), we only need to output the indices at positions |, the
|
|
// indices at positions _ can be thrown away, see Figure5(b) After
|
|
// Phase 2 (Merge phase) in the Bitonic Top K paper referenced
|
|
// above.
|
|
// For example, the paper shows we only need to output the orange
|
|
// bars. The output sequence should look like this | | | | | | | |.
|
|
// Because the sequence is halved, to map the output index back to
|
|
// the previous sequence to find the corresponding value, we need
|
|
// to double the index. When we double the index, we basically
|
|
// interpolate a position, so 2i looks like
|
|
// | _ | _ | _ | _ | _ | _ | _. We move the | to the first k
|
|
// position of each 2k positions by - elemIdx % k. E.g. for output
|
|
// at index 4,5,6,7, we want to get the corresponding element at
|
|
// original index 8,9,10,11, for output at index 8,9,10,11,
|
|
// we want to get the corresponding element at original index
|
|
// 16,17,18,19, so on and so forth.
|
|
|
|
var i = 0;
|
|
if (elemIdx < uniforms.k) {
|
|
i = elemIdx;
|
|
} else {
|
|
i = elemIdx * 2 - elemIdx % uniforms.k;
|
|
}
|
|
var i0 = 0;
|
|
if (uniforms.firstPass == 1) {
|
|
i0 = i;
|
|
} else {
|
|
i0 = i32(getIndices(batch, i));
|
|
}
|
|
var i1 = 0;
|
|
if (uniforms.firstPass == 1) {
|
|
i1 = i + uniforms.k;
|
|
} else {
|
|
i1 = i32(getIndices(batch, i + uniforms.k));
|
|
}
|
|
|
|
let x0 = getX(batch, i0);
|
|
var x1 = f32(0.0);
|
|
if (i1 < uniforms.inputSize) {
|
|
x1 = getX(batch, i1);
|
|
} else {
|
|
x1 = x0;
|
|
}
|
|
|
|
if (x0 >= x1) {
|
|
setOutputFlat(index, f32(i0));
|
|
} else {
|
|
setOutputFlat(index, f32(i1));
|
|
}
|
|
}
|
|
}
|
|
`}};function Ic(e,t){t!==null&&e.disposeData(t.dataId)}function t6(e){let t=1;for(;t<e;)t*=2;return t}function ofe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=r.shape,l=i[i.length-1];if(n.shouldExecuteOnCPU([r])){let w=n.readSync(r.dataId),[C,S]=wce(w,i,r.dtype,a,o);return[n.makeTensorInfo(C.shape,C.dtype,C.values),n.makeTensorInfo(S.shape,S.dtype,S.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,r.dtype,[]),n.makeTensorInfo(i,"int32",[])];if(l===1)return[r,kc({attrs:{shape:i,dtype:"int32",value:0},backend:n})];let u=v.sizeFromShape(i)/l,d=qe({inputs:{x:r},attrs:{shape:[u,l]},backend:n}),p=t6(a),h=t6(l),f=null,m=()=>f===null?[d,d]:[d,f],g=(w,C,S)=>{let E=m(),R=new rfe(S),_=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[w]},{type:"int32",data:[C]}],$=f;f=n.runWebGPUProgram(R,E,"int32",_),Ic(n,$)};for(let w=1;w<p;w*=2){let C=w*2;for(let S=w;S>=1;S/=2)g(C,S,[u,h])}for(let w=h;w>p;w/=2){let C=m(),S=new afe([u,w/2]),R=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[p]}],P=f;f=n.runWebGPUProgram(S,C,"int32",R),Ic(n,P);let _=p/2,$=_*2;for(let T=_;T>=1;T/=2)g($,T,f.shape)}let y=f;f=wc({inputs:{x:f},backend:n,attrs:{begin:0,size:[u,a]}}),Ic(n,y);let x=K4({inputs:{x:d,indices:f},backend:n,attrs:{axis:1,batchDims:1}});Ic(n,d);let A=i.slice(0,-1);A.push(a),y=f,f=qe({inputs:{x:f},attrs:{shape:A},backend:n}),Ic(n,y);let b=x;return x=qe({inputs:{x},attrs:{shape:A},backend:n}),Ic(n,b),[x,f]}var ife={kernelName:Zi,backendName:"webgpu",kernelFunc:ofe},lfe=class{constructor(e){this.variableNames=["Image","Transforms"],this.uniforms="interpolationModeId : i32; fillModeId : i32; fillValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="transform"}getUserCode(){return`
|
|
fn mapCoord(outCoord : f32, len : f32) -> f32{
|
|
var inCoord = outCoord;
|
|
if(uniforms.fillModeId == 2) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
let sz2 = 2.0 * len;
|
|
if (inCoord < sz2) {
|
|
inCoord = sz2 * f32(i32(f32(-inCoord / sz2))) +
|
|
inCoord;
|
|
}
|
|
if (inCoord < -len) {
|
|
inCoord = inCoord + sz2;
|
|
} else {
|
|
inCoord = -inCoord - 1.0;
|
|
}
|
|
}
|
|
} elseif (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
let sz2 = 2.0 * len;
|
|
inCoord = inCoord - sz2 * f32(i32(f32(inCoord / sz2)));
|
|
if (inCoord >= len) {
|
|
inCoord = sz2 - inCoord - 1.0;
|
|
}
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} elseif (uniforms.fillModeId == 3) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
let sz = len - 1.0;
|
|
inCoord = inCoord + len * (f32(i32(f32(-inCoord / sz))) + 1.0);
|
|
}
|
|
} elseif (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
let sz = len - 1.0;
|
|
inCoord = inCoord - len * f32(i32(f32(inCoord / sz)));
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} elseif (uniforms.fillModeId == 4) {
|
|
return clamp(outCoord, 0.0, len - 1.0);
|
|
}
|
|
return outCoord;
|
|
}
|
|
fn readWithFillValue(batch : i32, coordY : i32, coordX : i32,
|
|
channel : i32) -> f32 {
|
|
var outputValue : f32;
|
|
if (0 <= coordY && coordY < uniforms.imageShape[1] && 0 <= coordX && coordX < uniforms.imageShape[2]) {
|
|
outputValue = getImage(batch, coordY, coordX, channel);
|
|
} else {
|
|
outputValue = uniforms.fillValue;
|
|
}
|
|
return outputValue;
|
|
}
|
|
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
var outputValue : f32;
|
|
let batch = coords[0];
|
|
let x = coords[2];
|
|
let y = coords[1];
|
|
let channel = coords[3];
|
|
let xf = f32(x);
|
|
let yf = f32(y);
|
|
let a1 = getTransforms(batch, 0);
|
|
let a2 = getTransforms(batch, 1);
|
|
let a3 = getTransforms(batch, 2);
|
|
let b1 = getTransforms(batch, 3);
|
|
let b2 = getTransforms(batch, 4);
|
|
let b3 = getTransforms(batch, 5);
|
|
let c1 = getTransforms(batch, 6);
|
|
let c2 = getTransforms(batch, 7);
|
|
let projection = c1 * xf + c2 * yf + 1.0;
|
|
if (projection == 0.0) {
|
|
outputValue = uniforms.fillValue;
|
|
} else {
|
|
let inX = (a1 * xf + a2 * yf + a3) / projection;
|
|
let inY = (b1 * xf + b2 * yf + b3) / projection;
|
|
let mapX = mapCoord(inX, f32(uniforms.imageShape[2]));
|
|
let mapY = mapCoord(inY, f32(uniforms.imageShape[1]));
|
|
|
|
if (uniforms.interpolationModeId == 1) {
|
|
let coordY = i32(round(mapY));
|
|
let coordX = i32(round(mapX));
|
|
outputValue = readWithFillValue(batch, coordY, coordX,
|
|
channel);
|
|
} else {
|
|
let yFloor = floor(mapY);
|
|
let xFloor = floor(mapX);
|
|
let yCeil = yFloor + 1.0;
|
|
let xCeil = xFloor + 1.0;
|
|
let valueYFloor = (xCeil - mapX) *
|
|
readWithFillValue(batch, i32(yFloor), i32(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, i32(yFloor), i32(xCeil), channel);
|
|
let valueYCeil = (xCeil - mapX) *
|
|
readWithFillValue(batch, i32(yCeil), i32(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, i32(yCeil), i32(xCeil), channel);
|
|
outputValue = (yCeil - mapY) * valueYFloor +
|
|
(mapY - yFloor) * valueYCeil;
|
|
}
|
|
}
|
|
setOutputFlat(index, outputValue);
|
|
}
|
|
}
|
|
`}};function ufe(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=s,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],y=new lfe(g),x=o==="nearest"?1:2,A;switch(i){case"constant":A=1;break;case"reflect":A=2;break;case"wrap":A=3;break;case"nearest":A=4;break;default:A=1;break}let b=[{type:"int32",data:[x]},{type:"int32",data:[A]},{type:"float32",data:[l]}];return n.runWebGPUProgram(y,[r,a],"float32",b)}var cfe={kernelName:Yi,backendName:"webgpu",kernelFunc:ufe};function dfe(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=wc({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),y=qe({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=y,d.push(g)}return d.forEach(m=>n.disposeData(m.dataId)),f}var pfe={kernelName:Ji,backendName:"webgpu",kernelFunc:dfe},hfe=[Vue,Ice,Tce,Rce,Oce,zce,Bce,Vce,qce,Yce,Qce,sde,jue,ide,dde,mde,yde,xde,wde,Ide,Tde,Dde,Fde,Ode,zde,Mde,Bde,Vde,Gde,Zde,jde,Xde,Qde,tpe,spe,ope,upe,dpe,hpe,Hue,ade,mpe,ype,xpe,vpe,kpe,Spe,Cpe,Npe,Rpe,Dpe,Fpe,Ope,Nde,zpe,Bpe,Vpe,Xce,Gpe,jpe,Xpe,Jpe,ehe,Zpe,nhe,Kce,she,ahe,ihe,uhe,Bue,phe,mhe,yhe,xhe,whe,Ihe,The,Ehe,_he,Hce,Khe,Yhe,Fhe,Ohe,Whe,Lhe,Uhe,Ghe,jhe,Dhe,Rde,Qhe,sfe,ife,cfe,Fce,pfe,Upe];for(let e of hfe)cr(e);var ffe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t){let n=n6(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let r=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(r),r}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,n){if(this.freeBuffers==null)return;let s=n6(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}reset(){this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}dispose(){this.freeBuffers==null&&this.usedBuffers==null||(this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=null,this.usedBuffers=null,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0)}};function n6(e,t){return`${e}_${t}`}var s6=class{constructor(){this.outputShape=[0],this.variableNames=[],this.workGroupSize=[256,1,1],this.lastUniformData=[],this.inputTexture=null,this.layout=null,this.lastPixelSize={width:0,height:0},this.disposed=!1,this.shaderKey="fromPixels",this.useImport=!1}updateOutputShape(e){v.arraysEqual(this.outputShape,e)||(this.outputShape=e,this.workPerThread=e[2],this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]))}makeFromPixelsSource(){let e=this.useImport?"textureLoad(src, vec2<i32>(coords.yx));":"textureLoad(src, vec2<i32>(coords.yx), 0)";return`
|
|
[[binding(1), group(0)]] var src: ${this.useImport?"texture_external":"texture_2d<f32>"};
|
|
|
|
${Ke()}
|
|
let flatIndexBase = index * uniforms.numChannels;
|
|
for (var i = 0; i < uniforms.numChannels; i = i + 1) {
|
|
let flatIndex = flatIndexBase + i;
|
|
if (flatIndex < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(flatIndexBase);
|
|
let values = ${e};
|
|
result.numbers[flatIndex] = i32(floor(255.0 * values[i]));
|
|
}
|
|
}
|
|
}
|
|
`}getUserCode(){return this.makeFromPixelsSource()}setPipeline(e){this.pipeline=e}setUniform(e,t){if(!this.uniform){let n=e.createBuffer({size:t.length*4,usage:GPUBufferUsage.UNIFORM|GPUBufferUsage.COPY_DST});this.uniform=n}!t||t.length===this.lastUniformData.length&&t.every((n,s)=>n===this.lastUniformData[s])||(e.queue.writeBuffer(this.uniform,0,new Uint32Array(t)),this.lastUniformData=t)}makeInputTexture(e,t,n){return(!this.inputTexture||this.lastPixelSize.width!==t||this.lastPixelSize.height!==n)&&(this.inputTexture&&this.inputTexture.destroy(),this.inputTexture=e.createTexture({size:[t,n],format:"rgba8unorm",usage:GPUTextureUsage.COPY_DST|GPUTextureUsage.RENDER_ATTACHMENT|GPUTextureUsage.TEXTURE_BINDING}),this.lastPixelSize.width=t,this.lastPixelSize.height=n),this.inputTexture}dispose(){this.disposed||(this.uniform&&this.uniform.destroy(),this.inputTexture&&this.inputTexture.destroy(),this.disposed=!0)}getLayout(e){return this.layout===null&&(this.layout=this.createTextureLayout(e)),this.layout}createTextureLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,texture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},mfe=class extends s6{constructor(){super(...arguments);this.layout=null,this.useImport=!0}getUserCode(){return this.makeFromPixelsSource()}getLayout(e){return this.layout===null&&(this.layout=this.createTextureImportLayout(e)),this.layout}createTextureImportLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}}),t.push({binding:1,visibility:GPUShaderStage.COMPUTE,externalTexture:{}}),t.push({binding:2,visibility:GPUShaderStage.COMPUTE,buffer:{}});let n=e.createBindGroupLayout({entries:t}),s=e.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}},gfe=Y().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),r6=class extends su{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Dx())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new ffe(this.device),this.tensorMap=new od(this,as()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(this.dummyCanvas=document.createElement("canvas"),this.dummyCanvas.width=1,this.dummyCanvas.height=1,this.dummyContext=this.dummyCanvas.getContext("webgpu"),this.dummyContext.configure({device:e,format:"bgra8unorm"}),document.body.appendChild(this.dummyCanvas))}nextDataId(){return r6.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=v.sizeFromShape(t)*_x(n);return n==="bool"&&e instanceof Uint8Array&&(e=Int32Array.from(e)),this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=v.sizeFromShape(n)*_x(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new s6),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new mfe),this.fromPixelImportProgram;default:v.assert(!1,()=>"Unsupported fromPixels shape");return}}ensureCommandEncoderReady(){this.currentCommandEncoder||(this.currentCommandEncoder=this.device.createCommandEncoder())}ensureComputePassEnded(){this.currentComputePass&&(this.currentComputePass.endPass(),this.currentComputePass=null)}getComputePass(){return this.currentComputePass||(this.currentComputePass=this.currentCommandEncoder.beginComputePass()),this.currentComputePass}async getBufferData(e){if(e.values!=null)return e.values;let t=this.acquireBuffer(e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.copyBufferToBuffer(e.bufferInfo.buffer,0,t,0,e.bufferInfo.byteSize),this.submitQueue(),await t.mapAsync(GPUMapMode.READ);let n=t.getMappedRange().slice(0);return t.unmap(),t!=null&&this.bufferManager.releaseBuffer(t,e.bufferInfo.byteSize,GPUBufferUsage.COPY_DST|GPUBufferUsage.MAP_READ),Y().getBool("WEBGPU_USE_PROFILE_TOOL")&&(v.assert(this.dummyContext!==void 0,()=>"Fail to get context for profiling tool"),this.dummyContext.getCurrentTexture()),n}convertAndCacheOnCPU(e,t){let n=this.tensorMap.get(e);return this.maybeReleaseBuffer(e),n.values=t,n.values}readSync(e){let t=this.tensorMap.get(e),{values:n}=t;if(n==null)throw new Error("WebGPU readSync is only available for CPU-resident tensors.");return n}async read(e){if(!this.tensorMap.has(e))throw new Error(`Tensor ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=N.mergeRealAndImagArrays(a,o)}else{let r=await this.getBufferData(t);s=I4(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${l.name}: ${l.ms}`).join(", "),this.uploadWaitMs=0,this.downloadWaitMs=0,o}getAndSavePipeline(e,t){return e in this.pipelineCache||(this.pipelineCache[e]=t()),this.pipelineCache[e]}makeTensorInfo(e,t,n){let s;if(t==="string"&&n!=null&&n.length>0&&v.isString(n[0])){let r=n.map(a=>v.encodeString(a));s=this.write(r,e,t)}else s=this.write(n,e,t);return{dataId:s,shape:e,dtype:t}}tensorToBinding(e){if(!e)return null;let t=this.tensorMap.get(e.dataId);return{offset:0,size:t.bufferInfo.byteSize,buffer:t.bufferInfo.buffer}}async getQueryTime(e){return this.supportTimeQuery?this.getTimeFromQuerySet(e):0}uploadToGPU(e){let t=this.tensorMap.get(e);t.bufferInfo.buffer==null&&(t.bufferInfo.buffer=this.acquireBuffer(t.bufferInfo.byteSize),t.values&&this.queue.writeBuffer(t.bufferInfo.buffer,0,t.values))}makeUniformsDataView(e){let t=this.acquireBuffer(e.byteLength,GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM);return this.queue.writeBuffer(t,0,e),{offset:0,size:e.byteLength,buffer:t}}arrayToDataView(e,t){let n=4,s=new DataView(new ArrayBuffer(t*n)),r=0;return e.forEach(a=>{let o=a.data;if(a.type!=="int32"&&a.type!=="float32"&&a.type!=="uint32")throw new Error(`${a.type} not supported!`);a.type==="int32"?o.forEach(i=>{s.setInt32(r*n,i,!0),r++}):a.type==="uint32"?o.forEach(i=>{s.setUint32(r*n,i,!0),r++}):o.forEach(i=>{s.setFloat32(r*n,i,!0),r++})}),s}computePadding(e){let t=0,n=0,s=0,r=[];return e.forEach((a,o)=>{a.data.length===0&&(a.data=[1]);let i;switch(a.data.length){case 0:i=1;break;case 1:i=1;break;case 2:i=2;break;case 3:i=4;break;case 4:i=4;break;default:v.assert(!1,()=>`Unsupported ${a.data.length}D shape`)}n=Math.ceil(t/i)*i-t;for(let l=0;l<n;++l)r.push({type:a.type,data:[0]}),s++;r.push({type:a.type,data:a.data}),s=s+a.data.length,t+=a.data.length+n}),this.arrayToDataView(r,s)}createLayout(e){let t=[];t.push({binding:0,visibility:GPUShaderStage.COMPUTE,buffer:{type:"storage"}});for(let r=0;r<e;r++)t.push({binding:r+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"read-only-storage"}});t.push({binding:e+1,visibility:GPUShaderStage.COMPUTE,buffer:{type:"uniform"}});let n=this.device.createBindGroupLayout({entries:t}),s=this.device.createPipelineLayout({bindGroupLayouts:[n]});return{bindGroupLayout:n,pipelineLayout:s}}getCachedOrCreateLayout(e){return e in this.layoutCache||(this.layoutCache[e]=this.createLayout(e)),this.layoutCache[e]}runWebGPUProgram(e,t,n,s,r){if(!r){if(r=this.makeTensorInfo(e.outputShape,n),v.sizeFromShape(r.shape)===0){let E=this.tensorMap.get(r.dataId);return E.values=v.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}let a=[{type:"float32",data:[NaN]}],o=t.concat(r).map(E=>E.shape),i="int32";o.map(E=>{a.push({type:i,data:E})});let l=v.computeStrides(r.shape);if(a.push({type:i,data:l}),e.size){let E=v.sizeFromShape(e.outputShape);a.push({type:i,data:[e.isVec4?E/4:E]})}s&&(a=[...a,...s]);let c=null,u=this.computePadding(a),d=u.byteLength;c=this.makeUniformsDataView(u);let p=t.map((E,R)=>{if(E.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");return this.uploadToGPU(E.dataId),{dtype:this.tensorMap.get(E.dataId).dtype,shape:E.shape,name:e.variableNames[R]}}),h=p.map(E=>E.dtype).concat(r.dtype),f=p.map(E=>N.getBroadcastDims(E.shape,r.shape)),m=p.map(E=>v.arraysEqual(E.shape,r.shape)).join("_"),g=f.map(E=>E.join("_")).join(";"),y=q4(e,o,h,g,m),{bindGroupLayout:x,pipelineLayout:A}=this.getCachedOrCreateLayout(e.variableNames.length),b=this.getAndSavePipeline(y,()=>j4(this.device,e,A,p,r)),w=this.activeTimers!=null,C=Kde(this.device,x,t.map(E=>this.tensorToBinding(E)),this.tensorToBinding(r),c);this.ensureCommandEncoderReady();let S=this.getComputePass();if(w&&this.supportTimeQuery&&S.writeTimestamp(this.querySet,0),S.setPipeline(b),S.setBindGroup(0,C),S.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),w&&this.supportTimeQuery&&S.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(E=>{this.commandQueueOwnedIds.add(E.dataId)}),this.commandQueueOwnedIds.add(r.dataId),c){let E={byteSize:d,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:c.buffer};this.uniformDisposalQueue.push(E)}return Y().get("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE")<=this.dispatchNumberInEncoder&&this.submitQueue(),w&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)}),r}runFromPixelsProgram(e,t,n,s,r){let a=this.device.createBindGroup({layout:n.bindGroupLayout,entries:[{binding:0,resource:{buffer:t}},{binding:1,resource:s},{binding:2,resource:{buffer:e.uniform}}]});this.ensureCommandEncoderReady();let o=this.getComputePass(),i=this.activeTimers!=null;i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,0),o.setPipeline(e.pipeline),o.setBindGroup(0,a),o.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),i&&this.supportTimeQuery&&o.writeTimestamp(this.querySet,1),this.commandQueueOwnedIds.add(r),this.submitQueue(),i&&this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(this.querySet)})}async getTimeFromQuerySet(e){let t=this.acquireBuffer(16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),n=this.acquireBuffer(16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST);this.ensureCommandEncoderReady(),this.ensureComputePassEnded(),this.currentCommandEncoder.resolveQuerySet(e,0,2,t,0),this.currentCommandEncoder.copyBufferToBuffer(t,0,n,0,16),this.submitQueue(),await n.mapAsync(GPUMapMode.READ);let s=new BigUint64Array(n.getMappedRange()),r=Number(s[1]-s[0]);return n.unmap(),this.bufferManager.releaseBuffer(n,16,GPUBufferUsage.MAP_READ|GPUBufferUsage.COPY_DST),this.bufferManager.releaseBuffer(t,16,GPUBufferUsage.COPY_SRC|GPUBufferUsage.QUERY_RESOLVE),r/1e6}shouldExecuteOnCPU(e,t=gfe){return Y().getBool("WEBGPU_CPU_FORWARD")&&e.every(n=>this.tensorMap.get(n.dataId).bufferInfo.buffer==null&&v.sizeFromShape(n.shape)<t)}numDataIds(){return this.tensorMap.numDataIds()-this.tensorDisposalQueue.length}dispose(){this.disposed||(this.bufferManager.dispose(),this.fromPixelProgram&&this.fromPixelProgram.dispose(),this.fromPixelImportProgram&&this.fromPixelImportProgram.dispose(),this.disposed=!0)}},Bx=r6;Bx.nextDataId=0;var a6={};Me(a6,{WebGPUBackend:()=>Bx,webgpu_util:()=>S4});Ou.isBrowser()&&Dx()&&ul("webgpu",async()=>{Y().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:Y().get("WEBGPU_USE_LOW_POWER_GPU")?"low-power":"high-performance"},t=await navigator.gpu.requestAdapter(e),n={},s=t.features.has("timestamp-query");s?n={requiredFeatures:["timestamp-query"]}:console.warn("This device doesn't support timestamp-query extension. Start Chrome browser with flag --disable-dawn-features=disallow_unsafe_apis then try again. Or zero will shown for the kernel time when profiling mode isenabled. Using performance.now is not workable for webgpu sinceit doesn't support synchronously to read data from GPU.");let r=await t.requestDevice(n);return new Bx(r,s)},3);var qt=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(qt||{}),a0=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(a0||{}),o6;function yfe(e){o6=e.wasm.cwrap(Io,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Afe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s,p=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(a.dataId).id,f=0;if(o!=null){let E=n.dataIdMap.get(o.dataId);if(E.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${E.shape.length}.`);f=E.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=a0[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?r.shape[2]:r.shape[1],x=c?a.shape[1]:a.shape[2],A=ol.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),b=n.makeOutput([...A,y,x],r.dtype),w=n.dataIdMap.get(b.dataId).id,C=new Uint8Array(new Int32Array(r.shape).buffer),S=new Uint8Array(new Int32Array(a.shape).buffer);return o6(p,C,r.shape.length,h,S,a.shape.length,l,c,g,f,m,d||0,w),b}var xfe={kernelName:Io,backendName:"wasm",setupFunc:yfe,kernelFunc:Afe};function Tn(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:o,inputs:{x:i}}=a,l=o.dataIdMap.get(i.dataId).id,c=o.makeOutput(i.shape,t||i.dtype),u=o.dataIdMap.get(c.dataId).id;return v.sizeFromShape(c.shape)===0||n(l,qt[i.dtype],u),c}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var bfe=Tn(mi);function Xn(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:c,b:u}=l,d=i.dataIdMap.get(c.dataId).id,p=i.dataIdMap.get(u.dataId).id,h=n!=null?n:c.dtype,f=N.assertAndGetBroadcastShape(c.shape,u.shape),m=i.makeOutput(f,h);if(v.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),x=i.dataIdMap.get(m.dataId).id;return(()=>s(d,g,c.shape.length,p,y,u.shape.length,qt[c.dtype],x))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var vfe=!0,wfe=Xn(Kr,vfe),i6;function kfe(e){i6=e.wasm.cwrap(Da,null,["array","number","number","number"])}function Sfe(e){let{inputs:t,backend:n}=e,s=n.makeOutput(t[0].shape,t[0].dtype);if(v.sizeFromShape(s.shape)===0)return s;let r=t.map(i=>n.dataIdMap.get(i.dataId).id),a=new Uint8Array(new Int32Array(r).buffer),o=n.dataIdMap.get(s.dataId).id;return i6(a,r.length,qt[s.dtype],o),s}var Ife={kernelName:Da,backendName:"wasm",setupFunc:kfe,kernelFunc:Sfe};function o0(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(s).set(r),s}var Cfe={kernelName:Za,backendName:"wasm",kernelFunc:o0},l6;function Tfe(e){l6=e.wasm.cwrap(ko,null,["number","array","number","number","number","array","number"])}function Cc(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=Efe(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=Nfe(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=o0({inputs:t,backend:n});return f.shape=i,f}let c=n.makeOutput(i,l.dtype),u=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(c.dataId).id,p=new Uint8Array(new Int32Array(a).buffer),h=new Uint8Array(new Int32Array(l.shape).buffer);return l6(u,h,l.shape.length,qt[l.dtype],d,p,a.length),c}function Nfe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function Efe(e,t){let n=[],s=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&s.push(t[r]);for(let r=0;r<s.length;++r){let a=-1;for(let o=0;o<s.length;++o)s[o]>=r&&(a===-1||s[a]>s[o])&&(a=o);s[a]=r}return[n,s]}var Rfe={kernelName:ko,backendName:"wasm",kernelFunc:Cc,setupFunc:Tfe};function Yo(e,t,n){let s=e.shape,r=e.shape.length,a=v.parseAxisParam(t,s),o=a,i=N.getAxesPermutation(o,r),l=null,c=!1;if(i!=null){let u=new Array(r);for(let h=0;h<u.length;h++)u[h]=s[i[h]];o=N.getInnerMostAxes(o.length,r),l=Cc({inputs:{x:e},attrs:{perm:i},backend:n});let d=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(l.dataId).id!==d&&(c=!0)}return{transposed:l,originalAxes:a,axes:o,inputWasTransposed:c}}var u6;function _fe(e){u6=e.wasm.cwrap(uu,null,["number, number, number"])}function Dfe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Yo(o,r,t);if(h){let A=t.dataIdMap.get(u.dataId).id;c=u,l=A}let f=c.shape.length;N.assertAxesAreInnerMostDims("all",d,f);let[m,g]=N.computeOutAndReduceShapes(c.shape,d),y=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;u6(l,y,A)}if(h&&t.disposeData(u.dataId),a){let A=N.expandShapeToKeepDim(x.shape,p);x.shape=A}return x}var $fe={kernelName:uu,backendName:"wasm",setupFunc:_fe,kernelFunc:Dfe},c6;function Ffe(e){c6=e.wasm.cwrap(cu,null,["number, number, number"])}function Pfe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=Yo(o,r,t);if(h){let A=t.dataIdMap.get(u.dataId).id;c=u,l=A}let f=c.shape.length;N.assertAxesAreInnerMostDims("any",d,f);let[m,g]=N.computeOutAndReduceShapes(c.shape,d),y=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;c6(l,y,A)}if(h&&t.disposeData(u.dataId),a){let A=N.expandShapeToKeepDim(x.shape,p);x.shape=A}return x}var Ofe={kernelName:cu,backendName:"wasm",setupFunc:Ffe,kernelFunc:Pfe},d6;function Mfe(e){d6=e.wasm.cwrap($a,null,["number","number","number","number","number"])}function zfe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r}=s,{x:a}=n,o=t.dataIdMap.get(a.dataId).id,i=o,l=a,{transposed:c,axes:u,inputWasTransposed:d}=Yo(a,r,t);if(d){let y=t.dataIdMap.get(c.dataId).id;y!==o&&(l=c,i=y)}let p=l.shape.slice(0,-1),h=t.makeOutput(p,"int32"),f=t.dataIdMap.get(h.dataId).id,m=v.sizeFromShape(h.shape),g=l.shape[u[0]];return d6(i,qt[l.dtype],m,g,f),d&&t.disposeData(c.dataId),h}var Lfe={kernelName:$a,backendName:"wasm",kernelFunc:zfe,setupFunc:Mfe},p6;function Bfe(e){p6=e.wasm.cwrap(Fa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Wfe(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id,{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=n,u=N.computePool2DInfo(r.shape,o,i,1,l,c),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,y=u.strideHeight,x=u.strideWidth,A=u.inChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);if(u.dilationWidth!==1||u.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${u.dilationHeight}, ${u.dilationWidth}].`);let b=s.makeOutput(u.outShape,"float32"),w=s.dataIdMap.get(b.dataId).id;return p6(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,y,x,A,w),b}var Vfe={kernelName:Fa,backendName:"wasm",setupFunc:Bfe,kernelFunc:Wfe};function ps(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a);return v.assert(a===v.sizeFromShape(o),()=>`new shape: ${o}, old shape: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Ufe={kernelName:Li,backendName:"wasm",kernelFunc:ps},h6;function Gfe(e){h6=e.wasm.cwrap(Pa,null,["number","array","number","number","array","number","number","number","number"])}function Hfe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=a.shape.length,u=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[c-1]:a.shape[c-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[c-2]:a.shape[c-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),y=v.sizeFromShape(m),A=ol.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([p,h]);v.assert(u===d,()=>`Error in matMul: inner shapes (${u}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let b=o?[g,u,p]:[g,p,u],w=i?[y,h,d]:[y,d,h],C=ps({inputs:{x:r},backend:n,attrs:{shape:b}}),S=ps({inputs:{x:a},backend:n,attrs:{shape:w}}),E=n.dataIdMap.get(C.dataId).id,R=n.dataIdMap.get(S.dataId).id,P=o?C.shape[2]:C.shape[1],_=i?S.shape[1]:S.shape[2],$=Math.max(g,y),T=n.makeOutput([$,P,_],C.dtype),O=n.dataIdMap.get(T.dataId).id,V=new Uint8Array(new Int32Array(C.shape).buffer),K=new Uint8Array(new Int32Array(S.shape).buffer);return h6(E,V,C.shape.length,R,K,S.shape.length,o,i,O),n.disposeData(C.dataId),n.disposeData(S.dataId),T.shape=A,T}var jfe={kernelName:Pa,backendName:"wasm",setupFunc:Gfe,kernelFunc:Hfe};function Ml(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=Mt.parseSliceParams(t,n,s),i=Mt.isSliceContinous(t.shape,a,o),l=r.readSync(t.dataId),c=r.makeOutput(o,t.dtype),u=v.computeStrides(t.shape),d=r.dataIdMap.get(c.dataId);if(i){let f=Mt.computeFlatOffset(a,u);return t.dtype==="string"?d.stringBytes=l.slice(f,f+v.sizeFromShape(o)):r.typedArrayFromHeap(c).set(l.subarray(f,f+v.sizeFromShape(o))),c}if(t.dtype==="string"){let f=_m(l,a,o,t.shape,t.dtype);return d.stringBytes=f,c}let p=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)qfe(l,u[0],p,a,o);else if(h===3)Xfe(l,u[0],u[1],p,a,o);else if(h===4)Kfe(l,u[0],u[1],u[2],p,a,o);else{let f=_m(l,a,o,t.shape,t.dtype);p.set(f)}return c}function qfe(e,t,n,s,r){let a=0,o=s[0],i=s[1],l=o+r[0];for(let c=o;c<l;c++){let u=c*t+i;n.set(e.subarray(u,u+r[1]),a),a+=r[1]}}function Xfe(e,t,n,s,r,a){let o=0,i=r[0],l=r[1],c=r[2],u=i+a[0],d=l+a[1];for(let p=i;p<u;p++)for(let h=l;h<d;h++){let f=p*t+h*n+c;s.set(e.subarray(f,f+a[2]),o),o+=a[2]}}function Kfe(e,t,n,s,r,a,o){let i=0,l=a[0],c=a[1],u=a[2],d=l+o[0],p=c+o[1],h=u+o[2],f=a[3];for(let m=l;m<d;m++)for(let g=c;g<p;g++)for(let y=u;y<h;y++){let x=m*t+g*n+y*s+f;r.set(e.subarray(x,x+o[3]),i),i+=o[3]}}var Zfe={kernelName:Gi,backendName:"wasm",kernelFunc:Ml};function Yfe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((y,x)=>y*x),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=ps({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Cc({inputs:{x:h},backend:n,attrs:{perm:c}}),m=ps({inputs:{x:f},backend:n,attrs:{shape:u}}),g=Ml({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(h.dataId),g}var Jfe={kernelName:gi,backendName:"wasm",kernelFunc:Yfe};function Fp(e){let{inputs:{x:t},attrs:{dtype:n},backend:s}=e,r=s.makeOutput(t.shape,n),a=s.typedArrayFromHeap(t);return s.typedArrayFromHeap(r).set(a),r}var Qfe={kernelName:Oa,backendName:"wasm",kernelFunc:Fp},eme=Tn(Ma),f6;function tme(e){f6=e.wasm.cwrap(Zr,null,["number","number","number","number"])}function nme(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i=n.dataIdMap.get(r.dataId).id,l=n.makeOutput(r.shape,r.dtype),c=n.dataIdMap.get(l.dataId).id;return f6(i,a,o,c),l}var sme={kernelName:Zr,backendName:"wasm",setupFunc:tme,kernelFunc:nme};function m6(e){let{inputs:t,backend:n}=e,s=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=N.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>v.sizeFromShape(h.shape)>0);if(a.length===1)return o0({inputs:{x:a[0]},backend:n});let o=n.makeOutput(r,t[0].dtype);if(v.sizeFromShape(r)===0)return o;let i=a.map(h=>h.shape);if(N.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(A=>{let b=v.sizeFromShape(A.shape.slice(s));return ps({inputs:{x:A},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(A=>({vals:n.readSync(A.dataId),shape:A.shape}));r=N.computeOutShape(h.map(A=>A.shape),1);let m=h[0].shape[0]===1,g=KA(f,r,t[0].dtype,m),y=N.computeOutShape(a.map(A=>A.shape),s);o.shape=y;let x=n.dataIdMap.get(o.dataId);return 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ome(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a}=t,o=s.dataIdMap.get(r.dataId).id,i=s.dataIdMap.get(a.dataId).id,{strides:l,dilations:c,pad:u,dimRoundingMode:d,dataFormat:p}=n,h=N.convertConv2DDataFormat(p),f=N.computeConv2DInfo(r.shape,a.shape,l,c,u,d,!1,h),m=f.filterHeight,g=f.filterWidth,y=f.padInfo.top,x=f.padInfo.right,A=f.padInfo.bottom,b=f.padInfo.left,w=f.dilationHeight,C=f.dilationWidth,S=f.strideHeight,E=f.strideWidth,R=f.inChannels,P=f.outChannels,_=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. 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Please use 'channelsLast'.`);let _=s.makeOutput(h.outShape,"float32"),$=s.dataIdMap.get(_.dataId).id;return w6(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,y,x,A,P,b,w,C,S,E,R,$),_}var Sme={kernelName:Va,backendName:"wasm",setupFunc:wme,kernelFunc:kme},Ime=Tn(Ga),Cme=!1,Tme=Xn(vi,Cme,"bool"),Nme=Tn(Ha,"float32");function Wx(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(v.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),ps({inputs:{x:r},backend:s,attrs:{shape:i}})}var Eme={kernelName:wi,backendName:"wasm",kernelFunc:Wx};function k6(e){let{attrs:{shape:t,value:n,dtype:s},backend:r}=e,a=r.makeOutput(t,s);return r.typedArrayFromHeap(a).fill(n),a}var Rme={kernelName:Au,backendName:"wasm",kernelFunc:k6},S6;function _me(e){S6=e.wasm.cwrap(Si,null,["number","number","number","number","number","number"])}function Dme(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,c,u]=s.shape;return S6(a,i,l,c,u,o),r}var $me={kernelName:Si,backendName:"wasm",kernelFunc:Dme,setupFunc:_me},Fme=Tn(ja),Pme=!1,Ome=Xn(qa,Pme),I6;function Mme(e){I6=e.wasm.cwrap(Xa,null,["number","number","number","number","number","number","number"])}function zme(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:c}=n,u=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return I6(u,d,p,h,f,r,g),m}var Lme={kernelName:Xa,backendName:"wasm",setupFunc:Mme,kernelFunc:zme},C6;function Bme(e){C6=e.wasm.cwrap(Co,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Wme(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,l,u,c,p),g=a0[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,A=m.outChannels,b=0;if(o!=null){let Z=s.dataIdMap.get(o.dataId);if(Z.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==A)throw new Error(`FusedConv2D bias shape (${Z.shape}) does not match the number of output channels (${A})`);b=Z.id}let w=m.filterHeight,C=m.filterWidth,S=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,P=m.padInfo.left,_=m.dilationHeight,$=m.dilationWidth,T=m.strideHeight,O=m.strideWidth,V=m.inChannels,K=m.padInfo.type==="SAME"?1:0,z=m.batchSize,j=m.inHeight,W=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let Q=s.makeOutput(m.outShape,"float32"),te=s.dataIdMap.get(Q.dataId).id,ae=i==null?0:s.dataIdMap.get(i.dataId).id;return C6(y,z,j,W,x,w,C,b,S,E,R,P,K,_,$,T,O,V,A,g,ae,f||0,te),Q}var Vme={kernelName:Co,backendName:"wasm",setupFunc:Bme,kernelFunc:Wme},T6;function Ume(e){T6=e.wasm.cwrap(To,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Gme(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,l,u,c,p,!0),g=a0[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,A=m.outChannels,b=0;if(o!=null){let Z=s.dataIdMap.get(o.dataId);if(Z.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==A)throw new Error(`FusedDepthwiseConv2D bias shape (${Z.shape}) does not match the number of output channels (${A})`);b=Z.id}let w=m.filterHeight,C=m.filterWidth,S=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,P=m.padInfo.left,_=m.dilationHeight,$=m.dilationWidth,T=m.strideHeight,O=m.strideWidth,V=m.inChannels,K=m.padInfo.type==="SAME"?1:0,z=m.batchSize,j=m.inHeight,W=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. 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I0e={kernelName:so,backendName:"wasm",setupFunc:k0e,kernelFunc:S0e},C0e=!1,T0e=Xn(ro,C0e),P6=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(P6||{}),O6;function N0e(e){O6=e.wasm.cwrap(ao,null,["number","array","number","number","array","array","number","number"])}function E0e(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=s.map(f=>f[0]),d=s.map(f=>f[1]),p=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(d).buffer);return O6(o,c,t.shape.length,qt[t.dtype],p,h,P6[r],l),i}var R0e={kernelName:ao,backendName:"wasm",kernelFunc:E0e,setupFunc:N0e},_0e=!0,D0e=Xn(oo,_0e),$0e=Tn(_i);function Vx(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var M6;function F0e(e){M6=e.wasm.cwrap($i,"number",["number","number","number","number","number"])}function P0e(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,c=t.dataIdMap.get(i.dataId).id,u=t.dataIdMap.get(l.dataId).id,d=M6(c,u,a,r,o),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Vx(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var O0e={kernelName:$i,backendName:"wasm",setupFunc:F0e,kernelFunc:P0e},z6;function M0e(e){z6=e.wasm.cwrap(Iu,"number",["number","number","number","number","number","bool"])}function z0e(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=z6(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Vx(t,p);t.wasm._free(m);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[y,x]}var L0e={kernelName:Iu,backendName:"wasm",setupFunc:M0e,kernelFunc:z0e},L6;function B0e(e){L6=e.wasm.cwrap(Fi,"number",["number","number","number","number","number","number"])}function W0e(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=L6(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Vx(t,p);t.wasm._free(g);let y=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[y,x]}var V0e={kernelName:Fi,backendName:"wasm",setupFunc:B0e,kernelFunc:W0e},U0e=!1,G0e=Xn(Di,U0e,"bool"),B6;function H0e(e){B6=e.wasm.cwrap(Oi,null,["number","number","number","number","number"])}function j0e(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=n.makeOutput([...r.shape,a],"int32"),c=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return B6(d,a,o,i,c),l}var q0e={kernelName:Oi,backendName:"wasm",setupFunc:H0e,kernelFunc:j0e};function X0e(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var K0e={kernelName:Pi,backendName:"wasm",kernelFunc:X0e};function Z0e(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Wx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=Wx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=m6({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var Y0e={kernelName:Mi,backendName:"wasm",kernelFunc:Z0e},W6;function J0e(e){W6=e.wasm.cwrap(io,null,["number","array","number","number","array","array","number","number"])}function Q0e(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(v.sizeFromShape(t.shape)===0)return k6({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),c=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=s.map(m=>m[0]),p=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(d).buffer),f=new Uint8Array(new Int32Array(p).buffer);return W6(o,u,t.shape.length,qt[t.dtype],h,f,r,c),i}var V6={kernelName:io,backendName:"wasm",kernelFunc:Q0e,setupFunc:J0e},ege=!1,tge=Xn(lo,ege),U6;function nge(e){U6=e.wasm.cwrap(uo,null,["number","number","number"])}function 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Lge={kernelName:ji,backendName:"wasm",kernelFunc:zge},J6;function Bge(e){J6=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function Wge(e){let{backend:t,inputs:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=n,i=s.shape[0],l=s.shape[1],c=t.readSync(a.dataId)[0],u=[i+c,l],d=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(o.dataId).id,f=t.makeOutput(u,s.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(u.slice(0,1),r.dtype),y=t.dataIdMap.get(g.dataId).id,x=t.makeOutput([c],"bool"),A=t.dataIdMap.get(x.dataId).id,b=t.makeOutput([i],s.dtype),w=t.dataIdMap.get(b.dataId).id,C=t.makeOutput([4],"int32"),S=t.dataIdMap.get(C.dataId).id,E=J6(d,p,qt[r.dtype],i,c,l,h,m,y,A,w,S),R=t.readSync(C.dataId),P;switch(R[0]){case 1:{P=N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);break}case 2:{P=N.getSparseFillEmptyRowsNegativeIndexErrorMessage(R[1],R[2]);break}case 3:P=N.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(R[1],R[2],R[3]);break;default:P=""}if(t.disposeData(C.dataId),P)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(x.dataId),t.disposeData(b.dataId),new Error(P);let _=f,$=g;return E!==u[0]&&(_=Ml({inputs:{x:f},attrs:{begin:0,size:[E,l]},backend:t}),$=Ml({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[_,$,x,b]}var Vge={kernelName:bd,backendName:"wasm",setupFunc:Bge,kernelFunc:Wge},Q6;function Uge(e){Q6=e.wasm.cwrap(Du,null,["number","number","number","number","number","number","number"])}function Gge(e){let{backend:t,inputs:n}=e,{inputIndices:s,inputShape:r,newShape:a}=n;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
|
|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
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|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=t.dataIdMap.get(s.dataId).id,i=t.dataIdMap.get(r.dataId).id,l=t.dataIdMap.get(a.dataId).id,c=s.shape[0],u=v.sizeFromShape(a.shape),d=t.makeOutput([c,u],s.dtype),p=t.dataIdMap.get(d.dataId).id,h=t.makeOutput([u],a.dtype),f=t.dataIdMap.get(h.dataId).id,m=t.makeOutput([3],"int32"),g=t.dataIdMap.get(m.dataId).id;Q6(o,i,l,c,p,f,g);let y=t.readSync(m.dataId),x;switch(y[0]){case 0:{x=N.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(y[1],y[2]);break}case 1:{x=N.getSparseReshapeNegativeOutputDimErrorMessage(y[1],y[2]);break}case 2:x=N.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();break;case 3:{let A=Array.from(t.readSync(r.dataId)),b=Array.from(t.readSync(h.dataId));x=N.getSparseReshapeInputOutputMultipleErrorMessage(A,b);break}case 4:{let 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moduleLoaded(){}this.onmessage=function(e){try{if(e.data.cmd==="load"){Module["wasmModule"]=e.data.wasmModule;Module["wasmMemory"]=e.data.wasmMemory;Module["buffer"]=Module["wasmMemory"].buffer;Module["ENVIRONMENT_IS_PTHREAD"]=true;if(typeof e.data.urlOrBlob==="string"){importScripts(e.data.urlOrBlob)}else{var objectUrl=URL.createObjectURL(e.data.urlOrBlob);importScripts(objectUrl);URL.revokeObjectURL(objectUrl)}WasmBackendModuleThreadedSimd(Module).then(function(instance){Module=instance;moduleLoaded()})}else if(e.data.cmd==="objectTransfer"){Module["PThread"].receiveObjectTransfer(e.data)}else if(e.data.cmd==="run"){Module["__performance_now_clock_drift"]=performance.now()-e.data.time;Module["__emscripten_thread_init"](e.data.threadInfoStruct,0,0);var max=e.data.stackBase;var top=e.data.stackBase+e.data.stackSize;Module["establishStackSpace"](top,max);Module["_emscripten_tls_init"]();Module["PThread"].receiveObjectTransfer(e.data);Module["PThread"].setThreadStatus(Module["_pthread_self"](),1);try{var result=Module["invokeEntryPoint"](e.data.start_routine,e.data.arg);if(!Module["getNoExitRuntime"]())Module["PThread"].threadExit(result)}catch(ex){if(ex==="Canceled!"){Module["PThread"].threadCancel()}else if(ex!="unwind"){if(ex instanceof Module["ExitStatus"]){if(Module["getNoExitRuntime"]()){}else{Module["PThread"].threadExit(ex.status)}}else{Module["PThread"].threadExit(-2);throw ex}}}}else if(e.data.cmd==="cancel"){if(Module["_pthread_self"]()){Module["PThread"].threadCancel()}}else if(e.data.target==="setimmediate"){}else if(e.data.cmd==="processThreadQueue"){if(Module["_pthread_self"]()){Module["_emscripten_current_thread_process_queued_calls"]()}}else{err("worker.js received unknown command "+e.data.cmd);err(e.data)}}catch(ex){err("worker.js onmessage() captured an uncaught exception: "+ex);if(ex&&ex.stack)err(ex.stack);throw ex}};if(typeof process==="object"&&typeof process.versions==="object"&&typeof process.versions.node==="string"){self={location:{href:__filename}};var onmessage=this.onmessage;var nodeWorkerThreads=require("worker_threads");global.Worker=nodeWorkerThreads.Worker;var parentPort=nodeWorkerThreads.parentPort;parentPort.on("message",function(data){onmessage({data:data})});var nodeFS=require("fs");var nodeRead=function(filename){return nodeFS.readFileSync(filename,"utf8")};function globalEval(x){global.require=require;global.Module=Module;eval.call(null,x)}importScripts=function(f){globalEval(nodeRead(f))};postMessage=function(msg){parentPort.postMessage(msg)};if(typeof performance==="undefined"){performance={now:function(){return Date.now()}}}}',R2e=pi(s9()),c8=class extends su{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(h8),Hx=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new od(this,as())}write(e,t,n){let s={id:this.dataIdNextNumber++};return this.move(s,e,t,n,1),s}numDataIds(){return this.dataIdMap.numDataIds()}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}move(e,t,n,s,r){let a=this.dataIdNextNumber++;if(s==="string"){let c=t;this.dataIdMap.set(e,{id:a,stringBytes:c,shape:n,dtype:s,memoryOffset:null,refCount:r});return}let o=v.sizeFromShape(n),i=o*v.bytesPerElement(s),l=this.wasm._malloc(i);this.dataIdMap.set(e,{id:a,memoryOffset:l,shape:n,dtype:s,refCount:r}),this.wasm.tfjs.registerTensor(a,o,l),t!=null&&this.wasm.HEAPU8.set(new Uint8Array(t.buffer,t.byteOffset,i),l)}async read(e){return this.readSync(e)}readSync(e,t,n){let{memoryOffset:s,dtype:r,shape:a,stringBytes:o}=this.dataIdMap.get(e);if(r==="string")return(t==null||t===0)&&(n==null||n>=o.length)?o:o.slice(t,n);t=t||0,n=n||v.sizeFromShape(a);let i=v.bytesPerElement(r),l=this.wasm.HEAPU8.slice(s+t*i,s+n*i);return $2e(l.buffer,r)}disposeData(e,t=!1){if(this.dataIdMap.has(e)){let n=this.dataIdMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;this.wasm._free(n.memoryOffset),this.wasm.tfjs.disposeData(n.id),this.dataIdMap.delete(e)}return!0}refCount(e){return this.dataIdMap.has(e)?this.dataIdMap.get(e).refCount:0}incRef(e){let t=this.dataIdMap.get(e);t!=null&&t.refCount++}floatPrecision(){return 32}getMemoryOffset(e){return this.dataIdMap.get(e).memoryOffset}dispose(){this.wasm.tfjs.dispose(),"PThread"in this.wasm&&this.wasm.PThread.terminateAllThreads(),this.wasm=null}memory(){return{unreliable:!1}}makeOutput(e,t,n){let s;if(n==null)s=this.write(null,e,t);else{let r=this.dataIdNextNumber++;s={id:r},this.dataIdMap.set(s,{id:r,memoryOffset:n,shape:e,dtype:t,refCount:1});let a=v.sizeFromShape(e);this.wasm.tfjs.registerTensor(r,a,n)}return{dataId:s,shape:e,dtype:t}}typedArrayFromHeap({shape:e,dtype:t,dataId:n}){let s=this.wasm.HEAPU8.buffer,{memoryOffset:r}=this.dataIdMap.get(n),a=v.sizeFromShape(e);switch(t){case"float32":return new Float32Array(s,r,a);case"int32":return new Int32Array(s,r,a);case"bool":return new Uint8Array(s,r,a);default:throw new Error(`Unknown dtype ${t}`)}}};function _2e(e){return(t,n)=>(v.fetch(e,{credentials:"same-origin"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at '${e}'`),s.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(a=>{n(a.instance,a.module)})})}),{})}function d8(e,t,n){if(i0!=null)return i0;let s="tfjs-backend-wasm.wasm";return e&&t?s="tfjs-backend-wasm-threaded-simd.wasm":e&&(s="tfjs-backend-wasm-simd.wasm"),Op!=null&&Op[s]!=null?Op[s]:n+s}async function D2e(){let[e,t]=await Promise.all([Y().getAsync("WASM_HAS_SIMD_SUPPORT"),Y().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,s)=>{let r={};r.locateFile=(i,l)=>{if(i.endsWith(".worker.js")){let c=E2e,u=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(u)}return i.endsWith(".wasm")?d8(e,t,Pp!=null?Pp:l):l+i},Gx&&(r.instantiateWasm=_2e(d8(e,t,Pp!=null?Pp:"")));let a=!1;r.onAbort=()=>{if(a||Mp)return;Mp=!0,s({message:"Make sure the server can serve the `.wasm` file relative to the bundled js file. 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Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");i0=e,Gx=t}function p8(e,t=!1){if(Mp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof e=="string")Pp=e;else{Op=e;let n=F2e.filter(s=>Op[s]==null);if(n.length>0)throw new Error(`There were no entries found for the following binaries: ${n.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}Gx=t}var h8=-1,Hx=-1;function O2e(e){h8=e}function M2e(){if(Hx===-1)throw new Error("WASM backend not initialized.");return Hx}var z2e="0.0.0",L2e=2;ul("wasm",async()=>{let{wasm:e}=await D2e();return new c8(e)},L2e);var Jo="3.12.0-20211215",l0={tfjs:Jo,"tfjs-core":Jo,"tfjs-data":Jo,"tfjs-layers":Jo,"tfjs-converter":Jo,"tfjs-backend-cpu":Jo,"tfjs-backend-webgl":Jo,"tfjs-backend-wasm":Jo};var f8=`
|
|
precision highp float;
|
|
attribute vec2 pos;
|
|
attribute vec2 uv;
|
|
varying vec2 vUv;
|
|
uniform float flipY;
|
|
void main(void) {
|
|
vUv = uv;
|
|
gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
|
|
}
|
|
`;var m8=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform sampler2D texture;
|
|
uniform float m[20];
|
|
void main(void) {
|
|
vec4 c = texture2D(texture, vUv);
|
|
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[3] * c.a + m[4];
|
|
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[8] * c.a + m[9];
|
|
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[13] * c.a + m[14];
|
|
gl_FragColor.a = m[15] * c.r + m[16] * c.g + m[17] * c.b + m[18] * c.a + m[19];
|
|
}
|
|
`,g8=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform sampler2D texture;
|
|
uniform float m[20];
|
|
void main(void) {
|
|
vec4 c = texture2D(texture, vUv);
|
|
gl_FragColor.r = m[0] * c.r + m[1] * c.g + m[2] * c.b + m[4];
|
|
gl_FragColor.g = m[5] * c.r + m[6] * c.g + m[7] * c.b + m[9];
|
|
gl_FragColor.b = m[10] * c.r + m[11] * c.g + m[12] * c.b + m[14];
|
|
gl_FragColor.a = c.a;
|
|
}
|
|
`,y8=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform vec2 size;
|
|
uniform sampler2D texture;
|
|
vec2 pixelate(vec2 coord, vec2 size) {
|
|
return floor( coord / size ) * size;
|
|
}
|
|
void main(void) {
|
|
gl_FragColor = vec4(0.0);
|
|
vec2 coord = pixelate(vUv, size);
|
|
gl_FragColor += texture2D(texture, coord);
|
|
}
|
|
`,A8=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform sampler2D texture;
|
|
uniform vec2 px;
|
|
void main(void) {
|
|
gl_FragColor = vec4(0.0);
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-7.0*px.x, -7.0*px.y))*0.0044299121055113265;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-6.0*px.x, -6.0*px.y))*0.00895781211794;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-5.0*px.x, -5.0*px.y))*0.0215963866053;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-4.0*px.x, -4.0*px.y))*0.0443683338718;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-3.0*px.x, -3.0*px.y))*0.0776744219933;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-2.0*px.x, -2.0*px.y))*0.115876621105;
|
|
gl_FragColor += texture2D(texture, vUv + vec2(-1.0*px.x, -1.0*px.y))*0.147308056121;
|
|
gl_FragColor += texture2D(texture, vUv )*0.159576912161;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 1.0*px.x, 1.0*px.y))*0.147308056121;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 2.0*px.x, 2.0*px.y))*0.115876621105;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 3.0*px.x, 3.0*px.y))*0.0776744219933;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 4.0*px.x, 4.0*px.y))*0.0443683338718;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 5.0*px.x, 5.0*px.y))*0.0215963866053;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 6.0*px.x, 6.0*px.y))*0.00895781211794;
|
|
gl_FragColor += texture2D(texture, vUv + vec2( 7.0*px.x, 7.0*px.y))*0.0044299121055113265;
|
|
}
|
|
`,x8=`
|
|
precision highp float;
|
|
varying vec2 vUv;
|
|
uniform sampler2D texture;
|
|
uniform vec2 px;
|
|
uniform float m[9];
|
|
void main(void) {
|
|
vec4 c11 = texture2D(texture, vUv - px); // top left
|
|
vec4 c12 = texture2D(texture, vec2(vUv.x, vUv.y - px.y)); // top center
|
|
vec4 c13 = texture2D(texture, vec2(vUv.x + px.x, vUv.y - px.y)); // top right
|
|
vec4 c21 = texture2D(texture, vec2(vUv.x - px.x, vUv.y) ); // mid left
|
|
vec4 c22 = texture2D(texture, vUv); // mid center
|
|
vec4 c23 = texture2D(texture, vec2(vUv.x + px.x, vUv.y) ); // mid right
|
|
vec4 c31 = texture2D(texture, vec2(vUv.x - px.x, vUv.y + px.y) ); // bottom left
|
|
vec4 c32 = texture2D(texture, vec2(vUv.x, vUv.y + px.y) ); // bottom center
|
|
vec4 c33 = texture2D(texture, vUv + px ); // bottom right
|
|
gl_FragColor =
|
|
c11 * m[0] + c12 * m[1] + c22 * m[2] +
|
|
c21 * m[3] + c22 * m[4] + c23 * m[5] +
|
|
c31 * m[6] + c32 * m[7] + c33 * m[8];
|
|
gl_FragColor.a = c22.a;
|
|
}
|
|
`;var jx=(e,t,n)=>{let s=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(s,(r,a)=>(n[a]=0,r))},b8=class{constructor(t,n,s){fe(this,"uniform",{});fe(this,"attribute",{});fe(this,"gl");fe(this,"id");fe(this,"compile",(t,n)=>{let s=this.gl.createShader(n);return s?(this.gl.shaderSource(s,t),this.gl.compileShader(s),this.gl.getShaderParameter(s,this.gl.COMPILE_STATUS)?s:(J(`filter: gl compile failed: ${this.gl.getShaderInfoLog(s)}`),null)):(J("filter: could not create shader"),null)});this.gl=t;let r=this.compile(n,this.gl.VERTEX_SHADER),a=this.compile(s,this.gl.FRAGMENT_SHADER);if(this.id=this.gl.createProgram(),!(!r||!a)){if(!this.id){J("filter: could not create webgl program");return}if(this.gl.attachShader(this.id,r),this.gl.attachShader(this.id,a),this.gl.linkProgram(this.id),!this.gl.getProgramParameter(this.id,this.gl.LINK_STATUS)){J(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);return}this.gl.useProgram(this.id),jx(n,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=this.gl.getAttribLocation(this.id,o);jx(n,"uniform",this.uniform),jx(s,"uniform",this.uniform);for(let o in this.uniform)this.uniform[o]=this.gl.getUniformLocation(this.id,o)}}};function v8(){let e=0,t=null,n=!1,s=-1,r=[null,null],a=[],o=null,i=null,l=Kn(100,100),c={},u={INTERMEDIATE:1},d=l.getContext("webgl");if(this.gl=d,!d){J("filter: cannot get webgl context");return}function p(x,A){if(!(x===l.width&&A===l.height)){if(l.width=x,l.height=A,!o){let b=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);o=d.createBuffer(),d.bindBuffer(d.ARRAY_BUFFER,o),d.bufferData(d.ARRAY_BUFFER,b,d.STATIC_DRAW),d.pixelStorei(d.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}d.viewport(0,0,l.width,l.height),r=[null,null]}}function h(x,A){let b=d.createFramebuffer();d.bindFramebuffer(d.FRAMEBUFFER,b);let w=d.createRenderbuffer();d.bindRenderbuffer(d.RENDERBUFFER,w);let C=d.createTexture();return d.bindTexture(d.TEXTURE_2D,C),d.texImage2D(d.TEXTURE_2D,0,d.RGBA,x,A,0,d.RGBA,d.UNSIGNED_BYTE,null),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MAG_FILTER,d.LINEAR),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MIN_FILTER,d.LINEAR),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_WRAP_S,d.CLAMP_TO_EDGE),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_WRAP_T,d.CLAMP_TO_EDGE),d.framebufferTexture2D(d.FRAMEBUFFER,d.COLOR_ATTACHMENT0,d.TEXTURE_2D,C,0),d.bindTexture(d.TEXTURE_2D,null),d.bindFramebuffer(d.FRAMEBUFFER,null),{fbo:b,texture:C}}function f(x){return r[x]=r[x]||h(l.width,l.height),r[x]}function m(x=0){if(!i)return;let A=null,b=null,w=!1;e===0?A=t:A=f(s).texture||null,e++,n&&!(x&u.INTERMEDIATE)?(b=null,w=e%2==0):(s=(s+1)%2,b=f(s).fbo||null),d.bindTexture(d.TEXTURE_2D,A),d.bindFramebuffer(d.FRAMEBUFFER,b),d.uniform1f(i.uniform.flipY,w?-1:1),d.drawArrays(d.TRIANGLES,0,6)}function g(x){if(c[x])return i=c[x],d.useProgram((i?i.id:null)||null),i;if(i=new b8(d,f8,x),!i)return J("filter: could not get webgl 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n;if(!pa.inputTensor)pa.inputTensor=Bn(t);else if(pa.inputTensor.shape[1]!==t.shape[1]||pa.inputTensor.shape[2]!==t.shape[2])ne(pa.inputTensor),pa.inputTensor=Bn(t);else{let s={};s.diff=he(t,pa.inputTensor),s.squared=L(s.diff,s.diff),s.sum=we(s.squared);let a=(await s.sum.data())[0]/(t.shape[1]||1)/(t.shape[2]||1)/255/3;ne([pa.inputTensor,s.diff,s.squared,s.sum]),pa.inputTensor=Bn(t),n=a<=(e.cacheSensitivity||0)}return n}async function k8(e,t,n){let s={};if(!t||!n||t.shape.length!==4||t.shape.length!==n.shape.length)return e.debug||J("invalid input tensor or tensor shapes do not match:",t.shape,n.shape),0;if(t.shape[0]!==1||n.shape[0]!==1||t.shape[3]!==3||n.shape[3]!==3)return e.debug||J("input tensors must be of shape [1, height, width, 3]:",t.shape,n.shape),0;s.input1=Bn(t),s.input2=t.shape[1]!==n.shape[1]||t.shape[2]!==n.shape[2]?Se.resizeBilinear(n,[t.shape[1],t.shape[2]]):Bn(n),s.diff=he(s.input1,s.input2),s.squared=L(s.diff,s.diff),s.sum=we(s.squared);let a=(await 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OffscreenCanvas!="undefined",this.initial=!0,this.worker=this.browser&&this.offscreen?typeof WorkerGlobalScope!="undefined":void 0,typeof navigator!="undefined"){let t=navigator.userAgent.match(/\(([^()]+)\)/g);if(t&&t[0]){let n=t[0].match(/\(([^()]+)\)/g);this.platform=n&&n[0]?n[0].replace(/\(|\)/g,""):"",this.agent=navigator.userAgent.replace(t[0],""),this.platform[1]&&(this.agent=this.agent.replace(t[1],"")),this.agent=this.agent.replace(/ /g," ")}}else typeof process!="undefined"&&(this.platform=`${process.platform} ${process.arch}`,this.agent=`NodeJS ${process.version}`)}async updateBackend(){this.backends=Object.keys(as().registryFactory),this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&$s()==="wasm"&&(this.wasm.simd=await Y().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await Y().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Kn(100,100),n=t?t.getContext("webgl2"):void 0;if(this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&($s()==="webgl"||$s()==="humangl")){let s=Rr().gpgpu!=="undefined"?await Rr().getGPGPUContext().gl:null;s&&(this.webgl.version=s.getParameter(s.VERSION),this.webgl.renderer=s.getParameter(s.RENDERER))}this.webgpu.supported=this.browser&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{this.webgpu.supported&&(this.webgpu.adapter=(await navigator.gpu.requestAdapter()).name)}catch(s){this.webgpu.supported=!1}try{this.kernels=Jr($s()).map(s=>s.kernelName.toLowerCase())}catch(s){}}async updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}},pe=new S8;var Xx="2.5.7";var hs,Kx=[],W2e=["white","black","asian","indian","other"],V2e=[15,23,28,35.5,45.5,55.5,65],I8=0,C8=0,Zx=Number.MAX_SAFE_INTEGER;async function 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ne(o),i},m0=(e,t)=>{let n=f0(e),s=Ec(e),r=[t*s[0]/2,t*s[1]/2];return{startPoint:[n[0]-r[0],n[1]-r[1]],endPoint:[n[0]+r[0],n[1]+r[1]],landmarks:e.landmarks,confidence:e.confidence}},g0=e=>{let t=f0(e),n=Ec(e),s=Math.max(...n)/2;return{startPoint:[Math.round(t[0]-s),Math.round(t[1]-s)],endPoint:[Math.round(t[0]+s),Math.round(t[1]+s)],landmarks:e.landmarks,confidence:e.confidence}},L8=e=>{let t=e.map(s=>s[0]),n=e.map(s=>s[1]);return{startPoint:[Math.min(...t),Math.min(...n)],endPoint:[Math.max(...t),Math.max(...n)],landmarks:e}},cb=[[1,0,0],[0,1,0],[0,0,1]],j2e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),q2e=(e,t)=>j2e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var B8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Ll=(e,t)=>{let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n},X2e=(e,t)=>{let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n},W8=(e,t)=>{let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(Ll(e[r],X2e(t,a)))}return n},V8=(e,t)=>{let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=B8(t[0],t[1]),o=W8(a,r),i=B8(-t[0],-t[1]);return W8(o,i)},K2e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Ll(t[0],n),-Ll(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]},Z2e=(e,t)=>[Ll(e,t[0]),Ll(e,t[1])];function U8(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let s=0;s<t.strides.length;s++){let r=t.strides[s],a=Math.floor((e+r-1)/r),o=Math.floor((e+r-1)/r),i=t.anchors[s];for(let l=0;l<a;l++){let c=r*(l+.5);for(let u=0;u<o;u++){let d=r*(u+.5);for(let p=0;p<i;p++)n.push([d,c])}}}return n}function G8(e,t,n,s,r){let a=Ec(t),o=e.map(h=>[a[0]/r*(h[0]-r/2),a[1]/r*(h[1]-r/2),h[2]||0]),i=n&&n!==0&&Math.abs(n)>.2,l=i?V8(n,[0,0]):cb,c=i?o.map(h=>[...Z2e(h,l),h[2]]):o,u=i?K2e(s):cb,d=f0(t),p=[Ll(d,u[0]),Ll(d,u[1])];return c.map(h=>[Math.trunc(h[0]+p[0]),Math.trunc(h[1]+p[1]),Math.trunc(h[2]||0)])}function H8(e,t,n,s){let r=t.landmarks.length>=ab.count?ab.symmetryLine:zp.symmetryLine,a=0,o=cb,i;if(e&&pe.kernels.includes("rotatewithoffset"))if(a=q2e(t.landmarks[r[0]],t.landmarks[r[1]]),a&&a!==0&&Math.abs(a)>.2){let c=f0(t),u=[c[0]/n.shape[2],c[1]/n.shape[1]],d=Se.rotateWithOffset(n,a,0,u);o=V8(-a,c),i=ub(t,d,[s,s]),ne(d)}else i=ub(t,n,[s,s]);else i=ub(t,n,[s,s]);return[a,o,i]}var Y2e=e=>{let t=e.map(s=>s[0]),n=e.map(s=>s[1]);return[Math.min(...t)+(Math.max(...t)-Math.min(...t))/2,Math.min(...n)+(Math.max(...n)-Math.min(...n))/2]},j8=(e,t)=>{let n=Y2e(e),s=Ec(t);return{startPoint:[n[0]-s[0]/2,n[1]-s[1]/2],endPoint:[n[0]+s[0]/2,n[1]+s[1]/2]}};var q8=6,J2e=1.2,Us,X8=null,Qo=0,Bp=null,y0=()=>Qo;async function K8(e){var t,n;return pe.initial&&(Us=null),Us?e.debug&&J("cached model:",Us.modelUrl):(Us=await Be(We(e.modelBasePath,((t=e.face.detector)==null?void 0:t.modelPath)||"")),!Us||!Us.modelUrl?J("load model failed:",(n=e.face.detector)==null?void 0:n.modelPath):e.debug&&J("load model:",Us.modelUrl)),Qo=Us.inputs[0].shape?Us.inputs[0].shape[2]:0,Bp=Ce(Qo,"int32"),X8=fr(U8(Qo)),Us}function Q2e(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=ue(t.boxStarts,X8),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=de(t.boxSizes,Bp),t.centersNormalized=de(t.centers,Bp),t.halfBoxSize=de(t.boxSizesNormalized,Xe.tf2),t.starts=he(t.centersNormalized,t.halfBoxSize),t.ends=ue(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,Bp),t.endNormalized=L(t.ends,Bp);let n=Vu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(s=>ne(t[s])),n}async function Z8(e,t){var i,l,c,u;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return[];let n={};n.resized=Se.resizeBilinear(e,[Qo,Qo]),n.div=de(n.resized,Xe.tf127),n.normalized=he(n.div,Xe.tf05);let s=Us==null?void 0:Us.execute(n.normalized);if(Array.isArray(s)){let d=s.sort((p,h)=>p.size-h.size);n.concat384=St([d[0],d[2]],2),n.concat512=St([d[1],d[3]],2),n.concat=St([n.concat512,n.concat384],1),n.batch=rt(n.concat,0)}else n.batch=rt(s);ne(s),n.boxes=Q2e(n.batch),n.logits=Pe(n.batch,[0,0],[-1,1]),n.sigmoid=_n(n.logits),n.scores=rt(n.sigmoid),n.nms=await Se.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),a=[],o=await n.scores.data();for(let d=0;d<r.length;d++){let p=o[r[d]];if(p>(((u=t.face.detector)==null?void 0:u.minConfidence)||0)){let h={};h.bbox=Pe(n.boxes,[r[d],0],[1,-1]),h.slice=Pe(n.batch,[r[d],q8-1],[1,-1]),h.squeeze=rt(h.slice),h.landmarks=H(h.squeeze,[q8,-1]);let f=await h.bbox.data(),m={startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await h.landmarks.array(),confidence:p},g=z8(m,[(e.shape[2]||0)/Qo,(e.shape[1]||0)/Qo]),y=m0(g,J2e),x=g0(y);a.push(x),Object.keys(h).forEach(A=>ne(h[A]))}}return Object.keys(n).forEach(d=>ne(n[d])),a}var hb={};td(hb,{connected:()=>pb,kpt:()=>db});var db=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPinky","rightPinky","leftIndex","rightIndex","leftThumb","rightThumb","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftPalm","leftHand","rightPalm","rightHand"],pb={leftLeg:["leftHip","leftKnee","leftAnkle","leftHeel","leftFoot"],rightLeg:["rightHip","rightKnee","rightAnkle","rightHeel","rightFoot"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder","rightShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist","leftPalm"],rightArm:["rightShoulder","rightElbow","rightWrist","rightPalm"],leftEye:["leftEyeInside","leftEye","leftEyeOutside"],rightEye:["rightEyeInside","rightEye","rightEyeOutside"],mouth:["leftMouth","rightMouth"]};var Y8=224,eye,tye=5,A0=[8,16,32,32,32];async function J8(){let e=[],t=0;for(;t<tye;){let n=0,s=t;for(;s<A0.length&&A0[s]===A0[t];)n+=2,s++;let r=A0[t],a=Math.ceil(Y8/r),o=Math.ceil(Y8/r);for(let i=0;i<a;++i)for(let l=0;l<o;++l)for(let c=0;c<n;++c)e.push({x:(l+.5)/o,y:(i+.5)/a});t=s}eye={x:Ct(e.map(n=>n.x)),y:Ct(e.map(n=>n.y))}}function ha(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[s[0],s[1],r[0]-s[0],r[1]-s[1]],o=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:o}}function Q8(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[(s[0]+r[0])/2,(s[1]+r[1])/2],o=Math.max(a[0]-s[0],a[1]-s[1],-a[0]+r[0],-a[1]+r[1]),i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function x0(e,t){let n=[e[2]*t,e[3]*t];return[e[0]-(n[0]-e[2])/2,e[1]-(n[1]-e[3])/2,n[0],n[1]]}function fb(e){return[Math.max(0,e[1]),Math.max(0,e[0]),Math.min(1,e[3]+e[1]),Math.min(1,e[2]+e[0])]}var eT={initial:!0},pn={detector:null,landmarks:null},Rc={detector:[224,224],landmarks:[256,256]},mb=Number.MAX_SAFE_INTEGER,nye={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},b0=null,Wp,ei=[[0,0],[0,0],[0,0],[0,0]],tT=0,nT=e=>1-1/(1+Math.exp(e));async function sT(e){if(eT.initial&&(pn.detector=null),!pn.detector&&e.body.detector&&e.body.detector.modelPath){pn.detector=await Be(We(e.modelBasePath,e.body.detector.modelPath||""));let t=Object.values(pn.detector.modelSignature.inputs);Rc.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Rc.detector[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!pn.detector||!pn.detector.modelUrl?J("load model failed:",e.body.detector.modelPath):e.debug&&J("load model:",pn.detector.modelUrl)}else e.debug&&pn.detector&&J("cached model:",pn.detector.modelUrl);return await J8(),pn.detector}async function rT(e){if(eT.initial&&(pn.landmarks=null),pn.landmarks)e.debug&&J("cached model:",pn.landmarks.modelUrl);else{pn.landmarks=await Be(We(e.modelBasePath,e.body.modelPath||""));let t=Object.values(pn.landmarks.modelSignature.inputs);Rc.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Rc.landmarks[1]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!pn.landmarks||!pn.landmarks.modelUrl?J("load model failed:",e.body.modelPath):e.debug&&J("load model:",pn.landmarks.modelUrl)}return pn.landmarks}async function sye(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let s;if(Wp&&(n.cropped=Se.cropAndResize(e,[Wp],[0],[e.shape[1],e.shape[2]])),e.shape[1]!==e.shape[2]){let r=[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],a=[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0];ei=[[0,0],r,a,[0,0]],n.pad=Qs(n.cropped||e,ei),n.resize=Se.resizeBilinear(n.pad,[t,t]),s=de(n.resize,Xe.tf255)}else e.shape[1]!==t?(n.resize=Se.resizeBilinear(n.cropped||e,[t,t]),s=de(n.resize,Xe.tf255)):s=de(n.cropped||e,Xe.tf255);return Object.keys(n).forEach(r=>ne(n[r])),s}function rye(e,t){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+ei[2][0]+ei[2][1])/t[0]-ei[2][0]),Math.trunc(n.position[1]*(t[1]+ei[1][0]+ei[1][1])/t[1]-ei[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],n.position[2]];if(Wp)for(let n of e)n.positionRaw=[n.positionRaw[0]+Wp[1],n.positionRaw[1]+Wp[0],n.positionRaw[2]],n.position=[Math.trunc(n.positionRaw[0]*t[0]),Math.trunc(n.positionRaw[1]*t[1]),n.positionRaw[2]];return e}async function aye(e,t,n){var h;let s={};[s.ld,s.segmentation,s.heatmap,s.world,s.poseflag]=(h=pn.landmarks)==null?void 0:h.execute(e,nye.landmarks);let r=(await s.poseflag.data())[0],a=await s.ld.data();Object.keys(s).forEach(f=>ne(s[f]));let o=[],i=5;for(let f=0;f<a.length/i;f++){let m=nT(a[i*f+3]),g=nT(a[i*f+4]),y=Math.trunc(100*m*g*r)/100,x=[a[i*f+0]/Rc.landmarks[0],a[i*f+1]/Rc.landmarks[1],a[i*f+2]+0],A=[Math.trunc(n[0]*x[0]),Math.trunc(n[1]*x[1]),x[2]];o.push({part:db[f],positionRaw:x,position:A,score:y})}if(r<(t.body.minConfidence||0))return null;let l=rye(o,n),c=l.map(f=>f.position),u=ha(c,[n[0],n[1]]),d={};for(let[f,m]of Object.entries(pb)){let g=[];for(let y=0;y<m.length-1;y++){let x=l.find(b=>b.part===m[y]),A=l.find(b=>b.part===m[y+1]);x&&A&&g.push([x.position,A.position])}d[f]=g}return{id:0,score:Math.trunc(100*r)/100,box:u.box,boxRaw:u.boxRaw,keypoints:l,annotations:d}}async function gb(e,t){let n=[e.shape[2]||0,e.shape[1]||0],s=(t.body.skipTime||0)>ie()-tT,r=mb<(t.body.skipFrames||0);if(t.skipAllowed&&s&&r&&b0!==null)mb++;else{let a={};a.landmarks=await sye(e,256),b0=await aye(a.landmarks,t,n),Object.keys(a).forEach(o=>ne(a[o])),tT=ie(),mb=0}return b0?[b0]:[]}var _c=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking 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drier"},{class:80,label:"toothbrush"}];var ir,Bl=0,yb=[],aT=0,Ab=Number.MAX_SAFE_INTEGER;async function oT(e){if(pe.initial&&(ir=null),ir)e.debug&&J("cached model:",ir.modelUrl);else{ir=await Be(We(e.modelBasePath,e.object.modelPath||""));let t=Object.values(ir.modelSignature.inputs);Bl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!ir||!ir.modelUrl?J("load model failed:",e.object.modelPath):e.debug&&J("load model:",ir.modelUrl)}return ir}async function oye(e,t,n){if(!e)return[];let s={},r=[],a=await e.array();s.squeeze=rt(e);let o=Yt(s.squeeze,6,1);s.stack=an([o[1],o[0],o[3],o[2]],1),s.boxes=rt(s.stack),s.scores=rt(o[4]),s.classes=rt(o[5]),ne([e,...o]),s.nms=await Se.nonMaxSuppressionAsync(s.boxes,s.scores,n.object.maxDetected,n.object.iouThreshold,n.object.minConfidence);let i=await s.nms.data(),l=0;for(let c of Array.from(i)){let u=Math.trunc(100*a[0][c][4])/100,d=a[0][c][5],p=_c[d].label,[h,f]=[a[0][c][0]/Bl,a[0][c][1]/Bl],m=[h,f,a[0][c][2]/Bl-h,a[0][c][3]/Bl-f],g=[Math.trunc(m[0]*t[0]),Math.trunc(m[1]*t[1]),Math.trunc(m[2]*t[0]),Math.trunc(m[3]*t[1])];r.push({id:l++,score:u,class:d,label:p,box:g,boxRaw:m})}return Object.keys(s).forEach(c=>ne(s[c])),r}async function xb(e,t){let n=(t.object.skipTime||0)>ie()-aT,s=Ab<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&yb.length>0?(Ab++,yb):(Ab=0,new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=Se.resizeBilinear(e,[Bl,Bl]),i=t.object.enabled?ir==null?void 0:ir.execute(o,["tower_0/detections"]):null;aT=ie(),ne(o);let l=await oye(i,a,t);yb=l,r(l)}))}var wb={};td(wb,{connected:()=>vb,kpt:()=>bb});var bb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],vb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var hn,iT=0,Zn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},kb=Number.MAX_SAFE_INTEGER;async function lT(e){return pe.initial&&(hn=null),hn?e.debug&&J("cached model:",hn.modelUrl):(hn=await Be(We(e.modelBasePath,e.body.modelPath||"")),!hn||!hn.modelUrl?J("load model failed:",e.body.modelPath):e.debug&&J("load model:",hn.modelUrl)),hn}async function iye(e,t){let[n,s]=e.shape,r=H(e,[s*n]),a=An(r,0),o=(await a.data())[0];if(ne([r,a]),o>t){let i=Fs(r,0),l=Hd(i,n),c=(await 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0:d.minConfidence)||0)&&Zn.keypoints.push({score:Math.round(100*y)/100,part:bb[f],positionRaw:[m/hn.inputs[0].shape[2],g/hn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/hn.inputs[0].shape[2]),Math.round(e.shape[1]*g/hn.inputs[0].shape[1])]})}h.forEach(f=>ne(f))}Zn.score=Zn.keypoints.reduce((p,h)=>h.score>p?h.score:p,0);let i=Zn.keypoints.map(p=>p.position[0]),l=Zn.keypoints.map(p=>p.position[1]);Zn.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let c=Zn.keypoints.map(p=>p.positionRaw[0]),u=Zn.keypoints.map(p=>p.positionRaw[1]);Zn.boxRaw=[Math.min(...c),Math.min(...u),Math.max(...c)-Math.min(...c),Math.max(...u)-Math.min(...u)];for(let[p,h]of Object.entries(vb)){let f=[];for(let m=0;m<h.length-1;m++){let g=Zn.keypoints.find(x=>x.part===h[m]),y=Zn.keypoints.find(x=>x.part===h[m+1]);g&&y&&g.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&f.push([g.position,y.position])}Zn.annotations[p]=f}r([Zn])}))}var lye=["angry","disgust","fear","happy","sad","surprise","neutral"],Yn,v0=[],uT=0,cT=0,Ib=Number.MAX_SAFE_INTEGER;async function dT(e){var t,n;return pe.initial&&(Yn=null),Yn?e.debug&&J("cached model:",Yn.modelUrl):(Yn=await Be(We(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!Yn||!Yn.modelUrl?J("load model failed:",(n=e.face.emotion)==null?void 0:n.modelPath):e.debug&&J("load model:",Yn.modelUrl)),Yn}async function Cb(e,t,n,s){var o,i;if(!Yn)return[];let r=Ib<(((o=t.face.emotion)==null?void 0:o.skipFrames)||0),a=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>ie()-cT;return t.skipAllowed&&a&&r&&uT===s&&v0[n]&&v0[n].length>0?(Ib++,v0[n]):(Ib=0,new Promise(async l=>{var u,d;let c=[];if((u=t.face.emotion)==null?void 0:u.enabled){let p={},h=(Yn==null?void 0:Yn.inputs[0].shape)?Yn.inputs[0].shape[2]:0;p.resize=Se.resizeBilinear(e,[h,h],!1),p.channels=L(p.resize,Xe.rgb),p.grayscale=we(p.channels,3,!0),p.grayscaleSub=he(p.grayscale,Xe.tf05),p.grayscaleMul=L(p.grayscaleSub,Xe.tf2),p.emotion=Yn==null?void 0:Yn.execute(p.grayscaleMul),cT=ie();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((d=t.face.emotion)==null?void 0:d.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:lye[m]});c.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>ne(p[m]))}v0[n]=c,uT=s,l(c)}))}var Is,Tb=[],pT=0,hT=0,fT=Number.MAX_SAFE_INTEGER;async function mT(e){let t=We(e.modelBasePath,e.face.mobilefacenet.modelPath);return pe.initial&&(Is=null),Is?e.debug&&J("cached model:",t):(Is=await Be(t),Is?e.debug&&J("load model:",t):J("load model failed:",e.face.mobilefacenet.modelPath)),Is}async function Nb(e,t,n,s){var o,i;if(!Is)return[];let r=fT<(((o=t.face.embedding)==null?void 0:o.skipFrames)||0),a=(((i=t.face.embedding)==null?void 0:i.skipTime)||0)>ie()-hT;return t.skipAllowed&&a&&r&&pT===s&&Tb[n]?(fT++,Tb[n]):new Promise(async l=>{var u;let c=[];if(((u=t.face.embedding)==null?void 0:u.enabled)&&(Is==null?void 0:Is.inputs[0].shape)){let d={};d.crop=Se.resizeBilinear(e,[Is.inputs[0].shape[2],Is.inputs[0].shape[1]],!1),d.data=Is==null?void 0:Is.execute(d.crop);let p=await d.data.data();c=Array.from(p)}Tb[n]=c,pT=s,hT=ie(),l(c)})}var lr,ti=0,uye=2.3,Eb=or.leftEyeLower0,Rb=or.rightEyeLower0,Dc={leftBounds:[Eb[0],Eb[Eb.length-1]],rightBounds:[Rb[0],Rb[Rb.length-1]]},$c={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function gT(e){var t,n;return pe.initial&&(lr=null),lr?e.debug&&J("cached model:",lr.modelUrl):(lr=await Be(We(e.modelBasePath,((t=e.face.iris)==null?void 0:t.modelPath)||"")),!lr||!lr.modelUrl?J("load model failed:",(n=e.face.iris)==null?void 0:n.modelPath):e.debug&&J("load model:",lr.modelUrl)),ti=lr.inputs[0].shape?lr.inputs[0].shape[2]:0,ti===-1&&(ti=64),lr}function w0(e,t,n,s){for(let r=0;r<ob.length;r++){let{key:a,indices:o}=ob[r],i=or[`${n}${a}`];if(!s||s.includes(a))for(let l=0;l<o.length;l++){let c=o[l];e[i[l]]=[t[c][0],t[c][1],(t[c][2]+e[i[l]][2])/2]}}}var cye=e=>{let t=e[Dc.leftBounds[0]][2],n=e[Dc.rightBounds[0]][2];return t-n},yT=(e,t,n,s,r,a=!1)=>{let o=g0(m0(L8([e[n],e[s]]),uye)),i=Ec(o),l=Se.cropAndResize(t,[[o.startPoint[1]/r,o.startPoint[0]/r,o.endPoint[1]/r,o.endPoint[0]/r]],[0],[ti,ti]);if(a&&pe.kernels.includes("flipleftright")){let c=Se.flipLeftRight(l);ne(l),l=c}return{box:o,boxSize:i,crop:l}},AT=(e,t,n,s=!1)=>{let r=[];for(let a=0;a<$c.numCoordinates;a++){let o=e[a*3],i=e[a*3+1],l=e[a*3+2];r.push([(s?1-o/ti:o/ti)*n[0]+t.startPoint[0],i/ti*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice($c.index)}},xT=(e,t,n)=>{let s=e[or[`${n}EyeUpper0`][$c.upperCenter]][2],r=e[or[`${n}EyeLower0`][$c.lowerCenter]][2],a=(s+r)/2;return t.map((o,i)=>{let l=a;return i===2?l=s:i===4&&(l=r),[o[0],o[1],l]})};async function bT(e,t,n,s){if(!lr)return n.debug&&J("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:a,crop:o}=yT(e,t,Dc.leftBounds[0],Dc.leftBounds[1],s,!0),{box:i,boxSize:l,crop:c}=yT(e,t,Dc.rightBounds[0],Dc.rightBounds[1],s,!0),u=St([o,c]);ne(o),ne(c);let d=lr.execute(u);ne(u);let p=await d.data();ne(d);let h=p.slice(0,$c.numCoordinates*3),{rawCoords:f,iris:m}=AT(h,r,a,!0),g=p.slice($c.numCoordinates*3),{rawCoords:y,iris:x}=AT(g,i,l),A=cye(e);Math.abs(A)<30?(w0(e,f,"left",null),w0(e,y,"right",null)):A<1?w0(e,f,"left",["EyeUpper0","EyeLower0"]):w0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=xT(e,m,"left"),w=xT(e,x,"right");return e.concat(b).concat(w)}var Gr={boxes:[],skipped:Number.MAX_SAFE_INTEGER,timestamp:0},ur=null,Fc=0;async function vT(e,t){var i,l,c,u,d,p,h,f;let n=(((i=t.face.detector)==null?void 0:i.skipTime)||0)>ie()-Gr.timestamp,s=Gr.skipped<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);!t.skipAllowed||!n||!s||Gr.boxes.length===0?(Gr.boxes=await Z8(e,t),Gr.timestamp=ie(),Gr.skipped=0):Gr.skipped++;let r=[],a=[],o=0;for(let m=0;m<Gr.boxes.length;m++){let g=Gr.boxes[m],y=0,x,A={id:o++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([y,x,A.tensor]=H8((c=t.face.detector)==null?void 0:c.rotation,g,e,((u=t.face.mesh)==null?void 0:u.enabled)?Fc:y0()),(d=t==null?void 0:t.filter)==null?void 0:d.equalization){let b=await u0(A.tensor);ne(A.tensor),A.tensor=b}if(A.boxScore=Math.round(100*g.confidence)/100,(p=t.face.mesh)==null?void 0:p.enabled)if(!ur)t.debug&&J("face mesh detection requested, but model is not loaded");else{let[b,w,C]=ur.execute(A.tensor),S=await w.data();A.faceScore=Math.round(100*S[0])/100;let E=H(C,[-1,3]),R=await E.array();if(ne([C,E,w,b]),A.faceScore<(((h=t.face.detector)==null?void 0:h.minConfidence)||1))g.confidence=A.faceScore;else{((f=t.face.iris)==null?void 0:f.enabled)&&(R=await bT(R,A.tensor,t,Fc)),A.mesh=G8(R,g,y,x,Fc),A.meshRaw=A.mesh.map(_=>[_[0]/(e.shape[2]||0),_[1]/(e.shape[1]||0),(_[2]||0)/Fc]);for(let _ of Object.keys(or))A.annotations[_]=or[_].map($=>A.mesh[$]);A.score=A.faceScore;let P={...j8(A.mesh,g),confidence:g.confidence,landmarks:g.landmarks};A.box=ib(P,e),A.boxRaw=lb(P,e),a.push(P)}}else{A.box=ib(g,e),A.boxRaw=lb(g,e),A.score=A.boxScore,A.mesh=g.landmarks.map(b=>[(g.startPoint[0]+g.endPoint[0])/2+(g.endPoint[0]+g.startPoint[0])*b[0]/y0(),(g.startPoint[1]+g.endPoint[1])/2+(g.endPoint[1]+g.startPoint[1])*b[1]/y0()]),A.meshRaw=A.mesh.map(b=>[b[0]/(e.shape[2]||0),b[1]/(e.shape[1]||0),(b[2]||0)/Fc]);for(let b of Object.keys(zp))A.annotations[b]=[A.mesh[zp[b]]]}r.push(A)}return Gr.boxes=a,r}async function wT(e){var t,n;return pe.initial&&(ur=null),ur?e.debug&&J("cached model:",ur.modelUrl):(ur=await Be(We(e.modelBasePath,((t=e.face.mesh)==null?void 0:t.modelPath)||"")),!ur||!ur.modelUrl?J("load model failed:",(n=e.face.mesh)==null?void 0:n.modelPath):e.debug&&J("load model:",ur.modelUrl)),Fc=ur.inputs[0].shape?ur.inputs[0].shape[2]:0,ur}var kT=zl,ST=Lp;var Cs,k0=[],IT=0,CT=0,_b=Number.MAX_SAFE_INTEGER;async function TT(e){var n,s;let t=We(e.modelBasePath,((n=e.face.description)==null?void 0:n.modelPath)||"");return pe.initial&&(Cs=null),Cs?e.debug&&J("cached model:",t):(Cs=await Be(t),Cs?e.debug&&J("load model:",t):J("load model failed:",((s=e.face.description)==null?void 0:s.modelPath)||"")),Cs}function Db(e){let t=e.image||e.tensor||e;if(!(Cs==null?void 0:Cs.inputs[0].shape))return t;let n=Se.resizeBilinear(t,[Cs.inputs[0].shape[2],Cs.inputs[0].shape[1]],!1),s=L(n,Xe.tf255);return ne(n),s}async function $b(e,t,n,s){var o,i,l,c;if(!Cs)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let r=_b<(((o=t.face.description)==null?void 0:o.skipFrames)||0),a=(((i=t.face.description)==null?void 0:i.skipTime)||0)>ie()-IT;return t.skipAllowed&&r&&a&&CT===s&&((l=k0[n])==null?void 0:l.age)&&((c=k0[n])==null?void 0:c.age)>0?(_b++,k0[n]):(_b=0,new Promise(async u=>{var p,h;let d={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)==null?void 0:p.enabled){let f=Db(e),m=Cs==null?void 0:Cs.execute(f);IT=ie(),ne(f);let y=await(await m.find(R=>R.shape[1]===1)).data(),x=Math.trunc(200*Math.abs(y[0]-.5))/100;x>(((h=t.face.description)==null?void 0:h.minConfidence)||0)&&(d.gender=y[0]<=.5?"female":"male",d.genderScore=Math.min(.99,x));let A=Fs(m.find(R=>R.shape[1]===100),1),b=(await A.data())[0];ne(A);let C=await m.find(R=>R.shape[1]===100).data();d.age=Math.round(C[b-1]>C[b+1]?10*b-100*C[b-1]:10*b+100*C[b+1])/10;let S=m.find(R=>R.shape[1]===1024),E=S?await S.data():[];d.descriptor=Array.from(E),m.forEach(R=>ne(R))}k0[n]=d,CT=s,u(d)}))}function S0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Vp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function NT(e,t,n){let s=t.shape[1],r=t.shape[2],a=[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]];return Se.cropAndResize(t,a,[0],n)}function ET(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(a=>[a[0]*t[0],a[1]*t[1]]);return{startPoint:n,endPoint:s,palmLandmarks:r,confidence:e.confidence}}function I0(e,t=1.5){let n=Vp(e),s=S0(e),r=[t*s[0]/2,t*s[1]/2],a=[n[0]-r[0],n[1]-r[1]],o=[n[0]+r[0],n[1]+r[1]];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function C0(e){let t=Vp(e),n=S0(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],o=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function dye(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function RT(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return dye(n)}var _T=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ni(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function pye(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function DT(e,t){let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(ni(e[r],pye(t,a)))}return n}function Fb(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=_T(t[0],t[1]),o=DT(a,r),i=_T(-t[0],-t[1]);return DT(o,i)}function $T(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-ni(t[0],n),-ni(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function Pb(e,t){return[ni(e,t[0]),ni(e,t[1])]}var 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s={};s.reshape=H(t,[-1,7,2]),s.div=de(s.reshape,this.inputSizeTensor),s.landmarks=ue(s.div,this.anchors[n]);let r=L(s.landmarks,this.inputSizeTensor);return Object.keys(s).forEach(a=>ne(s[a])),r}async predict(t,n){let s={};s.resize=Se.resizeBilinear(t,[this.inputSize,this.inputSize]),s.div=de(s.resize,Xe.tf127),s.image=he(s.div,Xe.tf1),s.batched=this.model.execute(s.image),s.predictions=rt(s.batched),s.slice=Pe(s.predictions,[0,0],[-1,1]),s.sigmoid=_n(s.slice),s.scores=rt(s.sigmoid);let r=await s.scores.data();s.boxes=Pe(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await Se.nonMaxSuppressionAsync(s.norm,s.scores,3*n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence);let a=await s.nms.array(),o=[];for(let i of a){let l={};l.box=Pe(s.norm,[i,0],[1,-1]),l.slice=Pe(s.predictions,[i,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,i),l.palmLandmarks=H(l.norm,[-1,2]);let c=await l.box.data(),u=c.slice(0,2),d=c.slice(2,4),p=await l.palmLandmarks.array(),h={startPoint:u,endPoint:d,palmLandmarks:p,confidence:r[i]},f=ET(h,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);o.push(f),Object.keys(l).forEach(m=>ne(l[m]))}return Object.keys(s).forEach(i=>ne(s[i])),o}};var hye=5,PT=1.65,OT=[0,5,9,13,17,1,2],fye=0,mye=2,MT=0,Mb=class{constructor(t,n){fe(this,"handDetector");fe(this,"handPoseModel");fe(this,"inputSize");fe(this,"storedBoxes");fe(this,"skipped");fe(this,"detectedHands");this.handDetector=t,this.handPoseModel=n,this.inputSize=this.handPoseModel&&this.handPoseModel.inputs[0].shape?this.handPoseModel.inputs[0].shape[2]:0,this.storedBoxes=[],this.skipped=Number.MAX_SAFE_INTEGER,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(o=>o[0]),s=t.map(o=>o[1]),r=[Math.min(...n),Math.min(...s)],a=[Math.max(...n),Math.max(...s)];return{startPoint:r,endPoint:a}}getBoxForPalmLandmarks(t,n){let s=t.map(a=>Pb([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return I0(C0(r),hye)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=I0(C0(n),PT);s.palmLandmarks=[];for(let r=0;r<OT.length;r++)s.palmLandmarks.push(t[OT[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=S0(n),o=[a[0]/this.inputSize,a[1]/this.inputSize,(a[0]+a[1])/this.inputSize/2],i=t.map(h=>[o[0]*(h[0]-this.inputSize/2),o[1]*(h[1]-this.inputSize/2),o[2]*h[2]]),l=Fb(s,[0,0]),c=i.map(h=>[...Pb(h,l),h[2]]),u=$T(r),d=[...Vp(n),1],p=[ni(d,u[0]),ni(d,u[1])];return c.map(h=>[Math.trunc(h[0]+p[0]),Math.trunc(h[1]+p[1]),Math.trunc(h[2])])}async estimateHands(t,n){let s=!1,r,a=(n.hand.skipTime||0)>ie()-MT,o=this.skipped<(n.hand.skipFrames||0);n.skipAllowed&&a&&o&&(r=await this.handDetector.predict(t,n),this.skipped=0),n.skipAllowed&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(s=!0));let i=[];for(let l=0;l<this.storedBoxes.length;l++){let c=this.storedBoxes[l];if(!!c)if(n.hand.landmarks){let u=n.hand.rotation?RT(c.palmLandmarks[fye],c.palmLandmarks[mye]):0,d=Vp(c),p=[d[0]/t.shape[2],d[1]/t.shape[1]],h=n.hand.rotation&&pe.kernels.includes("rotatewithoffset")?Se.rotateWithOffset(t,u,0,p):t.clone(),f=Fb(-u,d),m=s?this.getBoxForPalmLandmarks(c.palmLandmarks,f):c,g=NT(m,h,[this.inputSize,this.inputSize]),y=de(g,Xe.tf255);ne(g),ne(h);let[x,A]=this.handPoseModel.execute(y);MT=ie(),ne(y);let b=(await x.data())[0];if(ne(x),b>=n.hand.minConfidence/4){let w=H(A,[-1,3]),C=await w.array();ne(A),ne(w);let S=this.transformRawCoords(C,m,u,f),E=this.getBoxForHandLandmarks(S);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:S,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};i.push(R)}else this.storedBoxes[l]=null;ne(A)}else{let u=I0(C0(c),PT),d={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:u.startPoint,bottomRight:u.endPoint},landmarks:[]};i.push(d)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var Jn={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Jn.nameMapping[e],getPoints:e=>Jn.pointsMapping[e]},si={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>si.nameMapping[e]},Bt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Bt.nameMapping[e]},Wl=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,n,s){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,s])}direction(t,n,s){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,s])}weight(t,n){this.weights[t]=n;let s=this.weights.reduce((r,a)=>r+a,0);this.weightsRelative=this.weights.map(r=>r*5/s)}matchAgainst(t,n){let s=0;for(let r in t){let a=t[r],o=this.curls[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}for(let r in n){let a=n[r],o=this.directions[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}return s/10}};var{thumb:Sr,index:fa,middle:ma,ring:Vl,pinky:Ul}=Jn,{none:Ir,half:gye,full:Cr}=si,{verticalUp:Pc,verticalDown:cxe,horizontalLeft:zb,horizontalRight:yye,diagonalUpRight:Aye,diagonalUpLeft:Oc,diagonalDownRight:dxe,diagonalDownLeft:pxe}=Bt,ri=new Wl("thumbs up");ri.curl(Sr,Ir,1);ri.direction(Sr,Pc,1);ri.direction(Sr,Oc,.25);ri.direction(Sr,Aye,.25);for(let e of[Jn.index,Jn.middle,Jn.ring,Jn.pinky])ri.curl(e,Cr,1),ri.direction(e,zb,1),ri.direction(e,yye,1);var en=new Wl("victory");en.curl(Sr,gye,.5);en.curl(Sr,Ir,.5);en.direction(Sr,Pc,1);en.direction(Sr,Oc,1);en.curl(fa,Ir,1);en.direction(fa,Pc,.75);en.direction(fa,Oc,1);en.curl(ma,Ir,1);en.direction(ma,Pc,1);en.direction(ma,Oc,.75);en.curl(Vl,Cr,1);en.direction(Vl,Pc,.2);en.direction(Vl,Oc,1);en.direction(Vl,zb,.2);en.curl(Ul,Cr,1);en.direction(Ul,Pc,.2);en.direction(Ul,Oc,1);en.direction(Ul,zb,.2);en.weight(fa,2);en.weight(ma,2);var ai=new Wl("point");ai.curl(Sr,Cr,1);ai.curl(fa,Ir,.5);ai.curl(ma,Cr,.5);ai.curl(Vl,Cr,.5);ai.curl(Ul,Cr,.5);ai.weight(fa,2);ai.weight(ma,2);var oi=new Wl("middle finger");oi.curl(Sr,Ir,1);oi.curl(fa,Cr,.5);oi.curl(ma,Cr,.5);oi.curl(Vl,Cr,.5);oi.curl(Ul,Cr,.5);oi.weight(fa,2);oi.weight(ma,2);var Mc=new Wl("open 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g=Math.sqrt(r*r+i*i),y=Math.sqrt(a*a+l*l),x=Math.sqrt(o*o+c*c),A=Math.max(g,y,x),b=e[0],w=e[1],C=n[0],S=n[1];A===g?(C=n[0],S=n[1]):A===x&&(b=t[0],w=t[1]);let P=BT([b,w],[C,S]),_=WT(P,Gl.TOTAL_ANGLE_VOTE_POWER);p+=_[0],h+=_[1],f+=_[2];for(let T of s){let O=WT(T,Gl.SINGLE_ANGLE_VOTE_POWER);p+=O[0],h+=O[1],f+=O[2]}let $;return p===Math.max(p,h,f)?$=UT(l,i,c,d):f===Math.max(h,f)?$=VT(a,r,o,u):$=vye(l,i,c,d,a,r,o,u),$}function GT(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of Jn.all){let o=Jn.getPoints(a),i=[],l=[];for(let c of o){let u=e[c[0]],d=e[c[1]],p=BT(u,d),h=p[0],f=p[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of Jn.all){let o=a===Jn.thumb?1:0,i=Jn.getPoints(a),l=e[i[o][0]],c=e[i[o+1][1]],u=e[i[3][1]],d=bye(l,c,u),p=wye(l,c,u,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function T0(e){if(!e||e.length===0)return null;let t=GT(e),n={};for(let s of Jn.all)n[Jn.getName(s)]={curl:si.getName(t.curls[s]),direction:Bt.getName(t.directions[s])};return n}function HT(e){let t=[];if(!e||e.length===0)return t;let n=GT(e);for(let s of zT){let r=s.matchAgainst(n.curls,n.directions);r>=xye&&t.push({name:s.name,confidence:r})}return t}var jT={thumb:[1,2,3,4],index:[5,6,7,8],middle:[9,10,11,12],ring:[13,14,15,16],pinky:[17,18,19,20],palm:[0]},ga,ya,qT;async function Lb(e,t){let n=await qT.estimateHands(e,t);if(!n)return[];let s=[];for(let r=0;r<n.length;r++){let a={};if(n[r].landmarks)for(let u of Object.keys(jT))a[u]=jT[u].map(d=>n[r].landmarks[d]);let o=n[r].landmarks,i=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],l=[0,0,0,0];if(o&&o.length>0){for(let u of o)u[0]<i[0]&&(i[0]=u[0]),u[1]<i[1]&&(i[1]=u[1]),u[0]>i[2]&&(i[2]=u[0]),u[1]>i[3]&&(i[3]=u[1]);i[2]-=i[0],i[3]-=i[1],l=[i[0]/(e.shape[2]||0),i[1]/(e.shape[1]||0),i[2]/(e.shape[2]||0),i[3]/(e.shape[1]||0)]}else i=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],l=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];let c=T0(o);s.push({id:r,score:Math.round(100*n[r].confidence)/100,boxScore:Math.round(100*n[r].boxConfidence)/100,fingerScore:Math.round(100*n[r].fingerConfidence)/100,label:"hand",box:i,boxRaw:l,keypoints:o,annotations:a,landmarks:c})}return s}async function Bb(e){var n,s,r,a,o,i;pe.initial&&(ga=null,ya=null),!ga||!ya?([ga,ya]=await Promise.all([e.hand.enabled?Be(We(e.modelBasePath,((n=e.hand.detector)==null?void 0:n.modelPath)||""),{fromTFHub:(((s=e.hand.detector)==null?void 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Et=[null,null],kye=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],ii=[[0,0],[0,0]],Sye=["hand","fist","pinch","point","face","tip","pinchtip"],XT=4,KT=1.6,Iye=512,Cye=1.4,N0=Number.MAX_SAFE_INTEGER,Wb=0,Aa=[0,0],Xt={boxes:[],hands:[]},ZT={thumb:[0,1,2,3,4],index:[0,5,6,7,8],middle:[0,9,10,11,12],ring:[0,13,14,15,16],pinky:[0,17,18,19,20],palm:[0]};async function YT(e){var t,n;if(pe.initial&&(Et[0]=null),Et[0])e.debug&&J("cached model:",Et[0].modelUrl);else{E0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),Et[0]=await Be(We(e.modelBasePath,((t=e.hand.detector)==null?void 0:t.modelPath)||""));let s=Object.values(Et[0].modelSignature.inputs);ii[0][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,ii[0][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!Et[0]||!Et[0].modelUrl?J("load model failed:",(n=e.hand.detector)==null?void 0:n.modelPath):e.debug&&J("load model:",Et[0].modelUrl)}return Et[0]}async function JT(e){var t,n;if(pe.initial&&(Et[1]=null),Et[1])e.debug&&J("cached model:",Et[1].modelUrl);else{Et[1]=await Be(We(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let s=Object.values(Et[1].modelSignature.inputs);ii[1][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,ii[1][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!Et[1]||!Et[1].modelUrl?J("load model failed:",(n=e.hand.skeleton)==null?void 0:n.modelPath):e.debug&&J("load model:",Et[1].modelUrl)}return Et[1]}async function Tye(e,t){let n=[];if(!e||!Et[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,Iye),o=Math.round(a*r/8)*8;s.resize=Se.resizeBilinear(e,[a,o]),s.cast=me(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await Et[0].executeAsync(s.cast,kye),s.boxes=rt(s.rawBoxes,[0,2]),s.scores=rt(s.rawScores,[0]);let i=os(s.scores,1);ne(i[XT]),i.splice(XT,1),s.filtered=an(i,1),ne(i),s.max=An(s.filtered,1),s.argmax=Fs(s.filtered,1);let l=0;s.nms=await Se.nonMaxSuppressionAsync(s.boxes,s.max,t.hand.maxDetected,t.hand.iouThreshold,t.hand.minConfidence);let c=await s.nms.data(),u=await s.max.data(),d=await s.argmax.data();for(let p of Array.from(c)){let h=Pe(s.boxes,p,1),f=await h.data();ne(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=x0(m,Cye),y=fb(g),x=[Math.trunc(m[0]*Aa[0]),Math.trunc(m[1]*Aa[1]),Math.trunc(m[2]*Aa[0]),Math.trunc(m[3]*Aa[1])],A=u[p],b=Sye[d[p]],w={id:l++,score:A,box:x,boxRaw:g,boxCrop:y,label:b};n.push(w)}return Object.keys(s).forEach(p=>ne(s[p])),n.sort((p,h)=>h.score-p.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function Vb(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&Et[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={};r.crop=Se.cropAndResize(e,[t.boxCrop],[0],[ii[1][0],ii[1][1]],"bilinear"),r.div=de(r.crop,Xe.tf255),[r.score,r.keypoints]=Et[1].execute(r.div,["Identity_1","Identity"]);let a=(await r.score.data())[0],o=(100-Math.trunc(100/(1+Math.exp(a))))/100;if(o>=(n.hand.minConfidence||0)){s.fingerScore=o,r.reshaped=H(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(u=>[u[0]/ii[1][1],u[1]/ii[1][0],u[2]||0]).map(u=>[u[0]*t.boxRaw[2],u[1]*t.boxRaw[3],u[2]||0]);s.keypoints=c.map(u=>[Aa[0]*(u[0]+t.boxRaw[0]),Aa[1]*(u[1]+t.boxRaw[1]),u[2]||0]),s.landmarks=T0(s.keypoints);for(let u of Object.keys(ZT))s.annotations[u]=ZT[u].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(i=>ne(r[i]))}return s}async function Ub(e,t){var r,a;if(!Et[0]||!Et[1]||!((r=Et[0])==null?void 0:r.inputs[0].shape)||!((a=Et[1])==null?void 0:a.inputs[0].shape))return[];Aa=[e.shape[2]||0,e.shape[1]||0],N0++;let n=(t.hand.skipTime||0)>ie()-Wb,s=N0<(t.hand.skipFrames||0);return t.skipAllowed&&n&&s?Xt.hands:new Promise(async o=>{let i=3*(t.hand.skipTime||0)>ie()-Wb,l=N0<3*(t.hand.skipFrames||0);t.skipAllowed&&Xt.hands.length===t.hand.maxDetected?Xt.hands=await Promise.all(Xt.boxes.map(u=>Vb(e,u,t))):t.skipAllowed&&i&&l&&Xt.hands.length>0?Xt.hands=await Promise.all(Xt.boxes.map(u=>Vb(e,u,t))):(Xt.boxes=await Tye(e,t),Wb=ie(),Xt.hands=await Promise.all(Xt.boxes.map(u=>Vb(e,u,t))),N0=0);let c=[...Xt.boxes];if(Xt.boxes.length=0,t.cacheSensitivity>0)for(let u=0;u<Xt.hands.length;u++){let d=Q8(Xt.hands[u].keypoints,Aa);if(d.box[2]/(e.shape[2]||1)>.05&&d.box[3]/(e.shape[1]||1)>.05&&Xt.hands[u].fingerScore&&Xt.hands[u].fingerScore>(t.hand.minConfidence||0)){let p=x0(d.box,KT),h=x0(d.boxRaw,KT),f=fb(h);Xt.boxes.push({...c[u],box:p,boxRaw:h,boxCrop:f})}}for(let u=0;u<Xt.hands.length;u++){let d=ha(Xt.hands[u].keypoints,Aa);Xt.hands[u].box=d.box,Xt.hands[u].boxRaw=d.boxRaw}o(Xt.hands)})}var fn,R0=[],Gb=Number.MAX_SAFE_INTEGER,QT=0,eN=0;async function tN(e){var t,n;return pe.initial&&(fn=null),fn?e.debug&&J("cached model:",fn.modelUrl):(fn=await Be(We(e.modelBasePath,((t=e.face.liveness)==null?void 0:t.modelPath)||"")),!fn||!fn.modelUrl?J("load model failed:",(n=e.face.liveness)==null?void 0:n.modelPath):e.debug&&J("load model:",fn.modelUrl)),fn}async function Hb(e,t,n,s){var o,i;if(!fn)return 0;let r=(((o=t.face.liveness)==null?void 0:o.skipTime)||0)>ie()-eN,a=Gb<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&r&&a&&QT===s&&R0[n]?(Gb++,R0[n]):(Gb=0,new Promise(async l=>{let c=Se.resizeBilinear(e,[(fn==null?void 0:fn.inputs[0].shape)?fn.inputs[0].shape[2]:0,(fn==null?void 0:fn.inputs[0].shape)?fn.inputs[0].shape[1]:0],!1),u=fn==null?void 0:fn.execute(c),d=(await u.data())[0];R0[n]=Math.round(100*d)/100,QT=s,eN=ie(),ne([c,u]),l(R0[n])}))}var Kb={};td(Kb,{connected:()=>D0,horizontal:()=>jb,kpt:()=>_0,relative:()=>Xb,vertical:()=>qb});var 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nN=.005,Ts={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function Zb(e){for(let t of jb){let n=e.keypoints.findIndex(r=>r.part===t[0]),s=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[0]<e.keypoints[s].position[0]){let r=e.keypoints[n];e.keypoints[n]=e.keypoints[s],e.keypoints[s]=r}}for(let t of qb){let n=e.keypoints.findIndex(r=>r&&r.part===t[0]),s=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[1]<e.keypoints[s].position[1]&&e.keypoints.splice(n,1)}for(let[t,n]of Xb){let s=e.keypoints.findIndex(c=>c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),a=e.keypoints.findIndex(c=>c&&c.part===n[0]),o=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[a]||!e.keypoints[o])continue;let 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a=e[0][r],o=Math.round(100*a[51+4])/100;if(o>t.body.minConfidence){let i=[];for(let d=0;d<17;d++){let p=a[3*d+2];if(p>t.body.minConfidence){let h=[a[3*d+1],a[3*d+0]];i.push({part:_0[d],score:Math.round(100*p)/100,positionRaw:h,position:[Math.round((n.shape[2]||0)*h[0]),Math.round((n.shape[1]||0)*h[1])]})}}let l=ha(i.map(d=>d.position),[n.shape[2],n.shape[1]]),c={};for(let[d,p]of Object.entries(D0)){let h=[];for(let f=0;f<p.length-1;f++){let m=i.find(y=>y.part===p[f]),g=i.find(y=>y.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}c[d]=h}let u={id:r,score:o,box:l.box,boxRaw:l.boxRaw,keypoints:[...i],annotations:c};Zb(u),s.push(u)}}return s.sort((r,a)=>a.score-r.score),s.length>t.body.maxDetected&&(s.length=t.body.maxDetected),s}async function Jb(e,t){if(!Pn||!(Pn==null?void 0:Pn.inputs[0].shape))return[];t.skipAllowed||(Hl.boxes.length=0),Yb++;let n=(t.body.skipTime||0)>ie()-Hl.last,s=Yb<(t.body.skipFrames||0);return t.skipAllowed&&n&&s?Hl.bodies:new Promise(async r=>{let a={};Yb=0,a.input=rN(e,oN),a.res=Pn==null?void 0:Pn.execute(a.input),Hl.last=ie();let o=await a.res.array();Hl.bodies=a.res.shape[2]===17?await Nye(o,t,e):await Eye(o,t,e);for(let i of Hl.bodies)aN(i,[e.shape[2]||1,e.shape[1]||1]),sN(i.keypoints);Object.keys(a).forEach(i=>ne(a[i])),r(Hl.bodies)})}var Gs,$0=[],lN=0,Qb=Number.MAX_SAFE_INTEGER,F0=2.5;async function uN(e){if(!Gs||pe.initial){Gs=await Be(We(e.modelBasePath,e.object.modelPath||""));let t=Object.values(Gs.modelSignature.inputs);Gs.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Gs||!Gs.modelUrl?J("load model failed:",e.object.modelPath):e.debug&&J("load model:",Gs.modelUrl)}else e.debug&&J("cached model:",Gs.modelUrl);return Gs}async function Rye(e,t,n,s){let r=0,a=[];for(let c of[1,2,4])X(async()=>{var g,y;let u=c*13,d=(g=e.find(x=>x.shape[1]===u**2&&x.shape[2]===_c.length))==null?void 0:g.squeeze(),p=(y=e.find(x=>x.shape[1]===u**2&&x.shape[2]<_c.length))==null?void 0:y.squeeze(),f=await p.reshape([-1,4,p.shape[1]/4]).argMax(2).array(),m=await d.array();for(let x=0;x<d.shape[0];x++)for(let A=0;A<d.shape[1];A++){let b=m[x][A];if(b>s.object.minConfidence&&A!==61){let w=(.5+Math.trunc(x%u))/u,C=(.5+Math.trunc(x/u))/u,S=f[x].map(V=>V*(u/c/t)),[E,R]=[w-F0/c*S[0],C-F0/c*S[1]],[P,_]=[w+F0/c*S[2]-E,C+F0/c*S[3]-R],$=[E,R,P,_];$=$.map(V=>Math.max(0,Math.min(V,1)));let T=[$[0]*n[0],$[1]*n[1],$[2]*n[0],$[3]*n[1]],O={id:r++,score:Math.round(100*b)/100,class:A+1,label:_c[A].label,box:T.map(V=>Math.trunc(V)),boxRaw:$};a.push(O)}}});e.forEach(c=>ne(c));let o=a.map(c=>[c.boxRaw[1],c.boxRaw[0],c.boxRaw[3],c.boxRaw[2]]),i=a.map(c=>c.score),l=[];if(o&&o.length>0){let c=await Se.nonMaxSuppressionAsync(o,i,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);l=await c.data(),ne(c)}return 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Up=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_ye=Up.length,Gp=Up.reduce((e,t,n)=>(e[t]=n,e),{}),Dye=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],ebe=Dye.map(([e,t])=>[Gp[e],Gp[t]]),cN=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function dN(e){let t=e.reduce(({maxX:n,maxY:s,minX:r,minY:a},{position:{x:o,y:i}})=>({maxX:Math.max(n,o),maxY:Math.max(s,i),minX:Math.min(r,o),minY:Math.min(a,i)}),{maxX:Number.NEGATIVE_INFINITY,maxY:Number.NEGATIVE_INFINITY,minX:Number.POSITIVE_INFINITY,minY:Number.POSITIVE_INFINITY});return[t.minX,t.minY,t.maxX-t.minX,t.maxY-t.minY]}function pN(e,[t,n],[s,r]){let a=t/s,o=n/r,i=(c,u)=>({id:u,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/s,c.box[2]/r,c.box[3]/s],box:[Math.trunc(c.box[0]*o),Math.trunc(c.box[1]*a),Math.trunc(c.box[2]*o),Math.trunc(c.box[3]*a)],keypoints:c.keypoints.map(({score:d,part:p,position:h})=>({score:d,part:p,position:[Math.trunc(h.x*o),Math.trunc(h.y*a)],positionRaw:[h.x/s,h.y/s]})),annotations:{}});return e.map((c,u)=>i(c,u))}var t5=class{constructor(t,n){fe(this,"priorityQueue");fe(this,"numberOfElements");fe(this,"getElementValue");this.priorityQueue=new 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0.3)`:n.color,e.fillStyle=n.useDepth&&r!==0?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(s[0],Math.round(s[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function Vye(e,t,n){if(!(t.length<2)){if(e.lineWidth=n.lineWidth,!n.useCurves||t.length<=2){vN(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let s=0;s<t.length-2;s++){let r=(t[s][0]+t[s+1][0])/2,a=(t[s][1]+t[s+1][1])/2;e.quadraticCurveTo(t[s][0],t[s][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function wN(e,t,n,s=5){let r,a,o;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(n[0],n[1]),r=Math.atan2(n[1]-t[1],n[0]-t[0]),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.moveTo(a,o),r+=1/3*(2*Math.PI),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.lineTo(a,o),r+=1/3*(2*Math.PI),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.lineTo(a,o),e.closePath(),e.stroke(),e.fill()}async function h5(e,t,n){let s=En(xa,n);if(!(!t||!e)&&s.drawGestures){let r=jl(e);if(!r)return;r.font=s.font,r.fillStyle=s.color;let a=1;for(let o=0;o<t.length;o++){let i=[],l=[];if([i,l]=Object.entries(t[o]),l.length>1&&l[1].length>0){let c=i[1]>0?`#${i[1]}`:"",u=`${i[0]} ${c}: ${l[1]}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(u,8,2+a*s.lineHeight)),r.fillStyle=s.labelColor,r.fillText(u,6,0+a*s.lineHeight),a+=1}}}}async function f5(e,t,n){var a,o,i,l,c;let s=En(xa,n);if(!t||!e)return;let r=jl(e);if(!!r)for(let u of t){if(r.font=s.font,r.strokeStyle=s.color,r.fillStyle=s.color,s.drawBoxes&&Hp(r,u.box[0],u.box[1],u.box[2],u.box[3],s),s.drawLabels){let d=[];if(d.push(`face: ${Math.trunc(100*u.score)}%`),u.genderScore&&d.push(`${u.gender||""} ${Math.trunc(100*u.genderScore)}%`),u.age&&d.push(`age: ${u.age||""}`),u.iris&&d.push(`distance: ${u.iris}`),u.real&&d.push(`real: ${Math.trunc(100*u.real)}%`),u.live&&d.push(`live: ${Math.trunc(100*u.live)}%`),u.emotion&&u.emotion.length>0){let p=u.emotion.map(h=>`${Math.trunc(100*h.score)}% ${h.emotion}`);p.length>3&&(p.length=3),d.push(p.join(" "))}u.rotation&&u.rotation.angle&&u.rotation.gaze&&(u.rotation.angle.roll&&d.push(`roll: ${Lc(u.rotation.angle.roll)}\xB0 yaw:${Lc(u.rotation.angle.yaw)}\xB0 pitch:${Lc(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&d.push(`gaze: ${Lc(u.rotation.gaze.bearing)}\xB0`)),d.length===0&&d.push("face"),r.fillStyle=s.color;for(let p=d.length-1;p>=0;p--){let h=Math.max(u.box[0],0),f=p*s.lineHeight+u.box[1];s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(d[p],h+5,f+16)),r.fillStyle=s.labelColor,r.fillText(d[p],h+4,f+15)}}if(r.lineWidth=2,u.mesh&&u.mesh.length>0){if(s.drawPoints)for(let d of u.mesh)p5(r,d[0],d[1],d[2],s);if(s.drawPolygons){if(u.mesh.length>450)for(let d=0;d<zl.length/3;d++){let p=[zl[d*3+0],zl[d*3+1],zl[d*3+2]].map(h=>u.mesh[h]);vN(r,p,s)}if(u.annotations&&u.annotations.leftEyeIris&&u.annotations.leftEyeIris[0]){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.leftEyeIris[3][0]-u.annotations.leftEyeIris[1][0])/2,p=Math.abs(u.annotations.leftEyeIris[4][1]-u.annotations.leftEyeIris[2][1])/2;r.ellipse(u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(u.annotations&&u.annotations.rightEyeIris&&u.annotations.rightEyeIris[0]){r.strokeStyle=s.useDepth?"rgba(255, 200, 255, 0.3)":s.color,r.beginPath();let d=Math.abs(u.annotations.rightEyeIris[3][0]-u.annotations.rightEyeIris[1][0])/2,p=Math.abs(u.annotations.rightEyeIris[4][1]-u.annotations.rightEyeIris[2][1])/2;r.ellipse(u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1],d,p,0,0,2*Math.PI),r.stroke(),s.fillPolygons&&(r.fillStyle=s.useDepth?"rgba(255, 255, 200, 0.3)":s.color,r.fill())}if(s.drawGaze&&((a=u.rotation)==null?void 0:a.angle)&&typeof Path2D!="undefined"){r.strokeStyle="pink";let d=u.box[0]+u.box[2]/2-u.box[3]*Lc(u.rotation.angle.yaw)/90,p=u.box[1]+u.box[3]/2+u.box[2]*Lc(u.rotation.angle.pitch)/90,h=new Path2D(`
|
|
M ${u.box[0]+u.box[2]/2} ${u.box[1]}
|
|
C
|
|
${d} ${u.box[1]},
|
|
${d} ${u.box[1]+u.box[3]},
|
|
${u.box[0]+u.box[2]/2} ${u.box[1]+u.box[3]}
|
|
`),f=new Path2D(`
|
|
M ${u.box[0]} ${u.box[1]+u.box[3]/2}
|
|
C
|
|
${u.box[0]} ${p},
|
|
${u.box[0]+u.box[2]} ${p},
|
|
${u.box[0]+u.box[2]} ${u.box[1]+u.box[3]/2}
|
|
`);r.stroke(f),r.stroke(h)}if(s.drawGaze&&((i=(o=u.rotation)==null?void 0:o.gaze)==null?void 0:i.strength)&&((c=(l=u.rotation)==null?void 0:l.gaze)==null?void 0:c.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.fillStyle="pink";let d=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];wN(r,[u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]],[d[0],d[1]],4);let p=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];wN(r,[u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]],[p[0],p[1]],4)}}}}}async function m5(e,t,n){var a;let s=En(xa,n);if(!t||!e)return;let r=jl(e);if(!!r){r.lineJoin="round";for(let o=0;o<t.length;o++){if(r.strokeStyle=s.color,r.fillStyle=s.color,r.lineWidth=s.lineWidth,r.font=s.font,s.drawBoxes&&t[o].box&&((a=t[o].box)==null?void 0:a.length)===4&&(Hp(r,t[o].box[0],t[o].box[1],t[o].box[2],t[o].box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+3,1+t[o].box[1]+s.lineHeight,t[o].box[2])),r.fillStyle=s.labelColor,r.fillText(`body ${100*t[o].score}%`,t[o].box[0]+2,0+t[o].box[1]+s.lineHeight,t[o].box[2]))),s.drawPoints&&t[o].keypoints)for(let i=0;i<t[o].keypoints.length;i++)!t[o].keypoints[i].score||t[o].keypoints[i].score===0||(r.fillStyle=s.useDepth&&t[o].keypoints[i].position[2]?`rgba(${127.5+2*(t[o].keypoints[i].position[2]||0)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:s.color,p5(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s));if(s.drawLabels&&t[o].keypoints){r.font=s.font;for(let i of t[o].keypoints)!i.score||i.score===0||(r.fillStyle=s.useDepth&&i.position[2]?`rgba(${127.5+2*i.position[2]}, ${127.5-2*i.position[2]}, 255, 0.5)`:s.color,r.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4))}if(s.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let i of Object.values(t[o].annotations))for(let l of i)Vye(r,l,s)}}}async function g5(e,t,n){let s=En(xa,n);if(!t||!e)return;let r=jl(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,Hp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(`hand:${Math.trunc(100*a.score)}%`,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])),r.stroke()),s.drawPoints&&a.keypoints&&a.keypoints.length>0)for(let o of a.keypoints)r.fillStyle=s.useDepth?`rgba(${127.5+2*(o[2]||0)}, ${127.5-2*(o[2]||0)}, 255, 0.5)`:s.color,p5(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{if(!i||i.length===0||!i[0])return;let c=i[i.length-1][2]||0;r.fillStyle=s.useDepth?`rgba(${127.5+2*c}, ${127.5-2*c}, 255, 0.5)`:s.color,r.fillText(l,i[i.length-1][0]+4,i[i.length-1][1]+4)};r.font=s.font,o(a.annotations.index,"index"),o(a.annotations.middle,"middle"),o(a.annotations.ring,"ring"),o(a.annotations.pinky,"pinky"),o(a.annotations.thumb,"thumb"),o(a.annotations.palm,"palm")}if(s.drawPolygons&&a.annotations){let o=i=>{if(!(!i||i.length===0||!i[0]))for(let l=0;l<i.length;l++){r.beginPath();let c=i[l][2]||0;r.strokeStyle=s.useDepth?`rgba(${127.5+l*c}, ${127.5-l*c}, 255, 0.5)`:s.color,r.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()}};r.lineWidth=s.lineWidth,o(a.annotations.index),o(a.annotations.middle),o(a.annotations.ring),o(a.annotations.pinky),o(a.annotations.thumb)}}}}async function y5(e,t,n){let s=En(xa,n);if(!t||!e)return;let r=jl(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,Hp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}}async function kN(e,t,n){let s=En(xa,n);if(!t||!e)return;let r=jl(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a=0;a<t.length;a++)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,Hp(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person #${a}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,t[a].box[0]+3,1+t[a].box[1]+s.lineHeight,t[a].box[2])),r.fillStyle=s.labelColor,r.fillText(o,t[a].box[0]+2,0+t[a].box[1]+s.lineHeight,t[a].box[2])}r.stroke()}}}async function SN(e,t){if(!e||!t)return;let n=jl(t);!n||n.drawImage(e,0,0)}async function IN(e,t,n){if(!t||!t.performance||!t||!e)return null;let s=ie(),r=En(xa,n),a=Promise.all([f5(e,t.face,r),m5(e,t.body,r),g5(e,t.hand,r),y5(e,t.object,r),h5(e,t.gesture,r)]);return d5=pe.perfadd?d5+Math.round(ie()-s):Math.round(ie()-s),t.performance.draw=d5,a}var Bc=.1,A5=.5;function Uye(e,t,n){let s=!1,r=n.length-1;for(let a=0;a<n.length;r=a++)n[a].y>t!=n[r].y>t&&e<(n[r].x-n[a].x)*(t-n[a].y)/(n[r].y-n[a].y)+n[a].x&&(s=!s);return s}async function CN(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,s=await e.tensor.buffer(),r=[];for(let o of or.silhouette)r.push({x:(e.mesh[o][0]-e.box[0])/e.box[2],y:(e.mesh[o][1]-e.box[1])/e.box[3]});Bc&&Bc>0&&(r=r.map(o=>({x:o.x>.5?o.x+Bc:o.x-Bc,y:o.y>.5?o.y+Bc:o.y-Bc})));for(let o=0;o<t;o++)for(let i=0;i<n;i++)Uye(o/t,i/t,r)||(s.set(A5*s.get(0,i,o,0),0,i,o,0),s.set(A5*s.get(0,i,o,1),0,i,o,1),s.set(A5*s.get(0,i,o,2),0,i,o,2));let a=s.toTensor();return ne(s),a}var Gye=e=>{let t=(d,p)=>Math.atan2(d[1]-p[1],d[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],s=1,r=e.mesh[33][2]>e.mesh[263][2],a=r?e.mesh[473]:e.mesh[468],o=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(o[0]-a[0])/i[0]-n[0],s*(a[1]-o[1])/i[1]-n[1]],c=Math.sqrt(l[0]**2+l[1]**2);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},TN=(e,t)=>{let n=m=>{let g=Math.sqrt(m[0]*m[0]+m[1]*m[1]+m[2]*m[2]);return m[0]/=g,m[1]/=g,m[2]/=g,m},s=(m,g)=>{let y=m[0]-g[0],x=m[1]-g[1],A=m[2]-g[2];return[y,x,A]},r=(m,g)=>{let y=m[1]*g[2]-m[2]*g[1],x=m[2]*g[0]-m[0]*g[2],A=m[0]*g[1]-m[1]*g[0];return[y,x,A]},a=m=>{let[g,y,x,A,b,w,C,S,E]=m,R,P,_;return A<1?A>-1?(_=Math.asin(A),P=Math.atan2(-C,g),R=Math.atan2(-w,b)):(_=-Math.PI/2,P=-Math.atan2(S,E),R=0):(_=Math.PI/2,P=Math.atan2(S,E),R=0),isNaN(R)&&(R=0),isNaN(P)&&(P=0),isNaN(_)&&(_=0),{pitch:2*-R,yaw:2*-P,roll:2*-_}},o=e.meshRaw;if(!o||o.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let i=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[o[10],o[152],o[234],o[454]].map(m=>[m[0]*t[0]/i,m[1]*t[1]/i,m[2]]),c=n(s(l[1],l[0])),u=n(s(l[3],l[2])),d=n(r(u,c));u=r(c,d);let p=[u[0],u[1],u[2],c[0],c[1],c[2],d[0],d[1],d[2]],h=a(p),f=o.length===478?Gye(e):{bearing:0,strength:0};return{angle:h,matrix:p,gaze:f}};var x5=async(e,t)=>{var h,f,m,g,y,x,A,b,w,C,S,E,R,P,_,$,T,O,V,K,z,j;let n,s,r,a,o,i,l,c,u,d=[];e.state="run:face",n=ie();let p=await vT(t,e.config);if(e.performance.face=pe.perfadd?(e.performance.face||0)+Math.trunc(ie()-n):Math.trunc(ie()-n),!t.shape||t.shape.length!==4)return[];if(!p)return[];for(let W=0;W<p.length;W++){if(e.analyze("Get Face"),!p[W].tensor||p[W].tensor.isDisposedInternal){J("Face object is disposed:",p[W].tensor);continue}if((h=e.config.face.detector)==null?void 0:h.mask){let oe=await CN(p[W]);ne(p[W].tensor),p[W].tensor=oe}let Q=p[W].mesh&&p[W].mesh.length>200?TN(p[W],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?o=((f=e.config.face.emotion)==null?void 0:f.enabled)?Cb(p[W].tensor||pt([]),e.config,W,p.length):null:(e.state="run:emotion",n=ie(),o=((m=e.config.face.emotion)==null?void 0:m.enabled)?await Cb(p[W].tensor||pt([]),e.config,W,p.length):null,e.performance.emotion=pe.perfadd?(e.performance.emotion||0)+Math.trunc(ie()-n):Math.trunc(ie()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=((g=e.config.face.antispoof)==null?void 0:g.enabled)?rb(p[W].tensor||pt([]),e.config,W,p.length):null:(e.state="run:antispoof",n=ie(),l=((y=e.config.face.antispoof)==null?void 0:y.enabled)?await rb(p[W].tensor||pt([]),e.config,W,p.length):null,e.performance.antispoof=pe.perfadd?(e.performance.antispoof||0)+Math.trunc(ie()-n):Math.trunc(ie()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?c=((x=e.config.face.liveness)==null?void 0:x.enabled)?Hb(p[W].tensor||pt([]),e.config,W,p.length):null:(e.state="run:liveness",n=ie(),c=((A=e.config.face.liveness)==null?void 0:A.enabled)?await Hb(p[W].tensor||pt([]),e.config,W,p.length):null,e.performance.liveness=pe.perfadd?(e.performance.antispoof||0)+Math.trunc(ie()-n):Math.trunc(ie()-n)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=((b=e.config.face.gear)==null?void 0:b.enabled)?Yx(p[W].tensor||pt([]),e.config,W,p.length):{}:(e.state="run:gear",n=ie(),r=((w=e.config.face.gear)==null?void 0:w.enabled)?await Yx(p[W].tensor||pt([]),e.config,W,p.length):{},e.performance.gear=Math.trunc(ie()-n)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(s=((C=e.config.face.ssrnet)==null?void 0:C.enabled)?Qx(p[W].tensor||pt([]),e.config,W,p.length):{},a=((S=e.config.face.ssrnet)==null?void 0:S.enabled)?nb(p[W].tensor||pt([]),e.config,W,p.length):{}):(e.state="run:ssrnet",n=ie(),s=((E=e.config.face.ssrnet)==null?void 0:E.enabled)?await Qx(p[W].tensor||pt([]),e.config,W,p.length):{},a=((R=e.config.face.ssrnet)==null?void 0:R.enabled)?await nb(p[W].tensor||pt([]),e.config,W,p.length):{},e.performance.ssrnet=Math.trunc(ie()-n)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?i=((P=e.config.face.mobilefacenet)==null?void 0:P.enabled)?Nb(p[W].tensor||pt([]),e.config,W,p.length):{}:(e.state="run:mobilefacenet",n=ie(),i=((_=e.config.face.mobilefacenet)==null?void 0:_.enabled)?await Nb(p[W].tensor||pt([]),e.config,W,p.length):{},e.performance.mobilefacenet=Math.trunc(ie()-n)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?u=(($=e.config.face.description)==null?void 0:$.enabled)?$b(p[W].tensor||pt([]),e.config,W,p.length):null:(e.state="run:description",n=ie(),u=((T=e.config.face.description)==null?void 0:T.enabled)?await $b(p[W].tensor||pt([]),e.config,W,p.length):null,e.performance.description=pe.perfadd?(e.performance.description||0)+Math.trunc(ie()-n):Math.trunc(ie()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,u,r,l,c]=await Promise.all([s,a,o,i,u,r,l,c])),e.analyze("Finish Face:"),((O=e.config.face.ssrnet)==null?void 0:O.enabled)&&s&&a&&(u={age:s.age,gender:a.gender,genderScore:a.genderScore}),((V=e.config.face.gear)==null?void 0:V.enabled)&&r&&(u={age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((K=e.config.face.mobilefacenet)==null?void 0:K.enabled)&&i&&(u.descriptor=i),!((z=e.config.face.iris)==null?void 0:z.enabled);let te=p[W].annotations&&p[W].annotations.leftEyeIris&&p[W].annotations.leftEyeIris[0]&&p[W].annotations.rightEyeIris&&p[W].annotations.rightEyeIris[0]&&p[W].annotations.leftEyeIris.length>0&&p[W].annotations.rightEyeIris.length>0&&p[W].annotations.leftEyeIris[0]!==null&&p[W].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[W].annotations.leftEyeIris[3][0]-p[W].annotations.leftEyeIris[1][0]),Math.abs(p[W].annotations.rightEyeIris[4][1]-p[W].annotations.rightEyeIris[2][1]))/t.shape[2]:0,ae=((j=e.config.face.detector)==null?void 0:j.return)?rt(p[W].tensor):null;ne(p[W].tensor),p[W].tensor&&delete p[W].tensor;let Z={...p[W],id:W};(u==null?void 0:u.age)&&(Z.age=u.age),(u==null?void 0:u.gender)&&(Z.gender=u.gender),(u==null?void 0:u.genderScore)&&(Z.genderScore=u==null?void 0:u.genderScore),(u==null?void 0:u.descriptor)&&(Z.embedding=u==null?void 0:u.descriptor),(u==null?void 0:u.race)&&(Z.race=u==null?void 0:u.race),o&&(Z.emotion=o),l&&(Z.real=l),c&&(Z.live=c),te&&te!==0&&(Z.iris=Math.trunc(500/te/11.7)/100),Q&&(Z.rotation=Q),ae&&(Z.tensor=ae),d.push(Z),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),d};var NN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=e[n].keypoints.find(l=>l.part==="leftWrist"),r=e[n].keypoints.find(l=>l.part==="rightWrist"),a=e[n].keypoints.find(l=>l.part==="nose");a&&s&&r&&s.position[1]<a.position[1]&&r.position[1]<a.position[1]?t.push({body:n,gesture:"i give up"}):a&&s&&s.position[1]<a.position[1]?t.push({body:n,gesture:"raise left hand"}):a&&r&&r.position[1]<a.position[1]&&t.push({body:n,gesture:"raise right hand"});let o=e[n].keypoints.find(l=>l.part==="leftShoulder"),i=e[n].keypoints.find(l=>l.part==="rightShoulder");o&&i&&Math.abs(o.positionRaw[1]-i.positionRaw[1])>.1&&t.push({body:n,gesture:`leaning ${o.position[1]>i.position[1]?"left":"right"}`})}return t},EN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++)if(e[n].mesh&&e[n].mesh.length>450){let s=(e[n].mesh[33][2]||0)-(e[n].mesh[263][2]||0),r=e[n].mesh[33][0]-e[n].mesh[263][0];Math.abs(s/r)<=.15?t.push({face:n,gesture:"facing center"}):t.push({face:n,gesture:`facing ${s<0?"left":"right"}`}),Math.abs(e[n].mesh[374][1]-e[n].mesh[386][1])/Math.abs(e[n].mesh[443][1]-e[n].mesh[450][1])<.2&&t.push({face:n,gesture:"blink left eye"}),Math.abs(e[n].mesh[145][1]-e[n].mesh[159][1])/Math.abs(e[n].mesh[223][1]-e[n].mesh[230][1])<.2&&t.push({face:n,gesture:"blink right eye"});let i=Math.min(100,500*Math.abs(e[n].mesh[13][1]-e[n].mesh[14][1])/Math.abs(e[n].mesh[10][1]-e[n].mesh[152][1]));i>10&&t.push({face:n,gesture:`mouth ${Math.trunc(i)}% open`});let l=e[n].mesh[152][2]||0;Math.abs(l)>10&&t.push({face:n,gesture:`head ${l<0?"up":"down"}`})}return t},RN=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.leftEyeIris[0]||!e[n].annotations.rightEyeIris||!e[n].annotations.rightEyeIris[0])continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),c=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(c=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2],p=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2];(d>.06||p>.06)&&(c=!1),d>p?d>.05&&t.push({iris:n,gesture:"looking right"}):p>.05&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(c=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),c&&t.push({iris:n,gesture:"looking center"})}return t},_N=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>(o.position[2]||0)<(i.position[2]||0)?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let r=HT(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var De={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},b5=0;function DN(e,t){var o,i,l,c,u,d,p,h,f,m,g,y,x,A,b,w,C,S,E,R,P,_,$,T,O,V,K;let n=ie();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null};let s=Date.now()-e.timestamp,r=s<1e3?8-Math.log(s+1):1;if(e.canvas&&(De.canvas=e.canvas),e.error&&(De.error=e.error),!De.body||e.body.length!==De.body.length)De.body=JSON.parse(JSON.stringify(e.body));else for(let z=0;z<e.body.length;z++){let j=e.body[z].box.map((Z,oe)=>((r-1)*De.body[z].box[oe]+Z)/r),W=e.body[z].boxRaw.map((Z,oe)=>((r-1)*De.body[z].boxRaw[oe]+Z)/r),Q=e.body[z].keypoints.map((Z,oe)=>({score:Z.score,part:Z.part,position:[De.body[z].keypoints[oe]?((r-1)*(De.body[z].keypoints[oe].position[0]||0)+(Z.position[0]||0))/r:Z.position[0],De.body[z].keypoints[oe]?((r-1)*(De.body[z].keypoints[oe].position[1]||0)+(Z.position[1]||0))/r:Z.position[1],De.body[z].keypoints[oe]?((r-1)*(De.body[z].keypoints[oe].position[2]||0)+(Z.position[2]||0))/r:Z.position[2]],positionRaw:[De.body[z].keypoints[oe]?((r-1)*(De.body[z].keypoints[oe].positionRaw[0]||0)+(Z.positionRaw[0]||0))/r:Z.position[0],De.body[z].keypoints[oe]?((r-1)*(De.body[z].keypoints[oe].positionRaw[1]||0)+(Z.positionRaw[1]||0))/r:Z.position[1],De.body[z].keypoints[oe]?((r-1)*(De.body[z].keypoints[oe].positionRaw[2]||0)+(Z.positionRaw[2]||0))/r:Z.position[2]]})),te={},ae={connected:{}};((i=(o=t.body)==null?void 0:o.modelPath)==null?void 0:i.includes("efficientpose"))?ae=wb:((c=(l=t.body)==null?void 0:l.modelPath)==null?void 0:c.includes("blazepose"))?ae=hb:((d=(u=t.body)==null?void 0:u.modelPath)==null?void 0:d.includes("movenet"))&&(ae=Kb);for(let[Z,oe]of Object.entries(ae.connected)){let re=[];for(let ge=0;ge<oe.length-1;ge++){let ve=Q.find(Te=>Te.part===oe[ge]),Re=Q.find(Te=>Te.part===oe[ge+1]);ve&&Re&&re.push([ve.position,Re.position])}te[Z]=re}De.body[z]={...e.body[z],box:j,boxRaw:W,keypoints:Q,annotations:te}}if(!De.hand||e.hand.length!==De.hand.length)De.hand=JSON.parse(JSON.stringify(e.hand));else for(let z=0;z<e.hand.length;z++){let j=e.hand[z].box.map((ae,Z)=>((r-1)*De.hand[z].box[Z]+ae)/r),W=e.hand[z].boxRaw.map((ae,Z)=>((r-1)*De.hand[z].boxRaw[Z]+ae)/r);De.hand[z].keypoints.length!==e.hand[z].keypoints.length&&(De.hand[z].keypoints=e.hand[z].keypoints);let Q=e.hand[z].keypoints&&e.hand[z].keypoints.length>0?e.hand[z].keypoints.map((ae,Z)=>ae.map((oe,re)=>((r-1)*(De.hand[z].keypoints[Z][re]||1)+(oe||0))/r)):[],te={};if(Object.keys(De.hand[z].annotations).length!==Object.keys(e.hand[z].annotations).length)De.hand[z].annotations=e.hand[z].annotations,te=De.hand[z].annotations;else if(e.hand[z].annotations)for(let ae of Object.keys(e.hand[z].annotations))te[ae]=e.hand[z].annotations[ae]&&e.hand[z].annotations[ae][0]?e.hand[z].annotations[ae].map((Z,oe)=>Z.map((re,ge)=>((r-1)*De.hand[z].annotations[ae][oe][ge]+re)/r)):null;De.hand[z]={...e.hand[z],box:j,boxRaw:W,keypoints:Q,annotations:te}}if(!De.face||e.face.length!==De.face.length)De.face=JSON.parse(JSON.stringify(e.face));else for(let z=0;z<e.face.length;z++){let j=e.face[z].box.map((Q,te)=>((r-1)*De.face[z].box[te]+Q)/r),W=e.face[z].boxRaw.map((Q,te)=>((r-1)*De.face[z].boxRaw[te]+Q)/r);if(e.face[z].rotation){let Q={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};Q.matrix=(p=e.face[z].rotation)==null?void 0:p.matrix,Q.angle={roll:((r-1)*(((f=(h=De.face[z].rotation)==null?void 0:h.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[z].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((x=(y=De.face[z].rotation)==null?void 0:y.angle)==null?void 0:x.yaw)||0)+(((b=(A=e.face[z].rotation)==null?void 0:A.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((C=(w=De.face[z].rotation)==null?void 0:w.angle)==null?void 0:C.pitch)||0)+(((E=(S=e.face[z].rotation)==null?void 0:S.angle)==null?void 0:E.pitch)||0))/r},Q.gaze={bearing:((r-1)*(((P=(R=De.face[z].rotation)==null?void 0:R.gaze)==null?void 0:P.bearing)||0)+((($=(_=e.face[z].rotation)==null?void 0:_.gaze)==null?void 0:$.bearing)||0))/r,strength:((r-1)*(((O=(T=De.face[z].rotation)==null?void 0:T.gaze)==null?void 0:O.strength)||0)+(((K=(V=e.face[z].rotation)==null?void 0:V.gaze)==null?void 0:K.strength)||0))/r},De.face[z]={...e.face[z],rotation:Q,box:j,boxRaw:W}}De.face[z]={...e.face[z],box:j,boxRaw:W}}if(!De.object||e.object.length!==De.object.length)De.object=JSON.parse(JSON.stringify(e.object));else for(let z=0;z<e.object.length;z++){let j=e.object[z].box.map((Q,te)=>((r-1)*De.object[z].box[te]+Q)/r),W=e.object[z].boxRaw.map((Q,te)=>((r-1)*De.object[z].boxRaw[te]+Q)/r);De.object[z]={...e.object[z],box:j,boxRaw:W}}if(e.persons){let z=e.persons;if(!De.persons||z.length!==De.persons.length)De.persons=JSON.parse(JSON.stringify(z));else for(let j=0;j<z.length;j++)De.persons[j].box=z[j].box.map((W,Q)=>((r-1)*De.persons[j].box[Q]+W)/r)}e.gesture&&(De.gesture=e.gesture);let a=ie();return b5=pe.perfadd?b5+Math.round(a-n):Math.round(a-n),e.performance&&(De.performance={...e.performance,interpolate:b5}),De}function M0(e,t,n={order:2,multiplier:25}){let s=0;for(let r=0;r<e.length;r++){let a=!n.order||n.order===2?e[r]-t[r]:Math.abs(e[r]-t[r]);s+=!n.order||n.order===2?a*a:a**n.order}return(n.multiplier||20)*s}var $N=(e,t,n,s)=>{if(e===0)return 1;let r=t===2?Math.sqrt(e):e**(1/t),a=(1-r/100-n)/(s-n);return Math.max(Math.min(a,1),0)};function FN(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let s=M0(e,t,n);return $N(s,n.order||2,n.min||0,n.max||1)}function PN(e,t,n={order:2,multiplier:25,threshold:0,min:.2,max:.8}){if(!Array.isArray(e)||!Array.isArray(t)||e.length<64||t.length===0||e.length!==t[0].length)return{index:-1,distance:Number.POSITIVE_INFINITY,similarity:0};let s=Number.MAX_SAFE_INTEGER,r=-1;for(let o=0;o<t.length;o++){let i=M0(e,t[o],n);if(i<s&&(s=i,r=o),s<(n.threshold||0))break}let a=$N(s,n.order||2,n.min||0,n.max||1);return{index:r,distance:s,similarity:a}}function ON(e,t,n,s,r){var i,l,c,u,d,p,h,f,m,g,y,x,A,b,w,C;let a=0,o=[];for(let S of e){let E={id:a++,face:S,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let O of 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2Q==`;async function Hye(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(z0);break;case"body":case"full":n=await t(L0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function jye(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+z0;break;case"full":case"body":n="data:image/jpeg;base64,"+L0;break;default:n=null}let s;if(typeof Image!="undefined")s=new Image;else if(pe.Image)s=new pe.Image;else return;s.onload=async()=>{let r=Kn(s.naturalWidth,s.naturalHeight);if(!r)J("Warmup: Canvas not found"),t(void 0);else{let a=r.getContext("2d");a&&a.drawImage(s,0,0);let o=await e.image(r),i=await e.detect(o.tensor,e.config);t(i)}},n?s.src=n:t(void 0)})}async function qye(e){let t=r=>Buffer.from(r,"base64"),n;e.config.warmup==="face"?n=t(z0):n=t(L0);let s;if("node"in Ur){let r=(void 0).decodeJpeg(n),a=r.expandDims(0);e.tf.dispose(r),s=await e.detect(a,e.config),e.tf.dispose(a)}else e.config.debug&&J("Warmup tfjs-node not loaded");return s}async function MN(e,t){let n=ie();if(e.state="warmup",t&&(e.config=En(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none")return{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:ie(),persons:[],error:null};let s;return new Promise(async r=>{typeof createImageBitmap=="function"?s=await Hye(e):typeof Image!="undefined"||pe.Canvas!==void 0?s=await jye(e):s=await qye(e);let a=ie();e.config.debug&&J("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var Wc,jp,qp,B0,zN=class{constructor(t){fe(this,"version");fe(this,"config");fe(this,"result");fe(this,"state");fe(this,"process");fe(this,"tf");fe(this,"env");fe(this,"draw");fe(this,"models");fe(this,"events");fe(this,"faceTriangulation");fe(this,"faceUVMap");fe(this,"performance");sd(this,Wc,void 0);sd(this,jp,void 0);sd(this,qp,void 0);fe(this,"gl");fe(this,"analyze",(...t)=>{if(!nd(this,jp))return;let n=this.tf.engine().state.numTensors,s=nd(this,Wc);rd(this,Wc,n);let r=n-s;r!==0&&J(...t,r)});sd(this,B0,t=>{if(!nd(this,qp))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof et))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});fe(this,"similarity",FN);fe(this,"distance",M0);fe(this,"match",PN);fe(this,"emit",t=>{var n;this.events&&this.events.dispatchEvent&&((n=this.events)==null||n.dispatchEvent(new Event(t)))});this.env=pe,Na.wasmPath=l0["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${tv}/dist/`,Na.modelBasePath=pe.browser?"../models/":"file://models/",Na.backend=pe.browser?"humangl":"tensorflow",this.version=Xx,Object.defineProperty(this,"version",{value:Xx}),this.config=JSON.parse(JSON.stringify(Na)),Object.seal(this.config),t&&(this.config=En(this.config,t)),this.tf=Ur,this.state="idle",rd(this,Wc,0),rd(this,jp,!1),rd(this,qp,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new u5,this.draw={options:xa,canvas:(n,s)=>SN(n,s),face:(n,s,r)=>f5(n,s,r),body:(n,s,r)=>m5(n,s,r),hand:(n,s,r)=>g5(n,s,r),gesture:(n,s,r)=>h5(n,s,r),object:(n,s,r)=>y5(n,s,r),person:(n,s,r)=>kN(n,s,r),all:(n,s,r)=>IN(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=kT,this.faceUVMap=ST,this.gl=Rt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Na)),this.config.backend=t}validate(t){return f2(Na,t||this.config)}now(){return ie()}image(t,n=!0){return Nc(t,this.config,n)}async segmentation(t,n){return yN(t,n,this.config)}enhance(t){return Db(t)}compare(t,n){return k8(this.config,t,n)}async init(){await O0(this,!0),await this.tf.ready()}async load(t){this.state="load";let n=ie(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=En(this.config,t)),this.env.initial&&(this.config.debug&&J(`version: ${this.version}`),this.config.debug&&J(`tfjs version: ${this.tf.version["tfjs-core"]}`),await O0(this)||J("error: backend check failed"),await df(),this.env.browser&&(this.config.debug&&J("configuration:",this.config),this.config.debug&&J("environment:",this.env),this.config.debug&&J("tf flags:",this.tf.ENV.flags))),await AN(this),this.env.initial&&this.config.debug&&J("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await xN(this),this.emit("load"));let a=Math.trunc(ie()-n);a>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+a:a)}next(t=this.result){return DN(t,this.config)}async warmup(t){let n=ie(),s=await MN(this,t),r=ie();return this.performance.warmup=Math.trunc(r-n),s}async profile(t,n){let s=await this.tf.profile(()=>this.detect(t,n)),r={};for(let i of s.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs;let a=[];Object.entries(r).forEach(i=>a.push({name:i[0],ms:i[1]})),a.sort((i,l)=>l.ms-i.ms),a.length=20;let o={};for(let i of a)o[i.name]=i.ms;return o}async detect(t,n){return this.state="detect",new Promise(async s=>{var g,y,x,A,b,w,C,S,E,R,P,_,$,T,O,V,K,z,j,W,Q,te;this.state="config";let r;this.config=En(this.config,n),this.state="check";let a=nd(this,B0).call(this,t);a&&(J(a,t),this.emit("error"),s({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:ie(),persons:[],error:a}));let o=ie();await O0(this),await this.load(),r=ie(),this.state="image";let i=await Nc(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ie()-r):Math.trunc(ie()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&J("could not convert input to tensor"),this.emit("error"),s({face:[],body:[],hand:[],gesture:[],object:[],performance:this.performance,timestamp:ie(),persons:[],error:"could not convert input to tensor"});return}this.emit("image"),r=ie(),this.config.skipAllowed=await w8(this.config,i.tensor),this.performance.totalFrames||(this.performance.totalFrames=0),this.performance.cachedFrames||(this.performance.cachedFrames=0),this.performance.totalFrames++,this.config.skipAllowed&&this.performance.cachedFrames++,this.performance.cacheCheck=this.env.perfadd?(this.performance.cacheCheck||0)+Math.trunc(ie()-r):Math.trunc(ie()-r),this.analyze("Check Changed:");let l=[],c=[],u=[],d=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?x5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=ie(),l=this.config.face.enabled?await x5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?En(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?(((g=this.config.body.modelPath)==null?void 0:g.includes("posenet"))?c=this.config.body.enabled?o5(i.tensor,p):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("blazepose"))?c=this.config.body.enabled?gb(i.tensor,p):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?c=this.config.body.enabled?Sb(i.tensor,p):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("movenet"))&&(c=this.config.body.enabled?Jb(i.tensor,p):[]),this.performance.body&&delete this.performance.body):(r=ie(),((b=this.config.body.modelPath)==null?void 0:b.includes("posenet"))?c=this.config.body.enabled?await o5(i.tensor,p):[]:((w=this.config.body.modelPath)==null?void 0:w.includes("blazepose"))?c=this.config.body.enabled?await gb(i.tensor,p):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("efficientpose"))?c=this.config.body.enabled?await Sb(i.tensor,p):[]:((S=this.config.body.modelPath)==null?void 0:S.includes("movenet"))&&(c=this.config.body.enabled?await Jb(i.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?En(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?(((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)==null?void 0:R.includes("handdetect"))?u=this.config.hand.enabled?Lb(i.tensor,h):[]:((_=(P=this.config.hand.detector)==null?void 0:P.modelPath)==null?void 0:_.includes("handtrack"))&&(u=this.config.hand.enabled?Ub(i.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ie(),((T=($=this.config.hand.detector)==null?void 0:$.modelPath)==null?void 0:T.includes("handdetect"))?u=this.config.hand.enabled?await Lb(i.tensor,h):[]:((V=(O=this.config.hand.detector)==null?void 0:O.modelPath)==null?void 0:V.includes("handtrack"))&&(u=this.config.hand.enabled?await Ub(i.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((K=this.config.object.modelPath)==null?void 0:K.includes("nanodet"))?d=this.config.object.enabled?e5(i.tensor,this.config):[]:((z=this.config.object.modelPath)==null?void 0:z.includes("centernet"))&&(d=this.config.object.enabled?xb(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ie(),((j=this.config.object.modelPath)==null?void 0:j.includes("nanodet"))?d=this.config.object.enabled?await e5(i.tensor,this.config):[]:((W=this.config.object.modelPath)==null?void 0:W.includes("centernet"))&&(d=this.config.object.enabled?await xb(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.analyze("End Object:"),this.state="detect:await",this.config.async&&([l,c,u,d]=await Promise.all([l,c,u,d])),this.state="detect:gesture";let f=[];this.config.gesture.enabled&&(r=ie(),f=[...EN(l),...NN(c),..._N(u),...RN(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ie()-o):Math.trunc(ie()-o);let m=((te=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:te.shape)||[];this.result={face:l,body:c,hand:u,gesture:f,object:d,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),error:null,get persons(){return ON(l,c,u,f,m)}},ne(i.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};Wc=new WeakMap,jp=new WeakMap,qp=new WeakMap,B0=new WeakMap;return Xye;})();
|
|
/**
|
|
* @license
|
|
* Copyright 2017 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google Inc. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
|
|
* limitations under the License.
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* =============================================================================
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|
*/
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/**
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* @license
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* Copyright 2020 Google LLC
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*
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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* =============================================================================
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*/
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|
/**
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* @license
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* Copyright 2020 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use backend file except in compliance with the License.
|
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* You may obtain a copy of the License at
|
|
*
|
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* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
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* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the License);
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an AS IS BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* https://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the License);
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an AS IS BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* Human main module
|
|
* @default Human Library
|
|
* @summary <https://github.com/vladmandic/human>
|
|
* @author <https://github.com/vladmandic>
|
|
* @copyright <https://github.com/vladmandic>
|
|
* @license MIT
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/** @license See the LICENSE file. */
|