mirror of https://github.com/vladmandic/human
8040 lines
1.6 MiB
8040 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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with dtype ${a.dtype}. `)}),n.length===1)return Ln(n[0]);let s=n,r={axis:t};return V.runKernel(hi,s,r)}var St=G({concat_:pD});function hD(e){let n={x:F(e,"x","sigmoid","float32")};return V.runKernel(po,n)}var os=G({sigmoid_:hD});function fD(e,t,n){let s=F(e,"x","slice","string_or_numeric");if(s.rank===0)throw new Error("Slicing scalar is not possible");let r={x:s},a={begin:t,size:n};return V.runKernel(Wi,r,a)}var Pe=G({slice_:fD});function mD(e){let n={x:F(e,"x","tanh","float32")};return V.runKernel(Ao,n)}var Lu=G({tanh_:mD});function gD(e,t,n,s,r,a){let o=F(e,"forgetBias","basicLSTMCell"),i=F(t,"lstmKernel","basicLSTMCell"),l=F(n,"lstmBias","basicLSTMCell"),c=F(s,"data","basicLSTMCell"),u=F(r,"c","basicLSTMCell"),d=F(a,"h","basicLSTMCell"),p=St([c,d],1),h=je(p,i),f=ue(h,l),m=f.shape[0],g=f.shape[1]/4,y=[m,g],x=Pe(f,[0,0],y),A=Pe(f,[0,g],y),b=Pe(f,[0,g*2],y),w=Pe(f,[0,g*3],y),k=ue(L(os(x),Lu(A)),L(u,os(ue(o,b)))),I=L(Lu(k),os(w));return[k,I]}var yD=G({basicLSTMCell_:gD});function 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${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}.`),Bu(o,i,l,u,c,a)}var vv=G({batchNorm2d_:vD});function wD(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}.`),Bu(o,i,l,u,c,a)}var wv=G({batchNorm3d_:wD});function kD(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}.`),Bu(o,i,l,u,c,a)}var kv=G({batchNorm4d_:kD});function SD(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: ${r.shape}.`);let a={x:s,weights:r},o={size:n};return V.runKernel($h,a,o)}var d1=G({bincount_:SD});function ID(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=V.runKernel(Pa,p,h);return u?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var _o=G({conv2d_:$D});function FD(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 ${l.rank}.`),Bn("conv1d",s,o),M(c.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${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 ${r} but only NWC is currently supported.`);let d=H(l,[1,l.shape[0],l.shape[1],l.shape[2]]),p=H(c,[c.shape[0],1,c.shape[1],c.shape[2]]),g=_o(p,d,[1,n],s,"NHWC",[1,a],o);return u?H(g,[g.shape[2],g.shape[3]]):H(g,[g.shape[0],g.shape[2],g.shape[3]])}var p1=G({conv1d_:FD});function PD(e,t,n,s,r,a="NHWC",o){M(e.length===t.rank,()=>`Length of inShape (${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]}.`),Bn("conv2dDerInput",r,o);let p={dy:l,filter:n},h={strides:s,pad:r,dataFormat:a,dimRoundingMode:o,inputShape:i},f=V.runKernel(Oa,p,h);return c?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var h1=G({conv2DBackpropInput_:PD});function OD(e,t,n,s,r,a){let o=F(e,"x","conv2dTranspose"),i=F(t,"filter","conv2dTranspose");return h1(n,o,i,s,r,"NHWC",a)}var f1=G({conv2dTranspose_:OD});function MD(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 ${r} but only NDHWC is currently supported.`);let u={x:l,filter:i},d={strides:n,pad:s,dataFormat:r,dilations:a},p=V.runKernel(dd,u,d);return c?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var m1=G({conv3d_:MD});function zD(e,t,n,s,r){M(e.length===t.rank,()=>`Length of inShape (${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=V.runKernel(Mh,u,d);return i?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Ev=G({conv3DBackpropInput_:zD});function LD(e,t,n,s,r){let a=F(e,"x","conv3dTranspose"),o=F(t,"filter","conv3dTranspose");return Ev(n,a,o,s,r)}var Rv=G({conv3dTranspose_:LD});function BD(e){let n={x:F(e,"x","cos","float32")};return V.runKernel(Ma,n)}var vf=G({cos_:BD});function WD(e){let n={x:F(e,"x","cosh","float32")};return V.runKernel(za,n)}var g1=G({cosh_:WD});function VD(e,t=0,n=!1,s=!1){let a={x:F(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:s};return V.runKernel(fi,a,o)}var y1=G({cumsum_:VD});function UD(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 V.runKernel(zh,o,i)}var _v=G({denseBincount_:UD});function GD(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 V.runKernel(gi,i,l)}var Dv=G({depthToSpace_:GD});function HD(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]}.`),Bn("depthwiseConv2d",s,o);let d={x:c,filter:l},p={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},h=V.runKernel(La,d,p);return u?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Bd=G({depthwiseConv2d_:HD});function jD(e){let n={x:F(e,"x","diag")};return V.runKernel(Wh,n)}var qD=G({diag_:jD});function XD(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=V.runKernel(pd,u,d);return c?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var $v=G({dilation2d_:XD});function KD(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 V.runKernel(yi,r)}var Ps=G({equal_:KD});function ZD(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=Ld(a,o),l=Ld(s,o),c=Ld(r,o),u={condition:i,t:l,e:c};return V.runKernel(Bi,u)}var Wn=G({where_:ZD});function YD(e){let n={x:F(e,"x","zerosLike")};return V.runKernel(Zi,n)}var nt=G({zerosLike_:YD});function JD(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 Wn(o,a,r)}var Fv=G({divNoNan_:JD});function QD(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 ${a}.`),n.rank===1&&s.rank===1){let o=H(n,[1,-1]),i=H(s,[-1,1]),l=je(o,i);return H(l,[])}else if(n.rank===1&&s.rank===2){let o=H(n,[1,-1]),i=H(s,[s.shape[0],s.shape[1]]),l=je(o,i);return H(l,[l.size])}else if(n.rank===2&&s.rank===1){let o=H(s,[-1,1]),i=je(n,o);return H(i,[i.size])}else{let o=H(s,[s.shape[0],s.shape[1]]);return je(n,o)}}var e$=G({dot_:QD});function t$(e,...t){let n=t.map((r,a)=>F(r,`tensors${a}`,"einsum")),s={equation:e};return V.runKernel(hd,n,s)}var Pv=G({einsum_:t$});function n$(e){let n={x:F(e,"x","elu","float32")};return V.runKernel(Wa,n)}var Wd=G({elu_:n$});function s$(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=ge(t,"float32"));let n={x:t};return V.runKernel(gu,n)}var Ov=G({erf_:s$});function r$(e){let n={x:F(e,"x","exp")};return V.runKernel(Va,n)}var Os=G({exp_:r$});function a$(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 V.runKernel(Ai,s,r)}var Zt=G({expandDims_:a$});function o$(e){let n={x:F(e,"x","expm1")};return V.runKernel(xi,n)}var Mv=G({expm1_:o$});function i$(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 ${t}.`);let s={x:n},r={reps:t};return V.runKernel(Kr,s,r)}var Qs=G({tile_:i$});function l$(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 Qs(Zt(o,0),[n[0],1,1]);if(n.length===2)return Qs(Zt(Zt(o,0),0),[n[0],n[1],1,1]);if(n.length===3)return Qs(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 ${n.length}D.`)}var A1=G({eye_:l$});function Vu(e,t,n){let s={shape:e,value:t,dtype:n};return V.runKernel(yu,{},s)}function u$(e){let n={x:F(e,"x","floor","float32")};return V.runKernel(Ua,n)}var Vd=G({floor_:u$});function c$(e,t,n=0,s=0){let r=F(e,"x","gather"),a=F(t,"indices","gather","int32"),o={x:r,indices:a},i={axis:n,batchDims:s};return V.runKernel(vi,o,i)}var Uu=G({gather_:c$});function d$(e,t){let n=F(e,"a","greater","string_or_numeric"),s=F(t,"b","greater","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return V.runKernel(ki,r)}var xs=G({greater_:d$});function p$(e,t){let n=F(e,"a","greaterEqual","string_or_numeric"),s=F(t,"b","greaterEqual","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return V.runKernel(ja,r)}var ll=G({greaterEqual_:p$});function h$(e){let n={input:F(e,"input","imag")};return V.runKernel(fd,n)}var wf=G({imag_:h$});function f$(e){let n={x:F(e,"x","isFinite")};return V.runKernel(Au,n)}var m$=G({isFinite_:f$});function g$(e){let n={x:F(e,"x","isInf")};return V.runKernel(xu,n)}var y$=G({isInf_:g$});function A$(e){let n={x:F(e,"x","isNaN")};return V.runKernel(bu,n)}var zv=G({isNaN_:A$});function x$(e,t=.2){let s={x:F(e,"x","leakyRelu")},r={alpha:t};return V.runKernel(Si,s,r)}var kf=G({leakyRelu_:x$});function b$(e,t){let n=F(e,"a","less","string_or_numeric"),s=F(t,"b","less","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return V.runKernel(Ii,r)}var x1=G({less_:b$});function v$(e,t){let n=F(e,"a","lessEqual","string_or_numeric"),s=F(t,"b","lessEqual","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return V.runKernel(Ci,r)}var ul=G({lessEqual_:v$});function Lv(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 V.runKernel(qh,{},s)}function w$(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(su(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let o=a,i=!1;a.rank===3&&(i=!0,o=H(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let l={x:o},c={depthRadius:t,bias:n,alpha:s,beta:r},u=V.runKernel(gd,l,c);return i?H(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Bv=G({localResponseNormalization_:w$});function k$(e){let n={x:F(e,"x","log","float32")};return V.runKernel(Xa,n)}var Ms=G({log_:k$});function S$(e){let n={x:F(e,"x","log1p")};return V.runKernel(vu,n)}var Sf=G({log1p_:S$});function I$(e){return M(Na(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let s=F(t,"x","tf.grad","string_or_numeric"),r=n!=null?F(n,"dy","tf.grad"):null;return V.tidy(()=>{let{value:a,grads:o}=V.gradients(()=>e(s),[s],r);return r!=null&&On(a.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),If(o),o[0]})}}function C$(e){return M(Na(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=Pd(t,"args","tf.grads","string_or_numeric"),r=n!=null?F(n,"dy","tf.grads"):null;return V.tidy(()=>{let{value:a,grads:o}=V.gradients(()=>e(...s),s,r);return r!=null&&On(a.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),If(o),o})}}function T$(e){return M(Na(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{M(t instanceof et,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),M(n==null||n instanceof et,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:s,value:r}=V.gradients(()=>e(t),[t],n);return If(s),{grad:s[0],value:r}}}function N$(e){return M(Na(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=V.gradients(()=>e(...t),t,n);return n!=null&&On(s.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),If(s.grads),s}}function Wv(e,t){M(Na(e),()=>"The f passed in variableGrads(f) must be a function"),M(t==null||Array.isArray(t)&&t.every(c=>c instanceof $d),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let c in V.registeredVariables)t.push(V.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 ${r} variables is trainable.`);let a=!0,{value:o,grads:i}=V.gradients(e,t,null,a);M(i.some(c=>c!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),M(o.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${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 V.customGrad(e)}function If(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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OO=G({transform_:PO});function MO(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(qu(0,a,1,"int32"),[-1,1]),l=qu(0,o,1,"int32"),c=he(i,l),u=fr(ul(c,Ce(+t,"int32")),ll(c,Ce(-n,"int32"))),d=jt([a,o],s.dtype);return H(an(is(H(s,[-1,a,o])).map(p=>Wn(u,p,d))),r)}var zO=G({bandPart_:MO});function LO(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: 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n=e.shape[0],s=e.shape[1],r=A1(n),a=Ln(e),o=mr([[1]],[1,1]),i=Ln(o),l=n>=s?s:n;for(let c=0;c<l;++c){let u=a,d=i,p=r;[i,a,r]=V.tidy(()=>{let h=Pe(a,[c,c],[n-c,1]),f=L1(h),m=Pe(a,[c,c],[1,1]),g=Wn(xs(m,0),mr([[-1]]),mr([[1]])),y=he(m,L(g,f)),x=de(h,y);x.shape[0]===1?i=Ln(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),k=tt(i);if(c===0)a=he(b,je(w,je(k,b)));else{let R=he(b,je(w,je(k,b)));a=St([Pe(a,[0,0],[c,s]),R],0)}let I=tt(w),E=Pe(r,[0,c],[n,r.shape[1]-c]);if(c===0)r=he(E,je(je(E,i),I));else{let R=he(E,je(je(E,i),I));r=St([Pe(r,[0,0],[n,c]),R],1)}return[i,a,r]}),te([u,d,p])}return!t&&n>s&&(r=Pe(r,[0,0],[n,s]),a=Pe(a,[0,0],[s,s])),[r,a]})}var VO=G({qr_:WO}),Vn;(function(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"})(Vn||(Vn={}));function UO(e,t,n=Vn.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===Vn.NONE)return a;if(n===Vn.SUM)return we(a);if(n===Vn.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===Vn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return de(we(a),Ce(s.size));{let o=L(r,bs(s.shape)),i=ge(we(Hu(o,Ce(0))),"float32");return de(we(a),i)}}throw Error(`Unknown reduction: ${n}`)}var Qr=G({computeWeightedLoss_:UO});function GO(e,t,n,s=Vn.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","absoluteDifference"),a=F(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=F(n,"weights","absoluteDifference")),On(r.shape,a.shape,"Error in absoluteDifference: ");let i=rn(he(r,a));return Qr(i,o,s)}var HO=G({absoluteDifference_:GO});function jO(e,t,n,s,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","cosineDistance"),o=F(t,"predictions","cosineDistance"),i=null;s!=null&&(i=F(s,"weights","cosineDistance")),On(a.shape,o.shape,"Error in cosineDistance: ");let l=Ce(1),c=he(l,we(L(a,o),n,!0));return Qr(c,i,r)}var qO=G({cosineDistance_:jO});function XO(e,t,n,s=Vn.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","hingeLoss"),a=F(t,"predictions","hingeLoss"),o=null;n!=null&&(o=F(n,"weights","hingeLoss")),On(r.shape,a.shape,"Error in hingeLoss: ");let i=Ce(1);r=he(L(Ce(2),r),i);let l=$r(he(i,L(r,a)));return Qr(l,o,s)}var KO=G({hingeLoss_:XO});function ZO(e,t,n,s=1,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","huberLoss"),o=F(t,"predictions","huberLoss"),i=null;n!=null&&(i=F(n,"weights","huberLoss")),On(a.shape,o.shape,"Error in huberLoss: ");let l=Ce(s),c=rn(he(o,a)),u=Ud(c,l),d=he(c,u),p=ue(L(Ce(.5),xt(u)),L(l,d));return Qr(p,i,r)}var YO=G({huberLoss_:ZO});function JO(e,t,n,s=1e-7,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","logLoss"),o=F(t,"predictions","logLoss"),i=null;n!=null&&(i=F(n,"weights","logLoss")),On(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 Qr(p,i,r)}var QO=G({logLoss_:JO});function eM(e,t,n,s=Vn.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","meanSquaredError"),a=F(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=F(n,"weights","meanSquaredError")),On(r.shape,a.shape,"Error in meanSquaredError: ");let i=O1(r,a);return Qr(i,o,s)}var tM=G({meanSquaredError_:eM});function nM(e,t){let n=F(e,"labels","sigmoidCrossEntropyWithLogits"),s=F(t,"logits","sigmoidCrossEntropyWithLogits");On(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=$r(s),a=L(s,n),o=Sf(Os(zt(rn(s))));return ue(he(r,a),o)}function sM(e,t,n,s=0,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"multiClassLabels","sigmoidCrossEntropy"),o=F(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=F(n,"weights","sigmoidCrossEntropy")),On(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let c=Ce(s),u=Ce(1),d=Ce(.5);a=ue(L(a,he(u,c)),L(d,c))}let l=nM(a,o);return Qr(l,i,r)}var rM=G({sigmoidCrossEntropy_:sM});function aM(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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${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 ${i.shape}`);let l={indices:r,values:a,denseShape:o,defaultValue:i},c=V.runKernel(xd,l);return{outputIndices:c[0],outputValues:c[1],emptyRowIndicator:c[2],reverseIndexMap:c[3]}}var uM=G({sparseFillEmptyRows_:lM});function cM(e,t,n){let s=F(e,"inputIndices","sparseReshape","int32"),r=F(t,"inputShape","sparseReshape","int32"),a=F(n,"newShape","sparseReshape","int32");if(s.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
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${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=V.runKernel(_u,o);return{outputIndices:i[0],outputShape:i[1]}}var dM=G({sparseReshape_:cM});function pM(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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|
${a.shape}`);let o={data:s,indices:r,segmentIds:a};return V.runKernel(bd,o)}var hM=G({sparseSegmentMean_:pM});function fM(e,t,n){let s=F(e,"data","sparseSegmentSum"),r=F(t,"indices","sparseSegmentSum","int32"),a=F(n,"segmentIds","sparseSegmentSum","int32");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${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 V.runKernel(vd,o)}var mM=G({sparseSegmentSum_:fM});function gM(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=V.runKernel(kd,d,u);return{nGrams:p[0],nGramsSplits:p[1]}}var yM=G({stringNGrams_:gM});function AM(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 ${r.shape}`);let a={skipEmpty:n},o={input:s,delimiter:r},i=V.runKernel(tf,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var xM=G({stringSplit_:AM});function bM(e,t){let n=F(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return V.runKernel(nf,r,s)}var vM=G({stringToHashBucketFast_:bM}),wM={fft:Df,ifft:jd,rfft:$f,irfft:P1},kM={hammingWindow:YP,hannWindow:hw,frame:fw,stft:tO},Se={flipLeftRight:aO,grayscaleToRGB:iO,resizeNearestNeighbor:_O,resizeBilinear:EO,rotateWithOffset:uO,cropAndResize:sO,nonMaxSuppression:dO,nonMaxSuppressionAsync:xO,nonMaxSuppressionWithScore:vO,nonMaxSuppressionWithScoreAsync:kO,nonMaxSuppressionPadded:IO,nonMaxSuppressionPaddedAsync:TO,threshold:FO,transform:OO},bw={bandPart:zO,gramSchmidt:BO,qr:VO},SM={absoluteDifference:HO,computeWeightedLoss:Qr,cosineDistance:qO,hingeLoss:KO,huberLoss:YO,logLoss:QO,meanSquaredError:tM,sigmoidCrossEntropy:rM,softmaxCrossEntropy:iM},Xd={sparseFillEmptyRows:uM,sparseReshape:dM,sparseSegmentMean:hM,sparseSegmentSum:mM},Lf={stringNGrams:yM,stringSplit:xM,stringToHashBucketFast:vM},ea=class extends sv{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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ea{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=V.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:K(()=>Vu(r.shape,this.initialAccumulatorValue).variable(i))}}let a=Array.isArray(e)?e[s].tensor:e[n];if(a==null)return;let o=this.accumulatedGrads[s].variable;K(()=>{let i=ue(o,xt(a));o.assign(i);let l=ue(L(de(a,Dn(ue(i,V.backend.epsilon()))),-this.learningRate),r);r.assign(l)})}),this.incrementIterations()}dispose(){this.accumulatedGrads!=null&&te(this.accumulatedGrads.map(e=>e.variable))}async getWeights(){return[await this.saveIterations()].concat(this.accumulatedGrads.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulatedGrads=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}}static fromConfig(e,t){return new e(t.learningRate,t.initialAccumulatorValue)}};Wf.className="Adagrad";Eo(Wf);var Vf=class extends ea{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],K(()=>{this.accBeta1=Ce(t).variable(),this.accBeta2=Ce(n).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);K(()=>{let n=he(1,this.accBeta1),s=he(1,this.accBeta2);t.forEach((r,a)=>{let o=V.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:K(()=>nt(o).variable(i))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${r}/v`,variable:K(()=>nt(o).variable(i))});let l=Array.isArray(e)?e[a].tensor:e[r];if(l==null)return;let c=this.accumulatedFirstMoment[a].variable,u=this.accumulatedSecondMoment[a].variable,d=ue(L(c,this.beta1),L(l,1-this.beta1)),p=ue(L(u,this.beta2),L(xt(l),1-this.beta2)),h=de(d,n),f=de(p,s);c.assign(d),u.assign(p);let m=ue(L(de(h,ue(Dn(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&te(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),K(()=>{this.accBeta1.assign($o(this.beta1,this.iterations_+1)),this.accBeta2.assign($o(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};Vf.className="Adam";Eo(Vf);var Uf=class extends ea{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],K(()=>{this.iteration=Ce(0).variable(),this.accBeta1=Ce(t).variable()}),s==null&&(this.epsilon=V.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);K(()=>{let n=he(1,this.accBeta1),s=de(-this.learningRate,ue(L(this.iteration,this.decay),1));t.forEach((r,a)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Uf.className="Adamax";Eo(Uf);var Kd=class extends ea{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=Array.isArray(e)?e[s].tensor:e[n];if(r==null)return;let a=V.registeredVariables[n];K(()=>{let o=ue(L(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=yn(Ce(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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r=V.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:K(()=>nt(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:K(()=>nt(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:K(()=>nt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedMeanSquares[s].variable,l=this.accumulatedMoments[s].variable;K(()=>{let c=ue(L(i,this.decay),L(xt(o),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[s].variable,d=ue(L(u,this.decay),L(o,1-this.decay)),p=de(L(o,this.learningRate),Dn(he(c,ue(xt(d),this.epsilon)))),h=ue(L(l,this.momentum),p);i.assign(c),u.assign(d),l.assign(h);let f=he(r,h);r.assign(f)}else{let u=ue(L(i,this.decay),L(xt(o),1-this.decay)),d=ue(L(l,this.momentum),de(L(o,this.learningRate),Dn(ue(u,this.epsilon))));i.assign(u),l.assign(d);let p=he(r,d);r.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&te(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&te(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&te(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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indices.shape[0] = ${e}`}function rz(e,t){return`indices(${e}, 0) is invalid: ${t} < 0`}function az(e,t,n){return`indices(${e}, 0) is invalid: ${t} >= ${n}`}function oz(e,t){return`only one output dimension may be -1, not both ${e} and ${t}`}function iz(e,t){return`size ${e} must be non-negative, not ${t}`}function lz(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function uz(e,t){let n=It(e),s=It(t);return`Input to reshape is a SparseTensor with ${n}
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this.assertNotDisposed(),cW(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}};function cW(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function hy(e){return e.map(t=>t.read())}function fy(e){e.forEach(t=>{t[0].write(t[1])})}var Jt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},br=class{constructor(e,t,n,s,r,a,o){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=r,this.outputTensorIndex=o,this.id=ek(),a!=null&&(this.originalName=Hw(a),this.name=jw(this.originalName)),this.rank=t.length}},dW=0,im=class{constructor(e,t){this.callArgs=t,this.id=dW++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},pW=0,st=class extends ce.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=pW++,this.activityRegularizer=null,this.inputSpec=null,this.supportsMasking=!1,this._trainableWeights=[],this._nonTrainableWeights=[],this._losses=[],this._updates=[],this._built=!1,this.inboundNodes=[],this.outboundNodes=[];let t=e.name;if(!t){let n=this.getClassName();t=na(n)+"_"+rm(n)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let n;if(e.batchInputShape!=null)n=e.batchInputShape;else if(e.inputShape!=null){let r=null;e.batchSize!=null&&(r=e.batchSize),n=[r].concat(e.inputShape)}this.batchInputShape=n;let s=e.dtype;s==null&&(s=e.inputDType),s==null&&(s="float32"),this.dtype=s}e.weights!=null?this.initialWeights=e.weights:this.initialWeights=null,this._refCount=null,this.fastWeightInitDuringBuild=!1}static nodeKey(e,t){return e.name+"_ib-"+t.toString()}getNodeAtIndex(e,t){if(this.inboundNodes.length===0)throw new yr(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new q(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return ls(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return ls(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new ta(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. 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Input received: ${e}`);for(let n=0;n<e.length;n++){let s=e[n],r=t[n];if(r==null)continue;let a=s.rank;if(r.ndim!=null&&a!==r.ndim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected max_ndim=${r.maxNDim}, found ndim=${a}`);if(r.minNDim!=null&&a<r.minNDim)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected min_ndim=${r.minNDim}, found ndim=${a}.`);if(r.dtype!=null&&s.dtype!==r.dtype)throw new q(`Input ${n} is incompatible with layer ${this.name} : expected dtype=${r.dtype}, found dtype=${s.dtype}.`);if(r.axes){let o=s.shape;for(let i in r.axes){let l=Number(i),c=r.axes[i],u=l>=0?o[l]:o[o.length+l];if(c!=null&&[c,null].indexOf(u)===-1)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected axis ${l} of input shape to have value ${c} but got shape ${o}.`)}}if(r.shape!=null)for(let o=0;o<r.shape.length;++o){let i=r.shape[o],l=s.shape[o];if(i!=null&&l!=null&&i!==l)throw new q(`Input ${n} is incompatible with layer ${this.name}: expected shape=${r.shape}, found shape=${s.shape}.`)}}}call(e,t){return e}invokeCallHook(e,t){this._callHook!=null&&this._callHook(e,t)}setCallHook(e){this._callHook=e}clearCallHook(){this._callHook=null}apply(e,t){t=t||{},this.assertNotDisposed();let n=Tt(e),s=!0;for(let a of n)if(!(a instanceof br)){s=!1;break}let r=!0;for(let a of n)if(a instanceof br){r=!1;break}if(s===r)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return yl(this.name,()=>{if(!this.built){this.assertInputCompatibility(e);let a=[];for(let o of Tt(e))a.push(o.shape);this.build(ls(a)),this.built=!0,this.initialWeights&&this.setWeights(this.initialWeights),this._refCount===null&&r&&(this._refCount=1)}if(this.assertInputCompatibility(e),r){let a=this.call(e,t),o=Tt(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=ls(i),this.activityRegularizer!=null)throw new Le("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return a}else{let a=hW(e),o=this.computeOutputShape(a),i,l=fW(e);if(this.warnOnIncompatibleInputShape(Array.isArray(e)?a[0]:a),o!=null&&o.length>0&&Array.isArray(o[0])?i=o.map((c,u)=>new br(l,c,this,Tt(e),t,this.name,u)):i=new br(l,o,this,Tt(e),t,this.name),this.addInboundNode(e,i,null,null,a,o,t),this._refCount++,this.activityRegularizer!=null)throw new Le("Layer invocation in the presence of activity regularizer(s) is not supported yet.");return i}})}warnOnIncompatibleInputShape(e){if(this.batchInputShape!=null)if(e.length!==this.batchInputShape.length)console.warn(`The rank of the input tensor provided (shape: ${JSON.stringify(e)}) does not match that of the batchInputShape (${JSON.stringify(this.batchInputShape)}) of the layer ${this.name}`);else{let t=!1;this.batchInputShape.forEach((n,s)=>{n!=null&&e[s]!=null&&e[s]!==n&&(t=!0)}),t&&console.warn(`The shape of the input tensor (${JSON.stringify(e)}) does not match the expectation of layer ${this.name}: ${JSON.stringify(this.batchInputShape)}`)}}get outputShape(){if(this.inboundNodes==null||this.inboundNodes.length===0)throw new ta(`The layer ${this.name} has never been called and thus has no defined output shape.`);let e=[];for(let t of this.inboundNodes){let n=JSON.stringify(t.outputShapes);e.indexOf(n)===-1&&e.push(n)}if(e.length===1){let t=this.inboundNodes[0].outputShapes;return Array.isArray(t)&&Array.isArray(t[0])&&t.length===1?t[0]:t}else throw new ta(`The layer ${this.name} has multiple inbound nodes with different output shapes. Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new yr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return om(this.weights)}build(e){this.built=!0}getWeights(e=!1){return hy(e?this.trainableWeights:this.weights)}setWeights(e){K(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. Provided weights: ${e}...`);if(t.length===0)return;let n=[],s=hy(t);for(let r=0;r<s.length;++r){let a=s[r],o=t[r],i=e[r];if(!v.arraysEqual(a.shape,i.shape))throw new q(`Layer weight shape ${a.shape} not compatible with provided weight shape ${i.shape}`);n.push([o,i])}fy(n)})}addWeight(e,t,n,s,r,a,o,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new q(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=i!=null?i():$t("zeros"));let l=s.apply(t,n),c=new nk(l,n,e,a,o);return l.dispose(),r!=null&&this.addLoss(()=>r.apply(c.read())),a==null&&(a=!0),a?this._trainableWeights.push(c):this._nonTrainableWeights.push(c),c}setFastWeightInitDuringBuild(e){this.fastWeightInitDuringBuild=e}addLoss(e){e==null||Array.isArray(e)&&e.length===0||(e=Tt(e),this._losses!==void 0&&this._losses!==null&&this.losses.push(...e))}computeOutputShape(e){return e}computeMask(e,t){if(!this.supportsMasking){if(t!=null)if(Array.isArray(t))t.forEach(n=>{if(n!=null)throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`)});else throw new TypeError(`Layer ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,o=null){let i=Tt(e);t=Tt(t),n=Tt(n),s=Tt(s),r=am(r),a=am(a);let l=[],c=[],u=[];for(let d of i)l.push(d.sourceLayer),c.push(d.nodeIndex),u.push(d.tensorIndex);new im({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},o);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${this.name}' is already disposed.`)}dispose(){if(!this.built)throw new Error(`Cannot dispose Layer ${this.name} because it has not been built yet.`);if(this._refCount===null)throw new Error(`Cannot dispose Layer ${this.name} because it has not been used yet.`);this.assertNotDisposed();let e=0;return--this._refCount==0&&(e=this.disposeWeights()),{refCountAfterDispose:this._refCount,numDisposedVariables:e}}};function hW(e){e=Tt(e);let t=[];for(let n of e)t.push(n.shape);return ls(t)}function fW(e){return"float32"}function sk(e,t,n){if((t==null||n!=null&&n>0)&&(t=e.sourceLayer,n=e.nodeIndex),t.inboundNodes.length===0)return[e];{let s=t.inboundNodes[n];if(s.inboundLayers.length===0)return s.inputTensors;{let r=[];for(let a=0;a<s.inboundLayers.length;a++){let o=s.inputTensors[a],i=s.inboundLayers[a],l=s.nodeIndices[a],c=sk(o,i,l);for(let u of c)r.indexOf(u)===-1&&r.push(u)}return r}}}var Ju=class extends st{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:rm("input").toString()});if(e.batchSize==null&&(e.batchSize=null),e.sparse==null&&(e.sparse=!1),this.trainable=!1,this.built=!0,this.sparse=e.sparse,e.inputShape!=null&&e.batchInputShape!=null)throw new q("Only provide the inputShape OR batchInputShape argument to inputLayer, not both at the same time.");let t=e.batchInputShape;if(t==null){if(e.inputShape==null)throw new q("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new q("Cannot specify batchSize if batchInputShape is specified when creating an InputLayer.");let n=e.dtype||"float32";this.batchInputShape=t,this.dtype=n,this.inputSpec=[{shape:t}];let s=new br(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new im({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new q(`Cannot pass any input to an InputLayer's apply() method. 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st{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let y=this.getClassName().toLowerCase();this.name=rm(y)}if(this.supportsMasking=!1,this.trainable_=!0,Array.isArray(e.inputs)?this.inputs=e.inputs.slice():this.inputs=[e.inputs],Array.isArray(e.outputs)?this.outputs=e.outputs.slice():this.outputs=[e.outputs],Oo(this.inputs).length!==this.inputs.length)throw new q(`The list of inputs passed to the model is redundant. 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Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let x=y.sourceLayer,A=y.nodeIndex,b=y.tensorIndex;this.outputLayers.push(x),this.outputLayersNodeIndices.push(A),this.outputLayersTensorIndices.push(b)}for(let y of this.inputs){let x=y.sourceLayer,A=y.nodeIndex,b=y.tensorIndex;Fr(A===0,"input layer has >1 nodes"),Fr(b===0,"input layer has >1 tensors"),this.inputLayers.push(x),this.inputLayersNodeIndices.push(A),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let x=this.inputLayers[y];if(!(x instanceof Ju))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${x.getClassName()}.`);this.inputNames.push(x.name),this.feedInputShapes.push(x.batchInputShape),this.feedInputNames.push(x.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},s={},r={},a={},o=[],i=(y,x,A,b,w,k)=>{(b==null||w==null||k==null)&&(b=y.sourceLayer,w=y.nodeIndex,k=y.tensorIndex);let I=b.inboundNodes[w];if(A.indexOf(I)!==-1)throw new yr(`The tensor ${y.name} at layer "${b.name}" is part of a cycle.`);if(x.indexOf(I)!==-1)return;this.containerNodes.add(Mr.nodeKey(b,w)),b.id in a||(a[b.id]=Object.keys(a).length),A.indexOf(I)===-1&&A.push(I);let E=I.inboundLayers.length;for(let R=0;R<E;R++){let P=I.inputTensors[R],D=I.inboundLayers[R],_=I.nodeIndices[R],T=I.tensorIndices[R];i(P,x,A,D,_,T)}for(x.push(I);A.indexOf(I)>=0;)A.splice(A.indexOf(I),1);o.push(I)},l=[],c=[];for(let y of this.outputs)i(y,l,c);let u=o.slice().reverse();for(let y of u){n[y.id]=y,y.id in t||(t[y.id]=0);let x=t[y.id],A=s[y.outboundLayer.id]==null?0:s[y.outboundLayer.id];x=Math.max(x,A),s[y.outboundLayer.id]=x,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=x;for(let b=0;b<y.inboundLayers.length;b++){let w=y.inboundLayers[b],k=y.nodeIndices[b],I=w.inboundNodes[k],E=t[I.id]==null?0:t[I.id];t[I.id]=Math.max(x+1,E),n[I.id]=I}}let d={};for(let y in t){let x=t[y];x in d||(d[x]=[]),d[x].push(n[y])}let p={};for(let y in s){let x=s[y];x in p||(p[x]=[]),p[x].push(r[y])}let h=Object.keys(p).map(y=>parseInt(y,10)).sort(jf);this.layers=[];for(let y of h){let x=p[y];x.sort((A,b)=>{let w=a[A.id],k=a[b.id];return w<k?-1:w>k?1:0});for(let A of x)A instanceof Mr&&this.internalContainerRefs.push(A),this.layers.push(A)}this.layersByDepth=p,h=Object.keys(d).map(y=>parseInt(y,10)).sort(jf);let f=this.inputs.slice(),m=[];for(let y of h)for(let x of d[y]){let A=x.outboundLayer;if(A!=null){for(let b of x.inputTensors)if(f.indexOf(b)===-1)throw new yr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${A.name}". The following previous layers were accessed without issue: ${m}`);for(let b of x.outputTensors)f.push(b);m.push(A.name)}}this.nodesByDepth=d;let g=this.layers.map(y=>y.name);for(let y of g){let x=g.filter(A=>A===y).length;if(x!==1)throw new yr(`The name "${y}" is used ${x} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new im({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new q("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. 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}`)}fy(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${ky}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=wy(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return K(()=>{e=Tt(e);let n=new bl;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return rp(this.outputs,n,t)})}computeMask(e,t){return K(()=>{e=Tt(e);let n;return t==null?n=fl(null,e.length):n=Tt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=am(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],c=i.name+"_0_0";n[c]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(jf);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],x=`${m.name}_${g}_${y}`,A=n[x];u.push(A)}let d=c.computeOutputShape(ls(u)),p=am(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=fl(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(jf);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],k=x[A];n[b.id]=[w,k]}}}}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 K(()=>{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}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(d.inboundLayers.length>0){let f=[];for(let m=0;m<d.inboundLayers.length;m++){let g=d.inboundLayers[m],y=d.nodeIndices[m],x=d.tensorIndices[m],A=Mr.nodeKey(g,y),b=t[A];b==null&&(b=0),f.push([g.name,b,x,h])}l.push(f)}}}let c={};c.name=a.name,c.className=o,c.config=i,c.inboundNodes=l,n.push(c)}e.layers=n;let s=[];for(let a=0;a<this.inputLayers.length;a++){let o=this.inputLayers[a],i=this.inputLayersNodeIndices[a],l=Mr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.inputLayersTensorIndices[a];s.push([o.name,c,u])}e.inputLayers=s;let r=[];for(let a=0;a<this.outputLayers.length;a++){let o=this.outputLayers[a],i=this.outputLayersNodeIndices[a],l=Mr.nodeKey(o,i);if(!this.containerNodes.has(l))continue;let c=t[l];c==null&&(c=0);let u=this.outputLayersTensorIndices[a];r.push([o.name,c,u])}return e.outputLayers=r,e}static fromConfig(e,t,n={},s=!1){let r={},a={};function o(m,g){m.name in a?a[m.name].push(g):a[m.name]=[g]}function i(m,g){let y=[],x;for(let A of g){let b=A[0],w=A[1],k=A[2];if(x=A[3]==null?{}:A[3],!(b in r)){o(m,g);return}let I=r[b];if(I.inboundNodes.length<=w){o(m,g);return}let E=I.inboundNodes[w];y.push(E.outputTensors[k])}y.length>0&&m.apply(ls(y),x)}function l(m){let g=m.name,y=vr(m,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(s),r[g]=y,m.inboundNodes.forEach(A=>{if(!(A instanceof Array))throw new q(`Corrupted configuration, expected array for nodeData: ${A}`);o(y,A)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!wB(a);)for(let m of u){let g=r[m.name];if(g.name in a){let y=a[g.name];delete a[g.name];for(let x of y)i(g,x)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],y=m[1],x=m[2];Fr(g in r);let b=r[g].inboundNodes[y].outputTensors;d.push(b[x])}let f=t.outputLayers;for(let m of f){let g=m[0],y=m[1],x=m[2];Fr(g in r);let b=r[g].inboundNodes[y].outputTensors;p.push(b[x])}return new e({inputs:d,outputs:p,name:c})}get stateful(){if(this._stateful)throw new q("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){K(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function jW(e,t,n){let s=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(s===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==s)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${s} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(a=>{a in e?r.push(e[a]):r.push(null)}),r}else throw new Error(`The model has multiple (${s}) outputs, so ${n} must be either an array with ${s} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function wk(e,t){return jW(e,t,"classWeight")}async function kk(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=K(()=>{if(e.shape.length===1)return Ln(e);if(e.shape.length===2){if(e.shape[1]>1)return Fs(e,1);if(e.shape[1]===1)return H(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),a=Array.from(await r.data());te(r);let o=[];return a.forEach(i=>{if(n[i]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${i} exists in the data but not in classWeight`);o.push(n[i])}),Ct(o,"float32")}else return null}function qW(e,t){return L(e,t)}var XW=32;function Sk(e,t){let n,s,r=t;n=r.xs,s=r.ys,v.assert(n!=null&&s!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let a=Ik("input",e.inputNames,n),o=Ik("output",e.outputNames,s),i=a[0].shape[0];v.assert(a.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${a.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),v.assert(o.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${o.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let l=0;l<a.length;l++)v.assert(a[l].shape[0]===i,()=>`Batch size mismatch: input ${e.inputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);for(let l=0;l<o.length;l++)v.assert(o[l].shape[0]===i,()=>`Batch size mismatch: output ${e.outputNames[l]} has ${o[l].shape[0]}; expected ${i} based on input ${e.inputNames[0]}.`);return{xs:a,ys:o}}function Ik(e,t,n){if(n instanceof et)return[n];if(Array.isArray(n))return v.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let s=[];for(let r of t){if(n[r]==null)throw new q(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function KW(e){if(e.length===3)throw new Le("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function ZW(e,t,n){let s=n.batchesPerEpoch!=null;if(v.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),v.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),v.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),v.assert(!s||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),v.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,a,o;if(r)if(Ck(n.validationData))v.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let g=KW(n.validationData);a=g.xs,o=g.ys}let i=e.makeTrainFunction(),l=e.getDedupedMetricsNames(),c;r?c=l.slice().concat(l.map(g=>"val_"+g)):c=l.slice();let u=ck(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=dk(u,d,n.epochs,null,null,YW(t,n),null,r,c);p.setModel(e),e.history=h,await p.onTrainBegin(),e.stopTraining_=!1;let f=n.initialEpoch==null?0:n.initialEpoch,m=await t.iterator();for(;f<n.epochs;){let g={};await p.onEpochBegin(f);let y=0,x=0;for(s||(m=await t.iterator());s?y<n.batchesPerEpoch:!0;){let A=await m.next();if(s&&A.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. Make sure that your dataset can generate at least \`batchesPerEpoch * epochs\` batches (in this case, ${n.batchesPerEpoch*n.epochs} batches). You may need to use the repeat() function when building your dataset.`);break}if(A.value!=null){let{xs:b,ys:w}=Sk(e,A.value),k={};k.batch=x,k.size=b[0].shape[0],await p.onBatchBegin(x,k);let I=[];if(n.classWeight!=null){let P=wk(n.classWeight,e.outputNames);for(let D=0;D<P.length;++D)I.push(await kk(w[D],null,P[D]))}let E=b.concat(w).concat(I),R=i(E);te(E);for(let P=0;P<l.length;++P){let D=l[P],_=R[P];k[D]=_,yn(_)}await p.onBatchEnd(x,k),ak(k),x++,y++}if(s?y>=n.batchesPerEpoch:A.done){if(r){let b;Ck(n.validationData)?b=Tt(await e.evaluateDataset(n.validationData,{batches:n.validationBatches})):b=Tt(e.evaluate(a,o,{batchSize:n.validationBatchSize==null?XW:n.validationBatchSize,verbose:0}));for(let w=0;w<e.metricsNames.length;++w)g[`val_${e.metricsNames[w]}`]=b[w]}break}if(e.stopTraining_)break}if(await p.onEpochEnd(f,g),f++,e.stopTraining_)break}return await p.onTrainEnd(),await e.history.syncData(),e.history}finally{e.isTraining=!1}}function YW(e,t){let n=null;return t.batchesPerEpoch!=null?n=t.batchesPerEpoch:Number.isFinite(e.size)&&(n=e.size),n}function Ck(e){return typeof e.iterator=="function"}function JW(e){return typeof e.next=="function"}async function QW(e,t,n){n=n||{};let s=n.batches!=null,r=e.testFunction,a=[];if(n.verbose>0)throw new Le("Verbose mode is not implemented yet.");v.assert(!s||n.batches>0&&Number.isInteger(n.batches),()=>`Test loop expects \`batches\` to be a positive integer, but received ${JSON.stringify(n.batches)}`);let o=JW(t)?t:await t.iterator(),i=0,l=0;for(;s?l<n.batches:!0;){let c=await o.next();if(a=K(()=>{if(c.value){let{xs:u,ys:d}=Sk(e,c.value),p=u.concat(d),h=K(()=>r(p));if(te(p),l===0)for(let m=0;m<h.length;++m)a.push(Ce(0));let f=p[0].shape[0];for(let m=0;m<h.length;++m){let g=h[m],y=a[m];a[m]=K(()=>ue(a[m],L(f,g))),l>0&&te(y)}te(h),i+=f,++l}return a}),c.done){s&&console.warn(`Your dataset iterator ran out of data during evaluateDataset(). 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stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=hf().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-hf().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=na(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>na(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let s of t)if(typeof n[s]=="string")e[s]=na(n[s]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof 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e.metrics)r[a]=ml(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=rs.getSaveHandlers(e);if(l.length===0)throw new q(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new q(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new q("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await rs.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:oV,generatedBy:`TensorFlow.js tfjs-layers v${ky}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:c,specs:u}=await rs.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...u),n.data=rs.concatenateArrayBuffers([n.data,c])}if(this.userDefinedMetadata!=null){let l=!0;Ak(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){Ak(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};sa.className="Model";ce.registerClass(sa);var _k=class extends sa{};_k.className="Functional";ce.registerClass(_k);async function iV(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=sp(n),r=vr(s,t);if(e.weightsManifest!=null){let a=await rs.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(i=>i.originalName)),o={};for(let i of r.weights)o[i.originalName]=a[i.originalName];r.loadWeights(o),te(a)}return r}async function lV(e,t){if(t==null&&(t={}),typeof e=="string"){let n=rs.getLoadHandlers(e,t);if(n.length===0)n.push(rs.browserHTTPRequest(e,t));else if(n.length>1)throw new q(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return uV(e,void 0,t)}async function uV(e,t,n){if(n==null&&(n={}),e.load==null)throw new q("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let s=await e.load(),r=s.modelTopology;r.model_config!=null&&(r=r.model_config);let a=n.strict==null?!0:n.strict,o=s.weightData!=null&&s.weightSpecs!=null&&a,i=vr(sp(r),t,o),l=s.trainingConfig;if(l!=null&&i.loadTrainingConfig(l),s.userDefinedMetadata!=null&&i.setUserDefinedMetadata(s.userDefinedMetadata),s.weightData!=null){if(s.weightSpecs==null)throw new q("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=cV(s.weightData,s.weightSpecs);i.loadWeights(c,a),i.optimizer!=null&&u.length>0&&await i.optimizer.setWeights(u),te(c),te(u.map(d=>d.tensor))}return i}function cV(e,t){let n=rs.decodeWeights(e,t),s={},r=[];return t.forEach(a=>{a.group==="optimizer"?r.push({name:a.name,tensor:n[a.name]}):s[a.name]=n[a.name]}),{modelWeights:s,optimizerWeights:r}}var Ey=class extends sa{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:rm("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(n=>n<0))throw new q(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof Ey||e instanceof sa,n;if(t){if(n=e,n.outputs.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. 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Add some layers first.");this.model=new sa({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new yr("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new yr("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new yr("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new yr("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},s=!1){let r,a={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new q("Legacy serialization format not supported yet.");r=t}else v.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,a=t;let o=new e(a);if(!(o instanceof Ey))throw new Le(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let c=vr(i,void 0,s);s&&c.setFastWeightInitDuringBuild(!0),o.add(c)}return o}set stopTraining(e){if(this.model==null)throw new q("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new q("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}},gm=Ey;gm.className="Sequential";ce.registerClass(gm);function dV(e){return new sa(e)}function pV(e){return new gm(e)}function hV(e,t){return t==null&&(t={}),lV(e,t)}function Dk(e){return rk(e)}function fV(e,t){my.registerCallbackConstructor(e,t)}var cs=class extends ce.Serializable{getConfig(){return{}}},$k=class extends cs{apply(e,t=1){return BB(e,t)}};$k.className="elu";ce.registerClass($k);var Fk=class extends cs{apply(e){return _1(e)}};Fk.className="selu";ce.registerClass(Fk);var Pk=class extends cs{apply(e){return $r(e)}};Pk.className="relu";ce.registerClass(Pk);var Ok=class extends cs{apply(e){return K(()=>Ud(6,$r(e)))}};Ok.className="relu6";ce.registerClass(Ok);var Mk=class extends cs{apply(e){return e}};Mk.className="linear";ce.registerClass(Mk);var zk=class extends cs{apply(e){return os(e)}};zk.className="sigmoid";ce.registerClass(zk);var Lk=class extends cs{apply(e){return VB(e)}};Lk.className="hardSigmoid";ce.registerClass(Lk);var Bk=class extends cs{apply(e){return Gu(e)}};Bk.className="softplus";ce.registerClass(Bk);var Wk=class extends cs{apply(e){return WB(e)}};Wk.className="softsign";ce.registerClass(Wk);var Vk=class extends cs{apply(e){return Lu(e)}};Vk.className="tanh";ce.registerClass(Vk);var Ry=class extends cs{apply(e,t=-1){return Xu(e,t)}};Ry.className="softmax";ce.registerClass(Ry);var Uk=class extends cs{apply(e,t=-1){return b1(e,t)}};Uk.className="logSoftmax";ce.registerClass(Uk);var Gk=class extends cs{apply(e,t=1){return K(()=>L(os(L(e,t)),e))}};Gk.className="swish";ce.registerClass(Gk);var Hk=class extends cs{apply(e){return K(()=>L(e,Lu(Gu(e))))}};Hk.className="mish";ce.registerClass(Hk);function Bo(e){return e.getClassName()}function _y(e,t={}){return Zd(e,ce.SerializationMap.getMap().classNameMap,t,"activation")}function Wo(e){if(e==null){let t={};return t.className="linear",t.config={},_y(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},_y(t)}else return e instanceof cs?e:_y(e)}function Dy(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 jk=class extends ce.Serializable{},op=class extends jk{constructor(e){super();Dy(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 K(()=>{let t=jt([1]);return this.hasL1&&(t=ue(t,we(L(this.l1,rn(e))))),this.hasL2&&(t=ue(t,we(L(this.l2,ep(e))))),H(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};op.className="L1L2";ce.registerClass(op);function mV(e){return Dy(e),new op({l1:e!=null?e.l1:null,l2:0})}function gV(e){return Dy(e),new op({l2:e!=null?e.l2:null,l1:0})}var qk={l1l2:"L1L2"};function bt(e){return q1(e)}function Xk(e,t={}){return Zd(e,ce.SerializationMap.getMap().classNameMap,t,"regularizer")}function Ft(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in qk?qk[e]:e,config:{}};return Xk(n)}else return e instanceof jk?e:Xk(e)}var $y=class extends st{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Ve(e);let n=$r(e);return this.maxValue!=null&&(n=As(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};$y.className="ReLU";ce.registerClass($y);var Fy=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Ve(e);return kf(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Fy.className="LeakyReLU";ce.registerClass(Fy);var Py=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=$t(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Ft(e.alphaRegularizer),this.alphaConstraint=un(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new q(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=ft(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Jt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Ve(e),Rf(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Lt(this.alphaInitializer),alphaRegularizer:bt(this.alphaRegularizer),alphaConstraint:ln(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Py.className="PReLU";ce.registerClass(Py);var Oy=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Le(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Ve(e);return Wd(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Oy.className="ELU";ce.registerClass(Oy);var My=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Ve(e);return L(n,ge(xs(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};My.className="ThresholdedReLU";ce.registerClass(My);var zy=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Ry().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}};zy.className="Softmax";ce.registerClass(zy);function tc(e,t,n){if(typeof e=="number")return fl(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(!OB(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 wr(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+zo([n-t,0]);else if(s==="same")e=e*t;else throw new q(`Unsupport padding mode: ${s}.`);return e}function Ly(e,t){return K(()=>(qt(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function Kk(e,t){return K(()=>(qt(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function yV(e,t,n,s=1,r="valid",a,o=1){return K(()=>{if(a==null&&(a=gr()),qt(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=p1(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=xr(i,n)),i})}function Zk(e,t,n,s=[1,1],r="valid",a,o,i=null){return K(()=>{if(a==null&&(a=gr()),qt(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=Ly(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Fo.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 AV(e,t,n,s=[1,1,1],r="valid",a,o){return K(()=>{if(a==null&&(a=gr()),qt(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 ${e.rank}.`);let i=Kk(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=m1(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=xr(i,n)),a==="channelsFirst"&&(i=tt(i,[0,4,1,2,3])),i})}var By=class extends st{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",By.verifyArgs(t),this.rank=e,xn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Le(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=tc(t.kernelSize,e,"kernelSize"),this.strides=tc(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,qt(this.dataFormat),this.activation=Wo(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=tc(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"&&!K1(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 ${JSON.stringify(e.kernelSize)}.`)}getConfig(){let e={kernelSize:this.kernelSize,strides:this.strides,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,activation:Bo(this.activation),useBias:this.useBias,biasInitializer:Lt(this.biasInitializer),biasRegularizer:bt(this.biasRegularizer),activityRegularizer:bt(this.activityRegularizer),biasConstraint:ln(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},ip=class extends By{constructor(e,t){super(e,t);this.kernel=null,ip.verifyArgs(t),this.filters=t.filters,xn(this.filters,"filters"),this.kernelInitializer=$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 K(()=>{e=Ve(e);let n,s=this.bias==null?null:this.bias.read(),r=Lw(this.activation.getClassName());if(r!=null&&this.rank===2)n=Zk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=yV(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=Zk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=AV(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=wr(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)}`)}},Yk=class extends ip{constructor(e){super(2,e);Yk.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!K1(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)}.`)}},ym=Yk;ym.className="Conv2D";ce.registerClass(ym);var Jk=class extends ip{constructor(e){super(3,e);Jk.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)}.`)}},Am=Jk;Am.className="Conv3D";ce.registerClass(Am);var Wy=class extends ym{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 ${this.padding}`)}build(e){if(e=ft(e),e.length!==4)throw new q("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Jt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return K(()=>{let n=Ve(e);if(n.shape.length!==4)throw new q(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=f1(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=tt(g,[0,3,1,2])),this.bias!=null&&(g=xr(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}};Wy.className="Conv2DTranspose";ce.registerClass(Wy);var Vy=class extends Am{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 ${this.padding}`)}build(e){if(e=ft(e),e.length!==5)throw new q("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new q("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Jt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return K(()=>{let n=Ve(e);if(n.shape.length!==5)throw new q(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=Rv(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=xr(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}};Vy.className="Conv3DTranspose";ce.registerClass(Vy);var Qk=class extends ip{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 K(()=>{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=Yv(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=xr(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}};Qk.className="SeparableConv";var Uy=class extends Qk{constructor(e){super(2,e)}};Uy.className="SeparableConv2D";ce.registerClass(Uy);var eS=class extends ip{constructor(e){super(1,e);eS.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"&&!K1(e.kernelSize,"number",1,1))throw new q(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},Gy=eS;Gy.className="Conv1D";ce.registerClass(Gy);var Hy=class extends st{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return K(()=>{if(e=Ve(e),this.dataFormat==="channelsLast"){let n=Xf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Xf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Xf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Xf(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Hy.className="Cropping2D";ce.registerClass(Hy);var jy=class extends st{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,$B(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 K(()=>{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}};jy.className="UpSampling2D";ce.registerClass(jy);function xV(e,t,n=[1,1],s="valid",r,a){return K(()=>{r==null&&(r=gr()),qt(r);let o=Ly(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 ${t.rank}-D`);return o=Bd(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=tt(o,[0,3,1,2])),o})}var qy=class extends By{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=$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 K(()=>{e=Ve(e);let n=xV(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=xr(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=wr(t,this.kernelSize[0],this.padding,this.strides[0]),a=wr(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}};qy.className="DepthwiseConv2D";ce.registerClass(qy);function tS(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 nS(e,t,n,s=!1,r,a,o=!1,i=!1){return K(()=>{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(Ar(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=ge(ge(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=is(t),m;r!=null&&(m=is(r));for(let y=0;y<h;++y){let x=f[y],A=K(()=>e(x,p));if(r==null)d=A[0],p=A[1];else{let b=K(()=>{let w=m[y],k=he(zs(w),w),I=ue(L(A[0],w),L(p[0],k)),E=p.map((R,P)=>ue(L(A[1][P],w),L(R,k)));return{output:I,newStates:E}});d=b.output,p=b.newStates}i&&u.push(d)}let g;return i&&(g=an(u,1)),[d,g,p]})}var sS=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 vm({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 Ar(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){py(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 K(()=>{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.");py(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){K(()=>{if(!this.stateful)throw new ta("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=>jt([n,s])):this.states_=[jt([n,this.cell.stateSize])];else if(e==null)te(this.states_),this.keptStates!=null&&(te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>jt([n,s])):this.states_[0]=jt([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()):te(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=tS(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 br){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 K(()=>{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 ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let o={training:s},l=nS((h,f)=>{let m=this.cell.call([h].concat(f),o);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),c=l[0],u=l[1],d=l[2];this.stateful&&this.resetStates(d,s);let p=this.returnSequences?u:c;return this.returnState?[p].concat(d):p})}getInitialState(e){return K(()=>{let t=jt(e.shape);return t=we(t,[1,2]),t=Qd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?sy(t,[1,n]):t):this.cell.stateSize>1?[sy(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===sS.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=vr(s,n);return new e(Object.assign(t,{cell:r}))}},ra=sS;ra.className="RNN";ce.registerClass(ra);var lp=class extends st{},xm=class extends lp{constructor(e){super(e);this.DEFAULT_ACTIVATION="tanh",this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_RECURRENT_INITIALIZER="orthogonal",this.DEFAULT_BIAS_INITIALIZER="zeros",this.units=e.units,xn(this.units,"units"),this.activation=Wo(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=$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=Yu([1,zo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Yu([1,zo([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 K(()=>{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=Vo({ones:()=>zs(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vo({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=xr(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:Bo(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}}};xm.className="SimpleRNNCell";ce.registerClass(xm);var Xy=class extends ra{constructor(e){e.cell=new xm(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(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)}};Xy.className="SimpleRNN";ce.registerClass(Xy);var bm=class extends lp{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=Wo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Wo(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=Yu([1,zo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Yu([1,zo([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 K(()=>{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=Vo({ones:()=>zs(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vo({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=xr(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:Bo(this.activation),recurrentActivation:Bo(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}}};bm.className="GRUCell";ce.registerClass(bm);var Ky=class extends ra{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 bm(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(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)}};Ky.className="GRU";ce.registerClass(Ky);var up=class extends lp{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=Wo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Wo(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=Yu([1,zo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Yu([1,zo([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 sr{apply(o,i){let l=r.apply([a]),c=new Zf().apply([a]),u=r.apply([a*2]);return Xw(Xw(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 K(()=>{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=Vo({ones:()=>zs(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Vo({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=xr(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:Bo(this.activation),recurrentActivation:Bo(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}}};up.className="LSTMCell";ce.registerClass(up);var Zy=class extends ra{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 up(e);super(e)}call(e,t){return K(()=>{this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(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)}};Zy.className="LSTM";ce.registerClass(Zy);var vm=class extends lp{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 K(()=>{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){py(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{yl(`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(vr(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 hy(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]])}fy(t)}};vm.className="StackedRNNCells";ce.registerClass(vm);function Vo(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):Zw(t(),n),i=()=>tp(o,t,s);return!r||r<=1?yn(i().clone()):Array(r).fill(void 0).map(i).map(c=>yn(c.clone()))}var rS=class extends ra{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 K(()=>{if(this.cell.dropoutMask!=null&&(te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(te(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 K(()=>{let{stateSize:t}=this.cell,n=e.shape,s=this.computeSingleOutputShape(n),r=[s[0],...s.slice(2)],a=jt(r);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){K(()=>{if(!this.stateful)throw new ta("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(()=>jt(r)):this.states_=[jt(r)];else if(e==null)te(this.states_),this.keptStates!=null&&(te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>jt(r)):this.states_[0]=jt(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()):te(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=wr(l,s[0],r,a[0],o[0]),d=wr(c,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,u,d]:[u,d,n]]}};rS.className="ConvRNN2D";var wm=class extends up{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=tc(n,2,"kernelSize"),this.kernelSize.forEach(i=>xn(i,"kernelSize")),this.strides=tc(s||1,2,"strides"),this.strides.forEach(i=>xn(i,"strides")),this.padding=r||"valid",Bs(this.padding),this.dataFormat=a||"channelsLast",qt(this.dataFormat),this.dilationRate=tc(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 sr{apply(u,d){let p=l.apply([c]),h=bs([c]),f=l.apply([c*2]);return ny([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 K(()=>{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=Vo({ones:()=>zs(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(Z,Q,ne)=>!Q||!Q[ne]?Z:L(Q[ne],Z),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=Vo({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,k]=Yt(this.kernel.read(),o,x),[I,E,R,P]=this.useBias?Yt(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,A,I,this.padding),u=this.inputConv(u,b,E,this.padding),d=this.inputConv(d,w,R,this.padding),p=this.inputConv(p,k,P,this.padding);let[D,_,T,O]=Yt(this.recurrentKernel.read(),o,x);f=this.recurrentConv(f,D),m=this.recurrentConv(m,_),g=this.recurrentConv(g,T),y=this.recurrentConv(y,O);let W=this.recurrentActivation.apply(ue(c,f)),X=this.recurrentActivation.apply(ue(u,m)),z=ue(L(X,a),L(W,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=_o(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?xr(r,n,this.dataFormat):r}recurrentConv(e,t){return _o(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};wm.className="ConvLSTM2DCell";ce.registerClass(wm);var Yy=class extends rS{constructor(e){let t=new wm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};Yy.className="ConvLSTM2D";ce.registerClass(Yy);var km=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 K(()=>{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 tp(()=>Zw(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()}};km.className="Dropout";ce.registerClass(km);var Jy=class extends km{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Jy.className="SpatialDropout1D";ce.registerClass(Jy);var Qy=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=Wo(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 K(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=Lw(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=xr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Bo(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}};Qy.className="Dense";ce.registerClass(Qy);var eA=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)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Mo(e,1)]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=Ve(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=tt(n,s)}return LB(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};eA.className="Flatten";ce.registerClass(eA);var tA=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.activation=Wo(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=Ve(e);return this.activation.apply(n)})}getConfig(){let e={activation:Bo(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};tA.className="Activation";ce.registerClass(tA);var nA=class extends st{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return K(()=>(e=Ve(e),MB(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};nA.className="RepeatVector";ce.registerClass(nA);var sA=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=Mo(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 K(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return H(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};sA.className="Reshape";ce.registerClass(sA);var rA=class extends st{constructor(e){super(e);if(e.dims==null)throw new Error("Required configuration field `dims` is missing during Permute constructor call.");if(!Array.isArray(e.dims))throw new Error(`Permute constructor requires \`dims\` to be an Array, but received ${e.dims} instead.`);let t=Ar(1,e.dims.length+1);if(!v.arraysEqual(e.dims.slice().sort(),t))throw new Error("Invalid permutation `dims`: "+JSON.stringify(e.dims)+" `dims` must contain consecutive integers starting from 1.");this.dims=e.dims,this.dimsIncludingBatch=[0].concat(this.dims),this.inputSpec=[new Jt({ndim:this.dims.length+1})]}computeOutputShape(e){e=ft(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return tt(Ve(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};rA.className="Permute";ce.registerClass(rA);var aA=class extends st{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Ve(e),s=-1;return yf(Hu(n,this.maskValue),s)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=-1,r=!0,a=yf(Hu(n,this.maskValue),s,r);return L(n,ge(a,n.dtype))})}};aA.className="Masking";ce.registerClass(aA);var oA=class extends st{constructor(e){super(e);if(this.embeddings=null,this.DEFAULT_EMBEDDINGS_INITIALIZER="randomUniform",e.batchInputShape==null&&e.inputShape==null){let t=null;e.batchSize!=null&&(t=e.batchSize),e.inputLength==null?this.batchInputShape=[t,null]:this.batchInputShape=[t].concat(Tt(e.inputLength))}this.inputDim=e.inputDim,xn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,xn(this.outputDim,"outputDim"),this.embeddingsInitializer=$t(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Ft(e.embeddingsRegularizer),this.activityRegularizer=Ft(e.activityRegularizer),this.embeddingsConstraint=un(e.embeddingsConstraint),this.maskZero=e.maskZero,this.supportsMasking=e.maskZero,this.inputLength=e.inputLength}build(e){this.embeddings=this.addWeight("embeddings",[this.inputDim,this.outputDim],this.dtype,this.embeddingsInitializer,this.embeddingsRegularizer,!0,this.embeddingsConstraint),this.built=!0}warnOnIncompatibleInputShape(e){}computeMask(e,t){return K(()=>this.maskZero?(e=Ve(e),Hu(e,nt(e))):null)}computeOutputShape(e){if(e=ft(e),this.inputLength==null)return[...e,this.outputDim];let t=Tt(this.inputLength);if(t.length!==e.length-1)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let s=0;s<t.length;++s){let r=t[s],a=e[s+1];if(r!=null&&a!=null&&r!==a)throw new q(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=a),n++}}return[e[0],...t,this.outputDim]}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=Ve(e);n.dtype!=="int32"&&(n=qf(n,"int32"));let s=Kw(this.embeddings.read(),H(n,[n.size]));return H(s,ft(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Lt(this.embeddingsInitializer),embeddingsRegularizer:bt(this.embeddingsRegularizer),activityRegularizer:bt(this.activityRegularizer),embeddingsConstraint:ln(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};oA.className="Embedding";ce.registerClass(oA);var wl=class extends st{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Le}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let s=0;s<t.length;++s){let r=e[e.length-t.length+s],a=t[s];if(r==null||a==null||r<0||a<0)n.push(null);else if(r===1)n.push(a);else if(a===1)n.push(r);else{if(r!==a)throw new q("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[ft(e)]),e=e,e.length<2)throw new q(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=Oo(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&Oo(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=zo(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=Qd(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let c=i.shape,u=c[0],d=c.slice(1).concat([u]),p=H(i,[u].concat(Mo(c.slice(1))));p=tt(p,[1,0]),p=H(p,d),n.push(p),r=!0}else if(l>1){let c=Ar(1,l).concat([0]);n.push(tt(i,c)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,c=i[l-1],u=[c].concat(i.slice(0,i.length-1));a=H(tt(H(a,[-1,c]),[1,0]),u)}else if(o>1){let i=[o-1].concat(Ar(0,o-1));a=tt(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Oo(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Zt(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=fr(n,t[s]);return n})}},iA=class extends wl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ue(t,e[n]);return t})}};iA.className="Add";ce.registerClass(iA);var lA=class extends wl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};lA.className="Multiply";ce.registerClass(lA);var uA=class extends wl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ue(t,e[n]);return L(1/e.length,t)})}};uA.className="Average";ce.registerClass(uA);var cA=class extends wl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Jr(t,e[n]);return t})}};cA.className="Maximum";ce.registerClass(cA);var dA=class extends wl{constructor(e){super(e)}mergeFunction(e){return K(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ud(t,e[n]);return t})}};dA.className="Minimum";ce.registerClass(dA);var pA=class extends wl{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new q("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let s of e)if(s!=null){t=!1;break}if(t)return;let n=[];for(let s=0;s<e.length;++s){let r=e[s].slice();r.splice(this.axis,1);let a=!1;for(let o of n)if(v.arraysEqual(o,r)){a=!0;break}a||n.push(r)}if(n.length>1)throw new q("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return K(()=>ny(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new q("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),s=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[s]==null||r[s]==null){n[s]=null;break}n[s]+=r[s]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new q("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new q(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return K(()=>{let n=!0;if(t.forEach(a=>{if(a!=null){n=!1;return}}),n)return null;let s=[];for(let a=0;a<e.length;++a)t[a]==null?s.push(ge(zs(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Zt(t[a],-1)):s.push(t[a]);let r=St(s,this.axis);return i1(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};pA.className="Concatenate";ce.registerClass(pA);function cp(e,t){for(;e<0;)e+=t;return e}function bV(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Le("batchDot is not implemented for tensors of 4D or higher rank yet");if(v.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),v.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Le("batchDot is not implemented for complex64-type Tensors yet.");let s=e.shape.length,r=t.shape.length;n==null&&(n=[s-1,r-2]);let a=n;return K(()=>{let o;if(s>r){o=s-r;let l=[];for(let c=0;c<o;++c)l.push(1);t=H(t,t.shape.concat(l))}else if(r>s){o=r-s;let l=[];for(let c=0;c<o;++c)l.push(1);e=H(e,e.shape.concat(l))}else o=0;let 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 hA=class extends wl{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} input(s).`);let t=e[0],n=e[1],s;return Array.isArray(this.axes)?s=this.axes.map((r,a)=>cp(r,e[a].shape.length)):s=[cp(this.axes,t.shape.length),cp(this.axes,n.shape.length)],this.normalize&&(t=lm(t,s[0]),n=lm(n,s[1])),bV(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[cp(this.axes,e.length),cp(this.axes,t.length)],n}computeOutputShape(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].slice(),n=e[1].slice();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);t.splice(s[0],1),n.splice(s[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};hA.className="Dot";ce.registerClass(hA);var fA=class extends 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 K(()=>{this.invokeCallHook(e,t);let n=Ve(e);return tp(()=>ue(Kf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};fA.className="GaussianNoise";ce.registerClass(fA);var mA=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 K(()=>{this.invokeCallHook(e,t);let n=Ve(e);return this.rate>0&&this.rate<1?tp(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return L(n,Kf(n.shape,1,r))},()=>n,t.training||!1):n})}};mA.className="GaussianDropout";ce.registerClass(mA);var gA=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Ve(e).shape}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={rate:this.rate};return Object.assign(t,e),t}call(e,t){return K(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return tp(()=>{let r=Ve(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=ll(ju(n),this.rate);l=qf(l,"float32");let c=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-c*i*this.rate,d=ue(L(r,l),L(ue(l,-1),i));return ue(L(d,c),u)},()=>Ve(e),t.training||!1)}return e})}};gA.className="AlphaDropout";ce.registerClass(gA);function dp(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=vv(e,t,n,s,r,a);else if(e.rank===3)o=wv(e,t,n,s,r,a);else if(e.rank===4)o=kv(e,t,n,s,r,a);else throw new Le(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function vV(e,t,n,s,r=.001){return K(()=>{let a=Nf(e,s),o=a.mean,i=a.variance;return[dp(e,o,i,n,t,r),o,i]})}function wV(e,t,n,s,r=.001){return K(()=>{let a=Nf(e,s),o=a.mean,i=a.variance,l=[];for(let f of Ar(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let c=H(o,l),u=H(i,l),d=t==null?null:H(t,l),p=n==null?null:H(n,l);return[dp(e,c,u,p,d,r),o,i]})}function kV(e,t,n,s,r=.001){return v.arraysEqual(s.slice().sort(),Ar(0,e.rank-1))?vV(e,t,n,s,r):wV(e,t,n,s,r)}var yA=class extends st{constructor(e){e==null&&(e={});super(e);this.supportsMasking=!0,this.axis=e.axis==null?-1:e.axis,this.momentum=e.momentum==null?.99:e.momentum,this.epsilon=e.epsilon==null?.001:e.epsilon,this.center=e.center==null?!0:e.center,this.scale=e.scale==null?!0:e.scale,this.betaInitializer=$t(e.betaInitializer||"zeros"),this.gammaInitializer=$t(e.gammaInitializer||"ones"),this.movingMeanInitializer=$t(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=$t(e.movingVarianceInitializer||"ones"),this.betaConstraint=un(e.betaConstraint),this.gammaConstraint=un(e.gammaConstraint),this.betaRegularizer=Ft(e.betaRegularizer),this.gammaRegularizer=Ft(e.gammaRegularizer)}build(e){e=ft(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new q(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Jt({ndim:e.length,axes:{[t]:n}})];let s=[n];this.scale&&(this.gamma=this.addWeight("gamma",s,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",s,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",s,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",s,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return K(()=>{let n=t.training==null?!1:t.training,s=Ve(e),r=s.shape,a=r.length,o=Ar(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=fl(1,a);l[i]=r[i];let c=o.slice();c.sort();let u=!v.arraysEqual(c,Ar(0,a).slice(0,a-1)),d=()=>{if(u){let y=H(this.movingMean.read(),l),x=H(this.movingVariance.read(),l),A=this.center?H(this.beta.read(),l):null,b=this.scale?H(this.gamma.read(),l):null;return dp(s,y,x,A,b,this.epsilon)}else return dp(s,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return d();let[p,h,f]=kV(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(y,x,A)=>{K(()=>{let b=1-A,w=y.read(),k=L(he(w,x),b);y.write(he(w,k))})};return(()=>{m(this.movingMean,h,this.momentum),m(this.movingVariance,f,this.momentum)})(),p})}getConfig(){let 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}};yA.className="BatchNormalization";ce.registerClass(yA);var AA=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!==Oo(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 K(()=>{let a=!0,{mean:o,variance:i}=Nf(n,this.axis,a),l=fl(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=Qs(o,p),i=Qs(i,p),u=Qs(u,h),d=Qs(d,h),dp(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}};AA.className="LayerNormalization";ce.registerClass(AA);function SV(e,t,n){return K(()=>{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=gr()),n!=="channelsLast"&&n!=="channelsFirst")throw new q(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],er(e,s)})}var xA=class extends st{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?gr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new q(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new q(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${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 K(()=>SV(Ve(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};xA.className="ZeroPadding2D";ce.registerClass(xA);function Sm(e,t,n,s,r,a){return K(()=>{qt(r),Uw(a),Bs(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=gr()),a==null&&(a="max"),e=Ly(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Tf(e,t,n,i):o=xf(e,t,n,i),r==="channelsFirst"&&(o=tt(o,[0,3,1,2])),o})}function aS(e,t,n,s,r,a){return K(()=>{qt(r),Uw(a),Bs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=gr()),a==null&&(a="max"),e=Kk(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=S1(e,t,n,i):o=c1(e,t,n,i),r==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var oS=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=wr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return K(()=>{this.invokeCallHook(e,t),e=Qd(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}},bA=class extends oS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Bs(s),Sm(e,t,n,s,r,"max")}};bA.className="MaxPooling1D";ce.registerClass(bA);var vA=class extends oS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Bs(s),Sm(e,t,n,s,r,"avg")}};vA.className="AveragePooling1D";ce.registerClass(vA);var iS=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,qt(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=wr(t,this.poolSize[0],this.padding,this.strides[0]),n=wr(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 K(()=>(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}},wA=class extends iS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Bs(s),Sm(e,t,n,s,r,"max")}};wA.className="MaxPooling2D";ce.registerClass(wA);var kA=class extends iS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Bs(s),Sm(e,t,n,s,r,"avg")}};kA.className="AveragePooling2D";ce.registerClass(kA);var lS=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,qt(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=wr(t,this.poolSize[0],this.padding,this.strides[0]),n=wr(n,this.poolSize[1],this.padding,this.strides[1]),s=wr(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 K(()=>(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}},SA=class extends lS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Bs(s),aS(e,t,n,s,r,"max")}};SA.className="MaxPooling3D";ce.registerClass(SA);var IA=class extends lS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return qt(r),Bs(s),aS(e,t,n,s,r,"avg")}};IA.className="AveragePooling3D";ce.registerClass(IA);var uS=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}},CA=class extends uS{constructor(e){super(e||{})}call(e,t){return K(()=>{let n=Ve(e);return Ut(n,1)})}};CA.className="GlobalAveragePooling1D";ce.registerClass(CA);var TA=class extends uS{constructor(e){super(e||{})}call(e,t){return K(()=>{let n=Ve(e);return An(n,1)})}};TA.className="GlobalMaxPooling1D";ce.registerClass(TA);var cS=class extends st{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,qt(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}},NA=class extends cS{call(e,t){return K(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?Ut(n,[1,2]):Ut(n,[2,3])})}};NA.className="GlobalAveragePooling2D";ce.registerClass(NA);var EA=class extends cS{call(e,t){return K(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?An(n,[1,2]):An(n,[2,3])})}};EA.className="GlobalMaxPooling2D";ce.registerClass(EA);var dS=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=vr(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},RA=class extends dS{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 K(()=>(e=Ve(e),nS((a,o)=>[Ve(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};RA.className="TimeDistributed";ce.registerClass(RA);function IV(e){gl(DB,"BidirectionalMergeMode",e)}var CV="concat",_A=class extends dS{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=vr(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=vr(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?CV:e.mergeMode,IV(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=tS(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 br;for(let l of a)if(l instanceof br!==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 K(()=>{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=ny([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){yl(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),yl(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 ar(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 qS(e){return!(typeof e=="number"||e.some(t=>t<0))}function pp(e,t,n){let s=qA(e,n),r=!qS(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=qA(a.shape,s)}),!qS(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function qA(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),ar(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 ar(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 ${e}`);if(this.size()===0)return pt([],[0].concat(this.elementShape));let t=[];for(let s=0;s<this.size();s++)t.push(s);let n=this.readMany(t);return ar(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${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,is(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=[];K(()=>{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)}},hp=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}`);ar(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 hp([...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.`);ar(e,this.elementShape,"TensorList shape mismatch: ");let s=pp(this.elementShape,this.tensors,e);return K(()=>{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=pp(this.elementShape,this.tensors,e),s=this.tensors.pop();return ar(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(ar(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.`);ar(this.tensors[e].shape,t,"TensorList shape mismatch: ");let s=pp(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.`);ar(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}`);ar(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let s=pp(this.elementShape,this.tensors,n);return e.length===0?pt([],[0].concat(s)):K(()=>{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}`);ar(this.elementShape,t,"TensorList shape mismatch: ");let n=pp(this.elementShape,this.tensors,t);return this.size()===0?pt([],[0].concat(n)):K(()=>{let s=this.tensors.map(r=>H(r,n));return St(s,0)})}};function _G(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);ar(r,t,"TensorList shape mismatch: ");let a=is(e);return new hp(a,t,s)}function DG(e,t,n){return new hp([],e,t,n)}function $G(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. ${s})`);let a=new hp([],n,e.dtype,s),o=is(e,0);return t.forEach((i,l)=>{a.setItem(i,o[l])}),a}function FG(e,t,n){let s=0,r=t.map(u=>(s+=u,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
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tensor.shape[0], but sum of lengths is
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${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),o=qA(a,n),i=s===0?0:e.size/s,l=K(()=>{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 hp([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var PG=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let s=S("thenBranch",e,t,n),r=S("elseBranch",e,t,n),a=S("cond",e,t,n),o=S("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=S("body",e,t,n),r=S("cond",e,t,n),a=S("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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implemented`)}},YG=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=Lf.stringNGrams(S("data",e,t,n),S("dataSplits",e,t,n),S("separator",e,t,n),S("nGramWidths",e,t,n),S("leftPad",e,t,n),S("rightPad",e,t,n),S("padWidth",e,t,n),S("preserveShortSequences",e,t,n));return[s,r]}case"StringSplit":{let{indices:s,values:r,shape:a}=Lf.stringSplit(S("input",e,t,n),S("delimiter",e,t,n),S("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[Lf.stringToHashBucketFast(S("input",e,t,n),S("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},JG=(e,t,n)=>{switch(e.op){case"Cast":return[ge(S("x",e,t,n),S("dtype",e,t,n))];case"ExpandDims":{let s=S("axis",e,t,n);return[Zt(S("x",e,t,n),s)]}case"Squeeze":{let s=S("axis",e,t,n);return[rt(S("x",e,t,n),s)]}case"Reshape":return[H(S("x",e,t,n),S("shape",e,t,n))];case"MirrorPad":return[qv(S("x",e,t,n),S("padding",e,t,n),S("mode",e,t,n))];case"PadV2":case"Pad":return[er(S("x",e,t,n),S("padding",e,t,n),S("constantValue",e,t,n))];case"SpaceToBatchND":{let s=S("blockShape",e,t,n),r=S("paddings",e,t,n);return[Ef(S("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=S("blockShape",e,t,n),r=S("crops",e,t,n);return[bf(S("x",e,t,n),s,r)]}case"DepthToSpace":{let s=S("blockSize",e,t,n),r=S("dataFormat",e,t,n).toUpperCase();return[Dv(S("x",e,t,n),s,r)]}case"BroadcastTo":return[Ld(S("x",e,t,n),S("shape",e,t,n))];case"BroadcastArgs":return[Sv(S("s0",e,t,n),S("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function KS(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return K(()=>NG(a,o,i));case"basic_math":return K(()=>EG(a,o,i));case"control":return PG(a,o,i);case"convolution":return K(()=>OG(a,o,i));case"creation":return K(()=>MG(a,o,i));case"dynamic":return zG(a,o,i);case"evaluation":return K(()=>LG(a,o,i));case"image":return K(()=>UG(a,o,i));case"graph":return K(()=>BG(a,o,i));case"logical":return K(()=>GG(a,o,i));case"matrices":return K(()=>HG(a,o,i));case"normalization":return K(()=>jG(a,o,i));case"reduction":return K(()=>qG(a,o,i));case"slice_join":return K(()=>XG(a,o,i));case"sparse":return K(()=>KG(a,o,i));case"spectral":return K(()=>ZG(a,o,i));case"string":return K(()=>YG(a,o,i));case"transformation":return K(()=>JG(a,o,i));case"hash_table":return VG(a,o,i,s);case"custom":let l=kS(a.op);if(l&&l.customExecutor)return l.customExecutor(new TG(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 ZS=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 YS(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((JS(p)||sH(p)||rH(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 QG(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 eH=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],tH=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],nH=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function JS(e){return eH.indexOf(e.op)>=0}function sH(e){return tH.indexOf(e.op)>=0}function rH(e){return nH.indexOf(e.op)>=0}var KA=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 KA(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=YS(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 QG(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 K(()=>{let u=new ZS(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=KS(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=oG(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 ZS(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}=YS(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=>!JS(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}]. ${x}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let d="";if(u.node.op==="Enter"&&S("isConstant",u.node,s,n)&&([d]=Lr(u.node.name,n)),s[u.node.name]==null){let p=KS(u.node,s,n,this._resourceManager);d||([d]=Lr(u.node.name,n));let h=n.currentContext;v.isPromise(p)?c.push(p.then(f=>(s[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l),f))):(s[d]=p,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l))}else this.processChildNodes(u.node,t,n,s,r,l)}return c}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Lr(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=vs(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${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 '${t}' is not found in the graph`)})}},aH=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},oH="?tfjs-format=file",iH="model.json",QS=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new aH}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=rs.browserHTTPRequest(e,this.loadOptions);else{let t=rs.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(rs.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let s=rs.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new KA(US.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=US.Instance.transformGraph(e.modelInitializer);this.initializer=new KA(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=rs.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Be(e,t={}){if(e==null)throw 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RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},a7=class extends r7{constructor(){super(a7.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}},o7=a7;o7.INITIAL_CAPACITY=32;function i7(e){return new xH(e)}function ZA(e){return new bH(e)}function yH(e,t){return new u7(e,t)}function AH(e,t=Em.FAIL){return new EH(e,t)}var bn=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new TH(this,e)}filter(e){return new IH(this,e)}map(e){return new CH(this,e)}mapAsync(e){return new l7(this,e)}serialMapAsync(e){return new l7(this,e).serial()}flatmap(e){return new NH(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new SH(this,e,t)}columnMajorBatch(e,t=!0,n=n7){return this.rowMajorBatch(e,t).map(r=>pH(r,n))}concatenate(e,t){return new u7(i7([this,e]),t)}take(e){return e<0||e==null?this:new kH(this,e)}skip(e){return e<0||e==null?this:new wH(this,e)}prefetch(e){return new c7(this,e)}shuffle(e,t){return new RH(this,e,t)}serial(){return new vH(this)}},xH=class extends bn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let 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this.upstream.next()}},kH=class extends bn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},SH=class extends bn{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},IH=class extends bn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async 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;te(e.value)}}},CH=class extends bn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=pr.getTensorsInContainer(e.value),n=this.transform(e.value),s=pr.getTensorsInContainer(n);for(let r of t)pr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},TH=class extends bn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},l7=class extends bn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=pr.getTensorsInContainer(e.value),n=await this.transform(e.value),s=pr.getTensorsInContainer(n);for(let r of t)pr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},YA=class extends bn{constructor(){super();this.outputQueue=new o7,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},NH=class extends YA{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=pr.getTensorsInContainer(e.value),n=this.transform(e.value),s=pr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)pr.isTensorInList(r,s)||r.dispose();return!0}},u7=class extends bn{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},Em;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(Em||(Em={}));var EH=class extends bn{constructor(e,t=0){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,n=0;function s(a){return a instanceof bn?{value:a.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await s7(this.iterators,s);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case 0:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},c7=class extends bn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new r7(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},RH=class extends c7{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=cH.alea(n||v.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},sc=class{constructor(){this.size=null}batch(e,t=!0){let n=this;v.assert(e>0,()=>`batchSize needs to be positive, but it is
|
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${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),ws(async()=>(await n.iterator()).columnMajorBatch(e,t,$H),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=>K(()=>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=>K(()=>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=ZA(async()=>({value:await t.iterator(),done:!1}));return yH(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=uH.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()}};sc.MAX_BUFFER_SIZE=1e4;function ws(e,t=null){return new class extends sc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function _H(e){return ws(async()=>i7(e),e.length)}function DH(e){if(!nc(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 s7(e,s=>{if(s instanceof sc)return{value:s.iterator(),recurse:!1};if(nc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return AH(n,Em.SHORTEST)},t)}function $H(e){if(e===null)return null;let t=e[0];return hH(t)?{value:FH(e),recurse:!1}:{value:null,recurse:!0}}function FH(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 d7=class extends sc{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))}},Rm='"',fp=Symbol("out"),p7=Symbol("field"),_m=Symbol("quote"),JA=Symbol("quoteafterquote"),h7=Symbol("quoteinquote"),f7=class extends sc{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 d7(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=fp;for(let o=0;o<r;o++)switch(a){case fp:switch(e.charAt(o)){case Rm:s=o+1,a=_m;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=fp;break;default:a=p7,s=o;break}break;case p7:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=fp,s=o+1;break;default:}break;case _m:switch(e.charAt(o)){case Rm:a=JA;break;default:}break;case JA:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=fp,s=o+1;break;case Rm:a=_m;break;default:a=h7;break}break;case h7:switch(e.charAt(o)){case Rm:a=_m;break;default:}break;default:}if(a===JA?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}},m7=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 m7(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)}},g7=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=mr([a,r,i,o],[1,4])}else this.cropBox=mr([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 g7(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=Js.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=Zt(ge(e,"float32"),0),n;n=Se.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return H(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},y7=class{},A7=class extends bn{split(e){return new PH(this,e)}},PH=class extends A7{constructor(e,t){super();this.upstream=e,this.impl=new OH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},OH=class extends YA{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},MH=class extends bn{decodeUTF8(){return new zH(this)}},zH=class extends A7{constructor(e){super();this.upstream=e,this.impl=new LH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},LH=class extends YA{constructor(e){super();if(this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=q5();this.decoder=new t("utf8")}}summary(){return`${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}},x7=class extends MH{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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function BH(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=WH(e));let a=await(n||v.fetch)(s,r);if(a.ok){let o=new Uint8Array(await a.arrayBuffer());return new x7(o,t)}else throw new Error(a.statusText)}var WH=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function b7(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var v7=class extends y7{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(b7(this.input)&&Y().get("IS_NODE")){let e=Ch();this.input=e.readFileSync(this.input.substr(7))}return new x7(this.input,this.options)}},w7=class extends y7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return b7(this.url)?new v7(this.url,this.fileOptions).iterator():BH(this.url,this.fileOptions)}};function VH(e,t={}){return new f7(new w7(e),t)}function UH(e){let t=ZA(e);return ws(async()=>t)}function GH(e){return ws(async()=>{let t=await e();return ZA(()=>t.next())})}async function HH(e,t){return g7.create(e,t)}async function jH(e){return m7.create(e)}var qH="0.0.0";function Re(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var XH=tr.whereImpl,k7=class extends nu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new ad(this,as())}nextDataId(){return k7.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){Re([e],"where");let t=this.readSync(e.dataId);return XH(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},QA=k7;QA.nextDataId=0;var Dm={};Me(Dm,{addImpl:()=>I7,bincountImpl:()=>tx,bincountReduceImpl:()=>C7,ceilImpl:()=>T7,concatImpl:()=>nx,equalImpl:()=>N7,expImpl:()=>R7,expm1Impl:()=>D7,floorImpl:()=>$7,gatherNdImpl:()=>F7,gatherV2Impl:()=>P7,greaterEqualImpl:()=>M7,greaterImpl:()=>O7,lessEqualImpl:()=>L7,lessImpl:()=>z7,linSpaceImpl:()=>B7,logImpl:()=>W7,maxImpl:()=>V7,maximumImpl:()=>U7,minimumImpl:()=>G7,multiplyImpl:()=>sx,negImpl:()=>H7,notEqualImpl:()=>j7,prodImpl:()=>q7,rangeImpl:()=>ax,rsqrtImpl:()=>X7,sigmoidImpl:()=>Oj,simpleAbsImpl:()=>S7,sliceImpl:()=>Pm,sparseFillEmptyRowsImpl:()=>Z7,sparseReshapeImpl:()=>Y7,sparseSegmentReductionImpl:()=>ox,sqrtImpl:()=>Lj,squaredDifferenceImpl:()=>J7,stridedSliceImpl:()=>Q7,stringNGramsImpl:()=>eI,stringSplitImpl:()=>tI,stringToHashBucketFastImpl:()=>nI,subImpl:()=>sI,tileImpl:()=>rI,topKImpl:()=>oI,transposeImpl:()=>rx,uniqueImpl:()=>iI});function S7(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var KH=e=>{let{x:t}=e.inputs,n=e.backend;Re(t,"abs");let s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return s=S7(r),n.makeOutput(s,t.shape,t.dtype)},ZH={kernelName:di,backendName:"cpu",kernelFunc:KH};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(I=>A[I]=0);let b=v.locToIndex(A,d,h),w=x.slice(-p);g.forEach(I=>w[I]=0);let k=v.locToIndex(w,p,f);u[y]=e(s[b],r[k])}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 YH={kernelName:ud,backendName:"cpu",kernelFunc:ks};function $m(e,t,n="float32"){if(n==="complex64"){let r=$m(e,t,"float32"),a=$m(e,t,"float32");return ks({inputs:{real:r,imag:a},backend:e})}let s=v.makeZerosTypedArray(v.sizeFromShape(t),n);return e.makeTensorInfo(t,n,s)}function Br(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var JH={kernelName:qa,backendName:"cpu",kernelFunc:Br};function kl(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.real,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var QH={kernelName:Ad,backendName:"cpu",kernelFunc:kl};function Uo(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Br({inputs:{x:r},backend:n});let o=$m(n,r.shape,r.dtype),i=Uo({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=kl({inputs:{input:r},backend:n}),i=Uo({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 ${a}`)}var ej={kernelName:$a,backendName:"cpu",kernelFunc:Uo};function vn(e,t,n,s){return n==null?({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;Re([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 tI(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;Hj(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 nI(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 sI=Qt((e,t)=>e-t),jj=ex((e,t,n,s)=>({real:e-n,imag:t-s})),ix=vn(yo,sI,jj),qj={kernelName:yo,backendName:"cpu",kernelFunc:ix};function rI(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 gp=(e,t)=>{let n=t.value-e.value;return n===0?e.index-t.index:n};function aI(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));aI(e,t,p,h)}let r=e[t],a=n,o=s;for(v.swap(e,n,t),gp(e[s],r)>0&&v.swap(e,n,s);a<o;){for(v.swap(e,a,o),a++,o--;gp(e[a],r)<0;)a=a+1;for(;gp(e[o],r)>0;)o=o-1}gp(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 oI(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&&(aI(f,s),f=f.slice(0,s)),r&&f.sort(gp);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 iI(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 Xj="0.0.0";ol("cpu",()=>new QA,1);var lI=mt(Wa,e=>e>=0?e:Math.exp(e)-1),Kj={kernelName:Wa,backendName:"cpu",kernelFunc:lI};function uI(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;Re([r],"leakyRelu");let o=v.sizeFromShape(r.shape),i=n.data.get(r.dataId).values,l=v.getTypedArrayFromDType("float32",o);for(let c=0;c<i.length;c++)l[c]=i[c]<0?a*i[c]:i[c];return n.makeTensorInfo(r.shape,"float32",l)}var Zj={kernelName:Si,backendName:"cpu",kernelFunc:uI},Yj=Qt((e,t)=>e<0?t*e:e);function cI(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t;Re([s,r],"prelu");let a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,[i,l]=Yj(s.shape,r.shape,a,o,"float32");return n.makeTensorInfo(l,"float32",i)}var Jj={kernelName:ao,backendName:"cpu",kernelFunc:cI},dI=mt(oo,e=>Math.max(0,e)),Qj={kernelName:oo,backendName:"cpu",kernelFunc:dI},pI=mt(lo,e=>Math.min(Math.max(0,e),6)),eq={kernelName:lo,backendName:"cpu",kernelFunc:pI};function lx(e,t,n,s,r){if(n==="linear")return Br({inputs:{x:t},backend:e});if(n==="relu")return dI({inputs:{x:t},backend:e});if(n==="elu")return lI({inputs:{x:t},backend:e});if(n==="relu6")return pI({inputs:{x:t},backend:e});if(n==="prelu")return 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b=o?[g,u,p]:[g,p,u],w=i?[y,h,d]:[y,d,h],k=Pt({inputs:{x:r},backend:n,attrs:{shape:b}}),I=Pt({inputs:{x:a},backend:n,attrs:{shape:w}}),E=o?k.shape[1]:k.shape[2],R=o?k.shape[2]:k.shape[1],P=i?I.shape[1]:I.shape[2],D=Math.max(g,y),_=n.data.get(k.dataId).values,T=n.data.get(I.dataId).values,O=v.computeStrides(k.shape),W=v.computeStrides(I.shape),[X,z,j]=o?[O[0],1,O[1]]:[O[0],O[1],1],[Z,Q,ne]=i?[1,W[1],W[0]]:[W[1],1,W[0]],ae=R*P,U=ze([D,R,P],k.dtype),oe=U.values,re=n.blockSize;for(let me=0;me<D;me++)for(let Ae=0;Ae<R;Ae+=re)for(let Te=0;Te<P;Te+=re)for(let Ne=0;Ne<E;Ne+=re){let Fe=Math.min(Ae+re,R),Ue=Math.min(Te+re,P),ot=Math.min(Ne+re,E);for(let Je=Ae;Je<Fe;Je++)for(let Ze=Te;Ze<Ue;Ze++){let gt=0;for(let it=Ne;it<ot;it++){let yt=Math.min(me,g-1)*X,_t=Math.min(me,y-1)*ne,Qn=_[yt+Je*z+it*j],lt=T[it*Z+Ze*Q+_t];gt+=Qn*lt}oe[me*ae+(Je*P+Ze)]+=gt}}return n.disposeIntermediateTensorInfo(k),n.disposeIntermediateTensorInfo(I),n.makeTensorInfo(A,U.dtype,U.values)}var nq={kernelName:Da,backendName:"cpu",kernelFunc:hI};function sq(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=hI({inputs:{a:r,b:a},attrs:{transposeA:l,transposeB:c},backend:n}),o&&(h=mp({inputs:{a:p,b:o},backend:n}),m.push(p),p=h),u&&(f=lx(n,p,u,i,d),m.push(p),p=f);for(let y of m)n.disposeIntermediateTensorInfo(y);return p}var rq={kernelName:vo,backendName:"cpu",kernelFunc:sq},aq=mt(ou,e=>Math.acos(e)),oq={kernelName:ou,backendName:"cpu",kernelFunc:aq},iq=mt(iu,e=>Math.acosh(e)),lq={kernelName:iu,backendName:"cpu",kernelFunc:iq};function uq(e){let{inputs:t,backend:n}=e,s=t;Re(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 cq={kernelName:Ea,backendName:"cpu",kernelFunc:uq};function 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i=v.parseAxisParam(a,r.shape),l=i,c=N.getAxesPermutation(l,r.shape.length),u=r;c!=null&&(u=Ws({inputs:{x:r},backend:n,attrs:{perm:c}}),l=N.getInnerMostAxes(l.length,r.shape.length)),N.assertAxesAreInnerMostDims("any",l,u.shape.length);let[d,p]=N.computeOutAndReduceShapes(u.shape,l),h=v.sizeFromShape(p),f=v.makeZerosTypedArray(v.sizeFromShape(d),u.dtype),m=n.data.get(u.dataId).values;for(let y=0;y<f.length;++y){let x=y*h,A=m[x];for(let b=0;b<h;++b){let w=m[x+b];A=A||w}f[y]=A}c!=null&&n.disposeIntermediateTensorInfo(u);let g=n.makeTensorInfo(d,u.dtype,f);if(o){let y=N.expandShapeToKeepDim(d,i),x=Pt({inputs:{x:g},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(g),x}return g}var fq={kernelName:uu,backendName:"cpu",kernelFunc:hq};function mq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Re(r,"argMax");let o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Ws({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],N.assertAxesAreInnerMostDims("argMax",o,l.shape.length);let[u,d]=N.computeOutAndReduceShapes(l.shape,o),p=v.sizeFromShape(u),h=v.makeZerosTypedArray(p,"int32"),f=v.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*f,x=m[y],A=0;for(let b=0;b<f;++b){let w=m[y+b];w>x&&(x=w,A=b)}h[g]=A}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var gq={kernelName:Ra,backendName:"cpu",kernelFunc:mq};function yq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s;Re(r,"argMin");let o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Ws({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),o=[o[0]],N.assertAxesAreInnerMostDims("argMin",o,l.shape.length);let[u,d]=N.computeOutAndReduceShapes(l.shape,o),p=v.sizeFromShape(u),h=v.makeZerosTypedArray(p,"int32"),f=v.sizeFromShape(d),m=n.data.get(l.dataId).values;for(let g=0;g<h.length;++g){let y=g*f,x=m[y],A=0;for(let b=0;b<f;++b){let w=m[y+b];w<x&&(x=w,A=b)}h[g]=A}return c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(u,"int32",h)}var Aq={kernelName:cu,backendName:"cpu",kernelFunc:yq},xq=mt(du,e=>Math.asin(e)),bq={kernelName:du,backendName:"cpu",kernelFunc:xq},vq=mt(pu,e=>Math.asinh(e)),wq={kernelName:pu,backendName:"cpu",kernelFunc:vq},kq=mt(hu,e=>Math.atan(e)),Sq={kernelName:hu,backendName:"cpu",kernelFunc:kq},Iq=Qt((e,t)=>Math.atan2(e,t)),Cq=vn(mu,Iq),Tq={kernelName:mu,backendName:"cpu",kernelFunc:Cq},Nq=mt(fu,e=>Math.atanh(e)),Eq={kernelName:fu,backendName:"cpu",kernelFunc:Nq};function ux(e,t,n,s,r,a){let o=r.strideHeight,i=r.strideWidth,l=r.dilationHeight,c=r.dilationWidth,u=r.effectiveFilterHeight,d=r.effectiveFilterWidth,p=r.padInfo.top,h=r.padInfo.left,f=a==="max"?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,m=ze(r.outShape,n),g=m.values,y=r.outShape[1]*r.outShape[2]*r.outShape[3],x=r.outShape[2]*r.outShape[3],A=r.outShape[3];for(let b=0;b<r.batchSize;++b){let w=b*y,k=b*s[0];for(let I=0;I<r.inChannels;++I)for(let E=0;E<r.outHeight;++E){let R=E*o-p,P=Math.max(0,R),D=Math.min(r.inHeight,u+R),_=w+E*x;for(let T=0;T<r.outWidth;++T){let O=T*i-h,W=Math.max(0,O),X=Math.min(r.inWidth,d+O),z=f,j=0,Z=0;for(let ne=P;ne<D;ne+=l){let ae=k+ne*s[1];for(let U=W;U<X;U+=c){let oe=ae+U*s[2],re=e[oe+I];a==="max"&&re>z?z=re:a==="avg"&&(j+=re,Z++)}if(isNaN(z))break}let Q=_+T*A+I;g[Q]=a==="avg"?j/Z:z}}}return m}function fI(e,t,n,s,r=!1,a=!1){let o=ze(s.outShape,"int32"),i=s.strideHeight,l=s.strideWidth,c=s.dilationHeight,u=s.dilationWidth,d=s.effectiveFilterHeight,p=s.effectiveFilterWidth,h=s.padInfo.top,f=s.padInfo.left,m=ze(t,n,e);for(let g=0;g<s.batchSize;++g)for(let y=0;y<s.inChannels;++y)for(let x=0;x<s.outHeight;++x){let A=x*i-h,b=A;for(;b<0;)b+=c;let w=Math.min(s.inHeight,d+A);for(let k=0;k<s.outWidth;++k){let I=k*l-f,E=I;for(;E<0;)E+=u;let R=Math.min(s.inWidth,p+I),P=Number.NEGATIVE_INFINITY,D=-1;for(let _=b;_<w;_+=c){let T=_-A;for(let O=E;O<R;O+=u){let 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oe=U*l-y,re=oe;for(;re<0;)re+=d;let me=Math.min(r.inWidth,f+oe),Ae=ae+U*E,Te=x,Ne=0,Fe=0;for(let ot=W;ot<X;ot+=c){let Je=D+ot*s[1];for(let Ze=Q;Ze<ne;Ze+=u){let gt=Je+Ze*s[2];for(let it=re;it<me;it+=d){let yt=gt+it*s[3],_t=e[yt+_];if(a==="max"&&_t>Te?Te=_t:a==="avg"&&(Ne+=_t,Fe++),isNaN(Te))break}if(isNaN(Te))break}if(isNaN(Te))break}let Ue=Ae+_;b[Ue]=a==="avg"?Ne/Fe:Te}}}}return A}function Rq(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 k=w*r-h,I=k;for(;I<0;)I+=i;let E=Math.min(t.inHeight,u+k);for(let R=0;R<t.outWidth;++R){let P=R*a-f,D=P;for(;D<0;)D+=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,k=u.effectiveFilterWidth,I=b-1-u.padInfo.front,E=k-1-u.padInfo.left,R=w-1-u.padInfo.top,P=ze(a.shape,"float32"),D=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 W=0;W<u.inDepth;++W)for(let X=0;X<u.inHeight;++X)for(let z=0;z<u.inWidth;++z){let j=W-I,Z=X-R,Q=z-E,ne=0;for(let ae=0;ae<b;ae+=y){let U=(j+ae)/d;if(!(U<0||U>=u.outDepth||Math.floor(U)!==U))for(let oe=0;oe<w;oe+=x){let re=(Z+oe)/p;if(!(re<0||re>=u.outHeight||Math.floor(re)!==re))for(let me=0;me<k;me+=A){let Ae=(Q+me)/h;if(Ae<0||Ae>=u.outWidth||Math.floor(Ae)!==Ae)continue;ne+=_.get(T,U,re,Ae,O)}}}P.set(ne*D,T,W,X,z,O)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var Oq={kernelName:Dh,backendName:"cpu",kernelFunc:Pq};function Mq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;Re([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"),k=1/(h*f),I=n.data.get(r.dataId).values,E=ze(r.shape,"float32",I);for(let R=0;R<u.batchSize;++R)for(let P=0;P<u.inChannels;++P)for(let D=0;D<u.inHeight;++D)for(let _=0;_<u.inWidth;++_){let T=D-b,O=_-A,W=0;for(let X=0;X<y;X+=m){let z=(T+X)/d;if(!(z<0||z>=u.outHeight||Math.floor(z)!==z))for(let j=0;j<x;j+=g){let Z=(O+j)/p;if(Z<0||Z>=u.outWidth||Math.floor(Z)!==Z)continue;W+=E.get(R,z,Z,P)}}w.set(W*k,R,D,_,P)}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var zq={kernelName:_h,backendName:"cpu",kernelFunc:Mq};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var Bq={kernelName:Ha,backendName:"cpu",kernelFunc:Lq};function Wq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;Re([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=Sl({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var Vq={kernelName:pi,backendName:"cpu",kernelFunc:Wq};function Uq(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=tx(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var Gq={kernelName:$h,backendName:"cpu",kernelFunc:Uq};function Hq(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=N.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var jq={kernelName:Fh,backendName:"cpu",kernelFunc:Hq},qq=mt(Xr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),Xq={kernelName:Xr,backendName:"cpu",kernelFunc:qq},Kq=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let c=0;c<i.length;c++){let u=i[c],d=l[c];s[c]=Math.hypot(u,d)}return n.makeOutput(s,t.shape,"float32")},Zq={kernelName:cd,backendName:"cpu",kernelFunc:Kq};function ac(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.data.get(s.dataId).complexTensorInfos.imag,a=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,a)}var Yq={kernelName:fd,backendName:"cpu",kernelFunc:ac};function oc(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(m=>m.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(m=>v.sizeFromShape(m.shape)>0);if(i.length===1)return Br({inputs:{x:i[0]},backend:n});let l=i.map(m=>m.shape);if(N.assertParamsConsistent(l,a),i[0].dtype==="complex64"){let m=i.map(b=>kl({inputs:{input:b},backend:n})),g=i.map(b=>ac({inputs:{input:b},backend:n})),y=oc({inputs:m,backend:n,attrs:{axis:a}}),x=oc({inputs:g,backend:n,attrs:{axis:a}}),A=ks({inputs:{real:y,imag:x},backend:n});return m.forEach(b=>n.disposeIntermediateTensorInfo(b)),g.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(x),A}let c=i.map(m=>{let g=v.sizeFromShape(m.shape.slice(a));return Pt({inputs:{x:m},backend:n,attrs:{shape:[-1,g]}})}),u=c.map(m=>({vals:n.data.get(m.dataId).values,shape:m.shape}));o=N.computeOutShape(c.map(m=>m.shape),1);let d=c[0].shape[0]===1,p=nx(u,o,t[0].dtype,d),h=N.computeOutShape(i.map(m=>m.shape),a),f=n.makeTensorInfo(h,t[0].dtype,p);return c.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var Jq={kernelName:hi,backendName:"cpu",kernelFunc:oc};function gI(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;Re([r,a],"conv2d");let d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(r.shape,a.shape,o,c,i,u,!1,d),h=p.filterHeight,f=p.filterWidth,m=p.dilationHeight,g=p.dilationWidth,y=p.padInfo.left,x=p.padInfo.top,A=p.dataFormat==="channelsLast",b=new sn(p.outShape,r.dtype),w=v.computeStrides(r.shape),k=v.computeStrides(a.shape),I=w[0],E=A?w[1]:w[2],R=A?w[2]:1,P=A?1:w[1],D=b.strides[0],_=A?b.strides[1]:b.strides[2],T=A?b.strides[2]:1,O=A?1:b.strides[1],W=n.data.get(r.dataId).values,X=n.data.get(a.dataId).values,z=b.values;for(let j=0;j<p.batchSize;++j){let Z=j*I,Q=j*D;for(let ne=0;ne<p.outHeight;++ne){let ae=Q+ne*_,U=ne*p.strideHeight-x;for(let oe=0;oe<h;++oe){let re=U+oe*m;if(re<0||re>=p.inHeight)continue;let me=oe*k[0],Ae=Z+re*E;for(let Te=0;Te<p.outWidth;++Te){let Ne=ae+Te*T,Fe=Te*p.strideWidth-y;for(let Ue=0;Ue<f;++Ue){let ot=Fe+Ue*g;if(ot<0||ot>=p.inWidth)continue;let Je=me+Ue*k[1],Ze=Ae+ot*R,gt=Je;for(let it=0;it<p.inChannels;++it){let yt=W[Ze+it*P];for(let _t=0;_t<p.outChannels;++_t)z[Ne+_t*O]+=yt*X[gt+_t];gt+=p.outChannels}}}}}}return n.makeTensorInfo(b.shape,b.dtype,z)}var Qq={kernelName:Pa,backendName:"cpu",kernelFunc:gI};function eX(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;Re([r,a],"conv2dBackpropFilter");let d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=p,y=p.dataFormat==="channelsLast",x=new sn(p.filterShape,"float32"),A=p.padInfo.left,b=p.padInfo.top,w=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,I=new sn(r.shape,r.dtype,w),E=new sn(a.shape,a.dtype,k);for(let R=0;R<m;++R){let P=Math.max(0,Math.ceil((b-R)/h)),D=Math.min(p.outHeight,(p.inHeight+b-R)/h);for(let _=0;_<g;++_){let T=Math.max(0,Math.ceil((A-_)/f)),O=Math.min(p.outWidth,(p.inWidth+A-_)/f);for(let W=0;W<p.inChannels;++W)for(let X=0;X<p.outChannels;++X){let z=0;for(let j=0;j<p.batchSize;++j)for(let Z=P;Z<D;++Z){let Q=R+Z*h-b;for(let ne=T;ne<O;++ne){let ae=_+ne*f-A;y?z+=I.get(j,Q,ae,W)*E.get(j,Z,ne,X):z+=I.get(j,W,Q,ae)*E.get(j,X,Z,ne)}}x.set(z,R,_,W,X)}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var tX={kernelName:Ph,backendName:"cpu",kernelFunc:eX};function nX(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;Re([r,a],"conv2dBackpropInput");let d=v.computeStrides(a.shape),p=v.computeStrides(r.shape),h=N.convertConv2DDataFormat(c),f=N.computeConv2DInfo(o,a.shape,i,1,l,u,!1,h),m=new sn(f.inShape,"float32"),g=m.values,y=n.data.get(r.dataId).values,x=n.data.get(a.dataId).values,[A,b,w]=d,{batchSize:k,filterHeight:I,filterWidth:E,inChannels:R,inHeight:P,inWidth:D,outChannels:_,outHeight:T,outWidth:O,strideHeight:W,strideWidth:X}=f;h=f.dataFormat;let z=I-1-f.padInfo.top,j=E-1-f.padInfo.left,Z=h==="channelsLast",Q=m.strides[0],ne=Z?m.strides[1]:m.strides[2],ae=Z?m.strides[2]:1,U=Z?1:m.strides[1],oe=p[0],re=Z?p[1]:p[2],me=Z?p[2]:1,Ae=Z?1:p[1];for(let Te=0;Te<k;++Te)for(let Ne=0;Ne<R;++Ne)for(let Fe=0;Fe<P;++Fe){let Ue=Fe-z,ot=Math.max(0,Math.ceil(Ue/W)),Je=Math.min(T,(I+Ue)/W);for(let Ze=0;Ze<D;++Ze){let gt=Ze-j,it=Math.max(0,Math.ceil(gt/X)),yt=Math.min(O,(E+gt)/X),_t=0;for(let lt=ot;lt<Je;++lt){let ms=lt*W-Ue;for(let kn=it;kn<yt;++kn){let qs=kn*X-gt,es=oe*Te+re*lt+me*kn,gs=A*(I-1-ms)+b*(E-1-qs)+w*Ne;for(let Es=0;Es<_;++Es){let Xs=y[es+Ae*Es],Rs=x[gs+Es];_t+=Xs*Rs}}}let Qn=Q*Te+ne*Fe+ae*Ze+U*Ne;g[Qn]=_t}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var sX={kernelName:Oa,backendName:"cpu",kernelFunc:nX};function rX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s;Re([r,a],"conv3d");let c=N.computeConv3DInfo(r.shape,a.shape,o,l,i),{filterDepth:u,filterHeight:d,filterWidth:p,dilationDepth:h,dilationHeight:f,dilationWidth:m,padInfo:g}=c,y=g.front,x=g.left,A=g.top,b=new sn(c.outShape,r.dtype),w=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,I=b.values,E=v.computeStrides(r.shape),R=v.computeStrides(a.shape);for(let P=0;P<c.batchSize;++P){let D=P*E[0],_=P*b.strides[0];for(let T=0;T<c.outDepth;++T){let O=_+T*b.strides[1],W=T*c.strideDepth-y;for(let X=0;X<u;++X){let z=W+X*h;if(z<0||z>=c.inDepth)continue;let j=X*R[0],Z=D+z*E[1];for(let Q=0;Q<c.outHeight;++Q){let ne=O+Q*b.strides[2],ae=Q*c.strideHeight-A;for(let U=0;U<d;++U){let oe=ae+U*f;if(oe<0||oe>=c.inHeight)continue;let re=j+U*R[1],me=Z+oe*E[2];for(let Ae=0;Ae<c.outWidth;++Ae){let Te=ne+Ae*c.outChannels,Ne=Ae*c.strideWidth-x;for(let Fe=0;Fe<p;++Fe){let Ue=Ne+Fe*m;if(Ue<0||Ue>=c.inWidth)continue;let ot=re+Fe*R[2],Je=me+Ue*c.inChannels,Ze=ot;for(let gt=0;gt<c.inChannels;++gt){let it=w[Je+gt];for(let yt=0;yt<c.outChannels;++yt)I[Te+yt]+=it*k[Ze+yt];Ze+=c.outChannels}}}}}}}}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var aX={kernelName:dd,backendName:"cpu",kernelFunc:rX};function oX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s;Re([r,a],"conv3dBackpropFilterV2");let c=v.computeStrides(r.shape),u=v.computeStrides(a.shape),d=N.computeConv3DInfo(r.shape,l,o,1,i),p=d.strideDepth,h=d.strideHeight,f=d.strideWidth,m=d.filterDepth,g=d.filterHeight,y=d.filterWidth,x=new sn(d.filterShape,"float32"),A=x.values,[b,w,k,I]=x.strides,E=n.data.get(a.dataId).values,[R,P,D,_]=u,T=n.data.get(r.dataId).values,[O,W,X,z]=c,j=d.padInfo.front,Z=d.padInfo.left,Q=d.padInfo.top;for(let ne=0;ne<m;++ne){let ae=Math.max(0,Math.ceil((j-ne)/p)),U=Math.min(d.outDepth,(d.inDepth+j-ne)/p),oe=ne*b;for(let re=0;re<g;++re){let me=Math.max(0,Math.ceil((Q-re)/h)),Ae=Math.min(d.outHeight,(d.inHeight+Q-re)/h),Te=re*w+oe;for(let Ne=0;Ne<y;++Ne){let Fe=Math.max(0,Math.ceil((Z-Ne)/f)),Ue=Math.min(d.outWidth,(d.inWidth+Z-Ne)/f),ot=Ne*k+Te;for(let Je=0;Je<d.inChannels;++Je){let Ze=Je*I+ot;for(let gt=0;gt<d.outChannels;++gt){let it=0;for(let yt=0;yt<d.batchSize;++yt){let _t=yt*O,Qn=yt*R;for(let lt=ae;lt<U;++lt){let kn=(ne+lt*p-j)*W+_t,qs=lt*P+Qn;for(let es=me;es<Ae;++es){let Es=(re+es*h-Q)*X+kn,Xs=es*D+qs;for(let Rs=Fe;Rs<Ue;++Rs){let ya=(Ne+Rs*f-Z)*z+Es,En=Rs*_+Xs;it+=T[ya+Je]*E[En+gt]}}}}A[Ze+gt]=it}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var iX={kernelName:Oh,backendName:"cpu",kernelFunc:oX};function lX(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s;Re([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,k]=c,I=n.data.get(a.dataId).values,[E,R,P,D]=u,{batchSize:_,filterDepth:T,filterHeight:O,filterWidth:W,inChannels:X,inDepth:z,inHeight:j,inWidth:Z,outChannels:Q,outDepth:ne,outHeight:ae,outWidth:U,strideDepth:oe,strideHeight:re,strideWidth:me}=d,Ae=T-1-d.padInfo.front,Te=O-1-d.padInfo.top,Ne=W-1-d.padInfo.left;for(let Fe=0;Fe<_;++Fe)for(let Ue=0;Ue<X;++Ue)for(let ot=0;ot<z;++ot){let Je=ot-Ae,Ze=Math.max(0,Math.ceil(Je/oe)),gt=Math.min(ne,(T+Je)/oe);for(let it=0;it<j;++it){let yt=it-Te,_t=Math.max(0,Math.ceil(yt/re)),Qn=Math.min(ae,(O+yt)/re);for(let lt=0;lt<Z;++lt){let ms=lt-Ne,kn=Math.max(0,Math.ceil(ms/me)),qs=Math.min(U,(W+ms)/me),es=0;for(let gs=Ze;gs<gt;++gs){let Es=gs*oe-Je;for(let Xs=_t;Xs<Qn;++Xs){let Rs=Xs*re-yt;for(let ts=kn;ts<qs;++ts){let ya=ts*me-ms,En=A*Fe+b*gs+w*Xs+k*ts,Aa=E*(T-1-Es)+R*(O-1-Rs)+P*(W-1-ya)+D*Ue;for(let _s=0;_s<Q;++_s){let Wc=x[En+_s],Vc=I[Aa+_s];es+=Wc*Vc}}}}h[f*Fe+m*ot+g*it+y*lt+Ue]=es}}}return n.makeTensorInfo(p.shape,p.dtype,p.values)}var uX={kernelName:Mh,backendName:"cpu",kernelFunc:lX},cX=mt(Ma,e=>Math.cos(e)),dX={kernelName:Ma,backendName:"cpu",kernelFunc:cX},pX=mt(za,e=>Math.cosh(e)),hX={kernelName:za,backendName:"cpu",kernelFunc:pX};function fX(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),k=v.computeStrides(y.shape);for(let I=0;I<f;I++){let E=I*4,R=x[E],P=x[E+1],D=x[E+2],_=x[E+3],T=A[I];if(T>=u)continue;let O=m>1?(D-R)*(d-1)/(m-1):0,W=g>1?(_-P)*(p-1)/(g-1):0;for(let X=0;X<m;X++){let z=m>1?R*(d-1)+X*O:.5*(R+D)*(d-1);if(z<0||z>d-1){for(let j=0;j<g;j++)for(let Z=0;Z<h;Z++){let Q=Z+j*k[2]+X*k[1]+I*k[0];y.values[Q]=c}continue}if(l==="bilinear"){let j=Math.floor(z),Z=Math.ceil(z),Q=z-j;for(let ne=0;ne<g;ne++){let ae=g>1?P*(p-1)+ne*W:.5*(P+_)*(p-1);if(ae<0||ae>p-1){for(let me=0;me<h;me++){let Ae=me+ne*k[2]+X*k[1]+I*k[0];y.values[Ae]=c}continue}let U=Math.floor(ae),oe=Math.ceil(ae),re=ae-U;for(let me=0;me<h;me++){let Ae=me+U*w[2]+j*w[1]+T*w[0],Te=b[Ae];Ae=me+oe*w[2]+j*w[1]+T*w[0];let Ne=b[Ae];Ae=me+U*w[2]+Z*w[1]+T*w[0];let Fe=b[Ae];Ae=me+oe*w[2]+Z*w[1]+T*w[0];let Ue=b[Ae],ot=Te+(Ne-Te)*re,Je=Fe+(Ue-Fe)*re;Ae=me+ne*k[2]+X*k[1]+I*k[0],y.values[Ae]=ot+(Je-ot)*Q}}}else for(let j=0;j<g;++j){let Z=g>1?P*(p-1)+j*W:.5*(P+_)*(p-1);if(Z<0||Z>p-1){for(let ae=0;ae<h;ae++){let U=ae+j*k[2]+X*k[1]+I*k[0];y.values[U]=c}continue}let Q=Math.round(Z),ne=Math.round(z);for(let ae=0;ae<h;ae++){let U=ae+Q*w[2]+ne*w[1]+T*w[0],oe=ae+j*k[2]+X*k[1]+I*k[0];y.values[oe]=b[U]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var mX={kernelName:mi,backendName:"cpu",kernelFunc:fX};function gX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;Re(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=${u}`);let d=zn(c.dtype,"int32"),p=v.makeZerosTypedArray(v.sizeFromShape(c.shape),d),h=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=i?(y,x)=>y+f-x-1:(y,x)=>y+x;for(let y=0;y<h.length;y+=f)for(let x=0;x<f;x++){let A=m(y,x);if(x===0)p[A]=o?0:h[A];else{let b=m(y,x-1);p[A]=o?h[b]+p[b]:h[A]+p[b]}}let g=n.makeTensorInfo(c.shape,d,p);if(l!=null){let y=N.getUndoAxesPermutation(l),x=Ws({inputs:{x:g},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),x}return g}var yX={kernelName:fi,backendName:"cpu",kernelFunc:gX};function AX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=tx(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=C7(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var xX={kernelName:zh,backendName:"cpu",kernelFunc:AX};function bX(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. Got ${o}`);let i=r.shape[0],l=r.shape[1],c=r.shape[2],u=r.shape[3],d=l*a,p=c*a,h=u/(a*a),f=n.data.get(r.dataId).values,m=new Float32Array(i*d*p*h),g=0;for(let y=0;y<i;++y)for(let x=0;x<d;++x){let A=Math.floor(x/a),b=x%a;for(let w=0;w<p;++w){let k=Math.floor(w/a),I=w%a,E=(b*a+I)*h;for(let R=0;R<h;++R){let D=R+E+u*(k+c*(A+l*y));m[g++]=f[D]}}}return n.makeTensorInfo([i,d,p,h],r.dtype,m)}var vX={kernelName:gi,backendName:"cpu",kernelFunc:bX};function yI(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s;Re([r,a],"depthwiseConv2DNative");let u=v.computeStrides(r.shape),d=v.computeStrides(a.shape),p=l;p==null&&(p=[1,1]),v.assert(N.eitherStridesOrDilationsAreOne(o,p),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. 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,k=new sn(h.outShape,r.dtype),I=n.data.get(r.dataId).values,E=n.data.get(a.dataId).values,R=k.values;for(let P=0;P<h.batchSize;++P){let D=P*u[0],_=P*k.strides[0];for(let T=0;T<h.outHeight;++T){let O=_+T*k.strides[1],W=T*h.strideHeight-b;for(let X=0;X<f;++X){let z=W+X*g;if(z<0||z>=h.inHeight)continue;let j=X*d[0],Z=D+z*u[1];for(let Q=0;Q<h.outWidth;++Q){let ne=O+Q*k.strides[2],ae=Q*h.strideWidth-A;for(let U=0;U<m;++U){let oe=ae+U*y;if(oe<0||oe>=h.inWidth)continue;let re=j+U*d[1],me=Z+oe*h.inChannels,Ae=ne,Te=re;for(let Ne=0;Ne<h.inChannels;++Ne){let Fe=I[me+Ne];for(let Ue=0;Ue<w;++Ue)R[Ae+Ue]+=Fe*E[Te+Ue];Ae+=w,Te+=w}}}}}}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var wX={kernelName:La,backendName:"cpu",kernelFunc:yI};function kX(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;Re([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),k=n.data.get(a.dataId).values,I=new sn(a.shape,a.dtype,k);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 D=0;D<m;++D){let _=Math.max(0,Math.ceil((y-D)/h)),T=Math.min(d.outWidth,(d.inWidth+y-D)/h);for(let O=0;O<d.outChannels;++O){let W=Math.trunc(O/A),X=O%A,z=0;for(let j=0;j<d.batchSize;++j)for(let Z=R;Z<P;++Z){let Q=E+Z*p-x;for(let ne=_;ne<T;++ne){let ae=D+ne*h-y;z+=w.get(j,Q,ae,W)*I.get(j,Z,ne,O)}}g.set(z,E,D,W,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var SX={kernelName:Lh,backendName:"cpu",kernelFunc:kX};function IX(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;Re([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,k]=d,I=n.data.get(a.dataId).values,[E,R,P]=p,{batchSize:D,filterHeight:_,filterWidth:T,inChannels:O,inHeight:W,inWidth:X,outChannels:z,outHeight:j,outWidth:Z,strideHeight:Q,strideWidth:ne}=h,ae=_-1-h.padInfo.top,U=T-1-h.padInfo.left,oe=z/O;for(let re=0;re<D;++re)for(let me=0;me<O;++me)for(let Ae=0;Ae<W;++Ae){let Te=Ae-ae,Ne=Math.max(0,Math.ceil(Te/Q)),Fe=Math.min(j,(_+Te)/Q);for(let Ue=0;Ue<X;++Ue){let ot=Ue-U,Je=Math.max(0,Math.ceil(ot/ne)),Ze=Math.min(Z,(T+ot)/ne),gt=0;for(let it=Ne;it<Fe;++it){let yt=it*Q-Te;for(let _t=Je;_t<Ze;++_t){let Qn=_t*ne-ot,lt=b*re+w*it+k*_t,ms=E*(_-1-yt)+R*(T-1-Qn)+P*me;for(let kn=0;kn<oe;++kn){let qs=me*oe+kn,es=A[lt+qs],gs=I[ms+kn];gt+=es*gs}}}m[g*re+y*Ae+x*Ue+me]=gt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var CX={kernelName:Bh,backendName:"cpu",kernelFunc:IX};function TX(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 NX={kernelName:Wh,backendName:"cpu",kernelFunc:TX},EX={kernelName:pd,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:k,filterWidth:I,dilationHeight:E,dilationWidth:R,outShape:P}=N.computeDilation2DInfo(s.shape,r.shape,a,o,"NHWC",i),D=v.sizeFromShape(P),_=P.length,T=v.getArrayFromDType(s.dtype,D);for(let W=0;W<h;++W)for(let X=0;X<y;++X){let z=X*b-A.top;for(let j=0;j<x;++j){let Z=j*w-A.left;for(let Q=0;Q<g;++Q){let ne=Number.MIN_SAFE_INTEGER;for(let U=0;U<k;++U){let oe=z+U*E;if(oe>=0&&oe<f)for(let re=0;re<I;++re){let me=Z+re*R;if(me>=0&&me<m){let Ae=v.locToIndex([W,oe,me,Q],u,v.computeStrides(s.shape)),Te=v.locToIndex([U,re,Q],p,v.computeStrides(r.shape)),Ne=c[Ae]+d[Te];Ne>ne&&(ne=Ne)}}}let 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|
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
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|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
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|
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
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|
${o.shape}`);let i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=n.data.get(o.dataId).values[0],[d,p,h,f,m]=Z7(i,s.shape,s.dtype,l,r.dtype,c,u);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var iY={kernelName:xd,backendName:"cpu",kernelFunc:oY};function lY(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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${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]=Y7(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var uY={kernelName:_u,backendName:"cpu",kernelFunc:lY};function cY(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]=ox(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var dY={kernelName:bd,backendName:"cpu",kernelFunc:cY};function pY(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]=ox(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var hY={kernelName:vd,backendName:"cpu",kernelFunc:pY};function fY(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=SI(f,m,i,p,u,c,l,d,g,h);return n.makeTensorInfo(i,y.dtype,y.values)}var mY={kernelName:wd,backendName:"cpu",kernelFunc:fY};function gY(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",()=>Ax(2)?2:Ax(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",()=>jI($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>qI($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=$e.getNumber("WEBGL_VERSION");return e===0?0:XI(e)});$e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>$e.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Pu.isMobile());$e.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>KI($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>$e.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:$e.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));$e.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>ZI($e.getNumber("WEBGL_VERSION")));$e.registerFlag("WEBGL_FENCE_API_ENABLED",()=>YI($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",()=>Pu.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 Nl(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 jm(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}; ${i};`}).join("")}function hJ(e,t){let n=e.length,s=e.map(a=>`${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 fJ(e,t,n="index"){let s=e.map((a,o)=>o),r=hJ(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 bx(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 vx(){return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
|
|
}
|
|
`}var JI=`
|
|
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:QI}=N;function mJ(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}=wx(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?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
|
|
`),a=e.map(h=>gJ(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
|
|
`),o=t.texShape,i=Gn(),l=xJ(i),c,u,d=wJ(i);return t.isPacked?(c=yJ(t.logicalShape,o,n.enableShapeUniforms),u=vJ(i)):(c=AJ(t.logicalShape,o,n.enableShapeUniforms),u=bJ(i)),n.packedInputs&&(d+=CJ),[d,l,u,r,c,a,n.userCode].join(`
|
|
`)}function uc(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return zJ(e,t);case 1:return BJ(e,t);case 2:return VJ(e,t);case 3:return GJ(e,t);case 4:return jJ(e,t);case 5:return qJ(e);case 6:return XJ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function eC(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return MJ(e);case 1:return LJ(e,t);case 2:return WJ(e,t);case 3:return UJ(e,t);default:return HJ(e,t)}}function gJ(e,t,n=!1,s){let r="";n?r+=eC(e,s):r+=uc(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=KJ(e,t):r+=ZJ(e,t)),r}function yJ(e,t,n){switch(e.length){case 0:return tC();case 1:return TJ(e,t,n);case 2:return PJ(e,t,n);case 3:return EJ(e,t,n);default:return _J(e,t,n)}}function AJ(e,t,n){switch(e.length){case 0:return tC();case 1:return NJ(e,t,n);case 2:return OJ(e,t,n);case 3:return RJ(e,t,n);case 4:return DJ(e,t,n);case 5:return $J(e,t);case 6:return FJ(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function xJ(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function bJ(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function vJ(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function wJ(e){return`${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);
|
|
}
|
|
|
|
${kJ}
|
|
${SJ}
|
|
${IJ}
|
|
`}var kJ=`
|
|
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);
|
|
}
|
|
`,SJ=`
|
|
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);
|
|
}
|
|
`,IJ=`
|
|
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);
|
|
}
|
|
`,CJ=`
|
|
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 tC(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function TJ(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 NJ(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 * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function EJ(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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 RJ(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;
|
|
${jm(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let s=Nl(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function _J(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let c=2;c<e.length-1;c++)o*=e[e.length-c-1],i=`
|
|
int b${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 DJ(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;
|
|
${jm(["r","c","d","d2"],e)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let s=Nl(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function $J(e,t){let n=Nl(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function FJ(e,t){let n=Nl(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function PJ(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${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, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function OJ(e,t,n){return v.arraysEqual(e,t)?n?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${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 * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function El(e){return`offset${e}`}function MJ(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 zJ(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=El(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 LJ(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 BJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int index) {
|
|
${cc(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=El(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 WJ(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 VJ(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=dc(e,l),h=["row","col"];return`
|
|
${uc(p,t)}
|
|
float ${r}(int row, int col) {
|
|
return ${r}(${pc(h,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
|
|
${cc(e)}
|
|
}
|
|
`;let c=a[0],u=a[1],d=El(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 UJ(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=dc(e,p),m=["b","row","col"];return`
|
|
${eC(f,t)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${pc(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 GJ(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=dc(e,c),g=["row","col","depth"];return`
|
|
${uc(m,t)}
|
|
float ${r}(int row, int col, int depth) {
|
|
return ${r}(${pc(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)));
|
|
${cc(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=El(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 HJ(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 jJ(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=dc(e,l),A=["row","col","depth","depth2"];return`
|
|
${uc(x,t)}
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
return ${r}(${pc(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)));
|
|
${cc(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=El(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 qJ(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=dc(e,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${uc(m)}
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${s}(${pc(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;
|
|
${cc(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=El(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 XJ(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=dc(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${uc(g)}
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${s}(${pc(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)));
|
|
${cc(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=El(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 cc(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 ${t}[i];
|
|
}
|
|
}
|
|
`}function KJ(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=QI(e.shapeInfo.logicalShape,t.logicalShape),l=vt(o),c=o-a,u,d=["x","y","z","w","u","v"];a===0?u="":o<2&&i.length>=1?u="coords = 0;":u=i.map(x=>`coords.${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 ZJ(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=QI(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 ${e} is not yet supported`)}function wx(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function dc(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function pc(e,t){return t.map(n=>e[n]).join(", ")}function YJ(e,t,n,s){let r=n.map((b,w)=>{let k={logicalShape:b.shape,texShape:b.isUniform?null:b.texData.texShape,isUniform:b.isUniform,isPacked:b.isUniform?!1:b.texData.isPacked,flatOffset:null};return b.texData!=null&&b.texData.slice!=null&&b.texData.slice.flatOffset>0&&(k.flatOffset=b.texData.slice.flatOffset),{name:t.variableNames[w],shapeInfo:k}}),a=r.map(b=>b.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=mJ(r,o,t),l=_I(e.gl,i),c=e.createProgram(l),u=null,d=e.getUniformLocation(c,"NAN",!1);Y().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let p=!1,h={},f={},m={};for(let b=0;b<t.variableNames.length;b++){let w=t.variableNames[b];h[w]=e.getUniformLocation(c,w,p),h[`offset${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 nC(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 ${l} must match`)})}function JJ(e,t,n,s,r){t.program.enableShapeUniforms||(nC(t.inShapeInfos,n),nC([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,c)=>{let u=t.program.variableNames[c],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`],h=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(h){let{uniformShape:m}=wx(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 ${l.type} is not supported yet.`)}),e.executeProgram()}function QJ(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:c,uniformShape:u,keptDims:d}=wx(e.packedInputs,o.shape,l),p="",h="",f="";if(u.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${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 eQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=xp.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?jm(["r","c","d"],e):Nl(["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;
|
|
}
|
|
`}},tQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=xp.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?jm(["r","c","d"],e):Nl(["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;
|
|
}
|
|
`}},nQ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Vs.DOWNLOAD;let t=Gn();this.outputShape=e,this.userCode=`
|
|
${JI}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},sQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Vs.DOWNLOAD;let t=Gn();this.outputShape=e,this.userCode=`
|
|
${JI}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},rQ=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?vx():bx(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.);
|
|
}
|
|
`}},aQ=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?vx():bx(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${s}
|
|
|
|
${n.output} = ${r};
|
|
}
|
|
`}},sC={};Me(sC,{bindVertexProgramAttributeStreams:()=>pC,createBufferFromOutputTexture:()=>mC,createFloat16MatrixTexture:()=>lC,createFloat16PackedMatrixTexture:()=>dC,createFloat32MatrixTexture:()=>iC,createIndexBuffer:()=>oC,createPackedMatrixTexture:()=>cC,createUnsignedBytesMatrixTexture:()=>uC,createVertexBuffer:()=>aC,createVertexShader:()=>rC,downloadByteEncodedFloatMatrixFromOutputTexture:()=>yC,downloadFloat32MatrixFromBuffer:()=>gC,downloadMatrixFromPackedOutputTexture:()=>xC,downloadPackedMatrixFromBuffer:()=>AC,getInternalFormatForFloat16MatrixTexture:()=>Sx,getInternalFormatForFloat16PackedMatrixTexture:()=>Tx,getInternalFormatForFloat32MatrixTexture:()=>kx,getInternalFormatForPackedMatrixTexture:()=>Cx,getInternalFormatForUnsignedBytesMatrixTexture:()=>Ix,uploadDenseMatrixToTexture:()=>hC,uploadPixelDataToTexture:()=>fC});function rC(e){let t=Gn(),n=`${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 RI(e,n)}function aC(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 FI(e,t)}function oC(e){let t=new Uint16Array([0,1,2,2,1,3]);return PI(e,t)}function Sp(e,t,n,s,r,a){MI(t,n);let o=OI(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 kx(e){return e.internalFormatFloat}function iC(e,t,n,s){let[r,a]=bp(t,n);return Sp(e,r,a,kx(s),s.textureFormatFloat,e.FLOAT)}function Sx(e){return e.internalFormatHalfFloat}function lC(e,t,n,s){let[r,a]=bp(t,n);return Sp(e,r,a,Sx(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function Ix(e){return e.downloadTextureFormat}function uC(e,t,n,s){let[r,a]=bp(t,n);return Sp(e,r,a,Ix(s),e.RGBA,e.UNSIGNED_BYTE)}function Cx(e){return e.internalFormatPackedFloat}function cC(e,t,n,s){let[r,a]=ic(t,n);return Sp(e,r,a,Cx(s),e.RGBA,e.FLOAT)}function Tx(e){return e.internalFormatPackedHalfFloat}function dC(e,t,n,s){let[r,a]=ic(t,n);return Sp(e,r,a,Tx(s),e.RGBA,s.textureTypeHalfFloat)}function pC(e,t,n){let s=0,r=3*4,a=3*4+2*4;return ke(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),gx(e,t,"clipSpacePos",n,3,a,s)&&gx(e,t,"uv",n,2,a,r)}function hC(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 fC(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 mC(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 gC(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 yC(e,t,n,s){let[r,a]=bp(t,n),o=4,i=new Uint8Array(eJ(t*n,o));return ke(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function AC(e,t,n,s,r,a,o,i){let l=e,c=new Float32Array(tJ(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 xC(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 qm=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,zm(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),Us(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),Us(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",Us(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Us(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=aC(this.gl),this.indexBuffer=oC(this.gl),this.framebuffer=zI(this.gl),this.textureConfig=mx(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(),iC(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),lC(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),uC(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),fC(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),hC(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),dC(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),cC(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(yx(this.gl,this.framebuffer),this.outputTexture=null),ke(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>yC(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return AC(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return gC(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=mC(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,()=>xC(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=rC(t));let n=DI(t);return ke(t,()=>t.attachShader(n,this.vertexShader)),ke(t,()=>t.attachShader(n,e)),$I(t,n),this.debug&&Bm(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=pC(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&&Bm(this.gl,this.program),ke(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?BI(this.gl,e,t):WI(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(),VI(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=ic(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&&Bm(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=oQ(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(),Wm(this.gl,e,this.framebuffer),this.debug&&wp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Wm(this.gl,this.outputTexture,this.framebuffer),this.debug&&wp(this.gl)):yx(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;Wm(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 oQ(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:iQ,bincountImpl:bC,bincountReduceImpl:lQ,ceilImpl:uQ,concatImpl:cQ,equalImpl:dQ,expImpl:pQ,expm1Impl:hQ,floorImpl:fQ,gatherNdImpl:mQ,gatherV2Impl:gQ,greaterImpl:yQ,greaterEqualImpl:AQ,lessImpl:xQ,lessEqualImpl:bQ,linSpaceImpl:vQ,logImpl:wQ,maxImpl:kQ,maximumImpl:SQ,minimumImpl:IQ,multiplyImpl:CQ,negImpl:TQ,notEqualImpl:NQ,prodImpl:EQ,rangeImpl:RQ,rsqrtImpl:_Q,sigmoidImpl:DQ,simpleAbsImpl:vC,sliceImpl:$Q,sparseFillEmptyRowsImpl:FQ,sparseReshapeImpl:PQ,sparseSegmentReductionImpl:wC,sqrtImpl:OQ,stridedSliceImpl:MQ,stringNGramsImpl:zQ,stringSplitImpl:LQ,stringToHashBucketFastImpl:BQ,subImpl:WQ,tileImpl:VQ,topKImpl:UQ,transposeImpl:Nx,uniqueImpl:GQ}=Dm;function kC(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Hn(e,t){return t===1?[e]:kC(e,t)}function HQ(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 jQ=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]})`}},SC=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=`
|
|
${qQ(t,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?vx():bx(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 qQ(e,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?fJ(["r","c","d"],"inputShape"):Nl(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var XQ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=CC(t,n),r=TC(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=IC(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===In.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===In.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===In.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===In.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===In.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=CC(n,s),a=TC(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=IC(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=Y().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 KQ(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 IC(e,t,n,s,r){let a=ZQ(t,s),o;if(r){let[l,c]=ic(e[0],e[1]);o=l*c}else{let[l,c]=bp(e[0],e[1]);o=l*c}let i=KQ(n,a);return o*i}function ZQ(e,t){switch(e){case In.PACKED_2X2_FLOAT32:return Cx(t);case In.PACKED_2X2_FLOAT16:return Tx(t);case In.UNPACKED_FLOAT32:return kx(t);case In.UNPACKED_FLOAT16:return Sx(t);case In.PACKED_4X1_UNSIGNED_BYTE:return Ix(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function YQ(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?In.PACKED_2X2_FLOAT32:In.UNPACKED_FLOAT32:e?In.PACKED_2X2_FLOAT16:In.UNPACKED_FLOAT16}function CC(e,t){if(e===Vs.UPLOAD)return In.PACKED_2X2_FLOAT32;if(e===Vs.RENDER||e==null)return YQ(t);if(e===Vs.DOWNLOAD||e===Vs.PIXELS)return In.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function TC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Ho=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;",JQ="return x;",NC="return abs(x);",QQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",eee=kr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,tee=kr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Xm="return x;",nee="return 1.0 / (1.0 + exp(-1.0 * x));",see="return x;",ree=`
|
|
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;
|
|
`,aee=`
|
|
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;
|
|
`,oee=`
|
|
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;
|
|
`,iee="return 1.0 / (1.0 + exp(-1.0 * x));",hc=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) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},lee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let t=e.length,n=Hn("rc",t),s=vt(t),r=HQ(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${o}));
|
|
}
|
|
`}},uee=tr.whereImpl,cee=1e-7,dee=1e-4,Km={};function pee(e){return e in Km||(Km[e]={}),Km[e]}var hee=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),fee=600;function mee(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*fee/1024/1024}var EC=class extends nu{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=pee(Y().getNumber("WEBGL_VERSION")),this.gpgpu=new qm(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 XQ(this.gpgpu),this.numMBBeforeWarning=mee(),this.texData=new ad(this,as())}nextDataId(){return EC.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:Vs.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:Vs.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 hc(o,Xm):d=new Ho(o,Xm);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 hc(s,Xm):h=new Ho(s,Xm);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,...Lm(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(!NI(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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...Lm(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=Y().getBool("WEBGL_PACK")&&s===!0,o=a?Vm(t):t,i=a?new sQ(o):new nQ(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${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=hee){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 uee(e.shape,t)}packedUnaryOp(e,t,n){let s=new hc(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=vC(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,NC,e.dtype);let t=new Ho(e.shape,NC),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 lee(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new jQ(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Cl(e.shape),...Tl(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Cl(t),...Tl(t)],a=new SC(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=Vm(s),o,i=Lm(a);n?o=new tQ(a):o=new eQ(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===xp.DENSE){let m=Lm(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=QJ(e,l,c),d=this.getAndSaveBinary(u,()=>YJ(this.gpgpu,e,l,c)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),JJ(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=K(()=>{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?cee:dee}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=HI(n,i),t.texShape=u),r!=null){let d=Vm(n),p,h=u[1],f=u[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(i||!m)&&([h,f]=ic(u[0],u[1])),i?p=new aQ(d,m):p=new rQ(d,m);let g=m?[f,h]:u,y=this.makeTensorInfo(g,s),x=this.texData.get(y.dataId);m?x.usage=Vs.PIXELS:x.usage=Vs.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),k=this.texData.get(w.dataId);t.texture=k.texture,t.texShape=k.texShape,t.isPacked=k.isPacked,t.usage=k.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=gee(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=EC;Ip.nextDataId=0;function gee(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 yee="0.0.0";function RC(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}Pu.isBrowser()&&ol("webgl",()=>new Ip,2);var Aee={forceHalfFloat:RC},_C=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,fc=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));
|
|
}
|
|
`}},Zm=`
|
|
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);
|
|
${a}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function Ss(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var xee={kernelName:qa,backendName:"webgl",kernelFunc:Ss};function jo(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=Ss({inputs:{x:s},backend:n}),l=Ss({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var bee={kernelName:ud,backendName:"webgl",kernelFunc:jo},DC="return (a < 0.) ? b * a : a;",$C=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function vee(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($C,r.shape,o.shape):new fc(DC,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),l}var wee={kernelName:Si,backendName:"webgl",kernelFunc:vee},FC="return (a < 0.) ? b * a : a;",PC=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function kee(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cp(PC,s.shape,r.shape):new fc(FC,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var See={kernelName:ao,backendName:"webgl",kernelFunc:kee},OC="if (isnan(x)) return x;",Iee=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,Cee=`
|
|
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 hc(o.shape,t):u=new Ho(o.shape,e),i.runWebGLProgram(u,[o],l)}}function Cn({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,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},I={dataId:w.dataId,dtype:w.dtype,shape:c.shape},E=new fc(e,l.shape,c.shape);return u.runWebGLProgram(E,[k,I],zn(b.dtype,w.dtype))}),x=jo({inputs:{real:g,imag:y},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(y),x}let d=a||zn(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 fc(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],d)}}function Ym(e,t=!1){if(e==="linear")return t?see:JQ;if(e==="relu")return t?aee:eee;if(e==="elu")return t?ree:QQ;if(e==="relu6")return t?oee:tee;if(e==="prelu")return t?PC:FC;if(e==="leakyrelu")return t?$C:DC;if(e==="sigmoid")return t?iee:nee;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var MC=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);
|
|
}
|
|
`}},zC={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},LC=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) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},BC="return a * b;";function Ex(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=N.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),c=new LC(zC.REAL,s.shape,r.shape),u=new LC(zC.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=jo({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[c,u]=CQ(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(u,a),p=n.texData.get(d.dataId);return p.values=c,d}let o;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Cp(BC,s.shape,r.shape):o=new fc(BC,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var Tee={kernelName:no,backendName:"webgl",kernelFunc:Ex};function Nee(e,t,n){let s=[Cl(e.shape),...Tl(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[Cl(t),...Tl(t)],o=new SC(a,s),i=!0,l=[s],c=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function ve(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),c=v.sizeFromShape(l);v.assert(i===c,()=>`The new shape (${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))?Nee(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var Eee={kernelName:Oi,backendName:"webgl",kernelFunc:ve},WC=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);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},Ree=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${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 _ee(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 Rl(e,t,n,s){let r=_ee(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 WC({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},i):new WC({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c}):u=new Ree({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 Dee=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=$ee(t);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function $ee(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 Fee=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 ${this.rank} is not yet supported.`);let s=vt(this.rank),r=kC("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=r[c];let o=`vec2(${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] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Jm(e,t,n){let s=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Fee(e.shape,t):new Dee(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function Pee(e,t,n,s){let r=t,a=e.shape.length,o=v.parseAxisParam(r,e.shape),i=o,l=N.getAxesPermutation(i,a),c=l!=null,u=e;c&&(u=Jm(e,l,s),i=N.getInnerMostAxes(i.length,a)),N.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=N.computeOutAndReduceShapes(u.shape,i),h=d;n&&(h=N.expandShapeToKeepDim(d,o));let f=v.sizeFromShape(p),g=v.sizeFromShape(e.shape)/f,y=ve({inputs:{x:u},attrs:{shape:[g,f]},backend:s}),x=Fd(e.dtype),A=Rl(y,x,"sum",s),b=ve({inputs:{x:A},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(y),s.disposeIntermediateTensorInfo(A),c&&s.disposeIntermediateTensorInfo(u),b}function Qm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Pee(r,a,o,n)}var Oee={kernelName:fo,backendName:"webgl",kernelFunc:Qm};function jn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];let c;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,p=Nx(d,r.shape,r.dtype,a,l);c=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(c.dataId);h.values=p}else c=Jm(r,a,o);return c}var Mee={kernelName:xo,backendName:"webgl",kernelFunc:jn},VC=1e3;function e0({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),b=sl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${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],k=s?[x,f,p]:[x,p,f],I=ve({inputs:{x:e},backend:r,attrs:{shape:w}}),E=ve({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[I,E],P=Math.max(y,x),D=n?I.shape[1]:I.shape[2],_=a!=null,T=o!=null,O=l==="leakyrelu",W=l!=null?Ym(l,!0):null,X=_||T||O||W!=null,z;if((h===1||f===1)&&D>VC&&X===!1){let Z=I,Q=E;n&&(Z=jn({inputs:{x:I},backend:r,attrs:{perm:[0,2,1]}}),R.push(Z)),s&&(Q=jn({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),R.push(Q));let ne=f!==1,ae=f===1,U=Z;ne&&(U=ve({inputs:{x:Z},backend:r,attrs:{shape:[P,D,1]}}),R.push(U));let oe=f===1?2:1,re=Q;ae&&(re=ve({inputs:{x:Q},backend:r,attrs:{shape:[P,1,D]}}),R.push(re));let me=Ex({inputs:{a:U,b:re},backend:r});z=Qm({inputs:{x:me},backend:r,attrs:{axis:oe,keepDims:!0}}),R.push(me)}else{let Z=zn(e.dtype,t.dtype),Q=new MC(w,k,[P,h,f],n,s,_,W,T,O),ne=[I,E];if(a!=null&&ne.push(a),T&&ne.push(o),O){let ae=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));ne.push(ae),R.push(ae)}z=r.runWebGLProgram(Q,ne,Z)}let j=ve({inputs:{x:z},backend:r,attrs:{shape:b}});R.push(z);for(let Z of R)r.disposeIntermediateTensorInfo(Z);return j}function zee(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 e0({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var Lee={kernelName:vo,backendName:"webgl",kernelFunc:zee},UC="return abs(x);";function Bee(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=vC(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new hc(s.shape,UC):r=new Ho(s.shape,UC),n.runWebGLProgram(r,[s],s.dtype)}var Wee={kernelName:di,backendName:"webgl",kernelFunc:Bee},Vee=kr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,Uee=at({opSnippet:Vee}),Gee={kernelName:ou,backendName:"webgl",kernelFunc:Uee},Hee=kr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,jee=at({opSnippet:Hee}),qee={kernelName:iu,backendName:"webgl",kernelFunc:jee},GC="return a + b;",Xee=Cn({opSnippet:GC,packedOpSnippet:GC,supportsComplex:!0,cpuKernelImpl:iQ}),Kee={kernelName:qr,backendName:"webgl",kernelFunc:Xee},Zee=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 = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}},Yee=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${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 t0(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=t0({inputs:s.slice(0,l),backend:n}),u=t0({inputs:s.slice(l),backend:n});return t0({inputs:[c,u],backend:n})}let r=s.map(l=>l.dtype).reduce((l,c)=>zn(l,c)),a=s.map(l=>l.shape),i=Y().getBool("WEBGL_PACK")?new Yee(s[0].shape,a):new Zee(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var Jee={kernelName:Ea,backendName:"webgl",kernelFunc:t0};function Qee(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=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Rl(m,m.dtype,"all",n),y;if(o){let x=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var ete={kernelName:lu,backendName:"webgl",kernelFunc:Qee};function tte(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=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Rl(m,m.dtype,"any",n),y;if(o){let x=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var nte={kernelName:uu,backendName:"webgl",kernelFunc:tte},ste=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 ${o} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},rte=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${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 I=vt(d);u=`
|
|
${I} sourceLocR = ${I}(${c.join()}, 0);
|
|
++${c[i-1]};
|
|
${I} sourceLocG = ${I}(${c.join()}, 0);
|
|
++${c[i-2]};
|
|
${I} sourceLocA = ${I}(${c.join()}, 0);
|
|
--${c[i-1]};
|
|
${I} sourceLocB = ${I}(${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(I=>"int "+I),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.)`,k=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()}));
|
|
}
|
|
${k}
|
|
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 HC(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 ste(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=HC(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}function jC(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=N.computeOptimalWindowSize(a),i=new rte(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=jC(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function qC(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=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=HC(e,p,s);a.push(h);let f=ve({inputs:{x:h},backend:e,attrs:{shape:c}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return jC(e,t,s)}function ate(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=qC(n,l,o[0],"max");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var ote={kernelName:Ra,backendName:"webgl",kernelFunc:ate};function ite(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=qC(n,l,o[0],"min");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var lte={kernelName:cu,backendName:"webgl",kernelFunc:ite},ute=kr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,cte=at({opSnippet:ute}),dte={kernelName:du,backendName:"webgl",kernelFunc:cte},pte=kr+"return log(x + sqrt(x * x + 1.0));",hte=at({opSnippet:pte}),fte={kernelName:pu,backendName:"webgl",kernelFunc:hte},mte=kr+`
|
|
return atan(x);
|
|
`,gte=at({opSnippet:mte}),yte={kernelName:hu,backendName:"webgl",kernelFunc:gte},Ate=Iee+`
|
|
return atan(a, b);
|
|
`,xte=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Cee+`
|
|
return result;
|
|
`,bte=Cn({opSnippet:Ate,packedOpSnippet:xte}),vte={kernelName:mu,backendName:"webgl",kernelFunc:bte},wte=kr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,kte=at({opSnippet:wte}),Ste={kernelName:fu,backendName:"webgl",kernelFunc:kte},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) * ${e.inChannels} + d`,y="0.0";if(f||(y="-1.0 / 1e-20"),n){let I=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${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 ${I} 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,k=`
|
|
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)
|
|
);
|
|
|
|
${k}
|
|
}
|
|
|
|
int xC = xCCorner + ${b};
|
|
if (${w===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
} else if (${w===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
} else if (${w===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${k}
|
|
}
|
|
}
|
|
setOutput(${A});
|
|
}
|
|
`}},Rx=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 k=Math.floor(a/4)*4,I=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 < ${k}; 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 + ${k};
|
|
if (${I===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${E}
|
|
} else if (${I===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${E}
|
|
} else if (${I===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 Ite(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;lc(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 Cte={kernelName:_a,backendName:"webgl",kernelFunc:Ite};function Tte(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 Rx(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var Nte={kernelName:ld,backendName:"webgl",kernelFunc:Tte},Ete=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 >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Rte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
|
|
const ivec3 pads = ivec3(${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 _te(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 Rte(p);return n.runWebGLProgram(h,[r],o.dtype)}var Dte={kernelName:Dh,backendName:"webgl",kernelFunc:_te};function $te(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;lc([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=N.computePool2DInfo(o.shape,i,l,1,c),d=new Ete(u);return n.runWebGLProgram(d,[r],o.dtype)}var Fte={kernelName:_h,backendName:"webgl",kernelFunc:$te};function Pte(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return e0({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var Ote={kernelName:Da,backendName:"webgl",kernelFunc:Pte},Mte=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)));
|
|
}
|
|
`}},zte=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);
|
|
}
|
|
`}},Lte=({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 zte(s.shape,r.shape,a.shape,u,d,l):new Mte(s.shape,r.shape,a.shape,u,d,l);return t.runWebGLProgram(p,c,c[0].dtype)},Bte={kernelName:Ha,backendName:"webgl",kernelFunc:Lte},Wte=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=Vte(this.rank),s,r=e.map((a,o)=>`sourceLoc.${_x[o]} = start[${o}] + coords.${_x[o]};`);s=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${r.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${s}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}},_x=["x","y","z","w","u","v"];function Vte(e){if(e===1)return"sourceLoc";if(e<=6)return _x.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Ute=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=vt(this.rank),n=Hn("coords",this.rank),s=Hn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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}
|
|
${i}
|
|
setOutput(result);
|
|
}
|
|
`}};function Gte(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Mt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function mc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Mt.parseSliceParams(r,a,o);if(Mt.assertParamsValid(r,i,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=$Q(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 Ute(l):new Wte(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),Gte(r,i,l,n)}var Hte={kernelName:Wi,backendName:"webgl",kernelFunc:mc},jte=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=ve({inputs:{x:r},backend:n,attrs:{shape:l}}),m=jn({inputs:{x:f},backend:n,attrs:{perm:c}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:u}}),y=mc({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},qte={kernelName:pi,backendName:"webgl",kernelFunc:jte};function Xte(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=bC(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var Kte={kernelName:$h,backendName:"webgl",kernelFunc:Xte};function Zte(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 Yte={kernelName:Fh,backendName:"webgl",kernelFunc:Zte},Jte="return float(a != b);",XC=Cn({opSnippet:Jte,cpuKernelImpl:NQ,dtype:"bool"}),Qte={kernelName:Ei,backendName:"webgl",kernelFunc:XC};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 ene={kernelName:Ad,backendName:"webgl",kernelFunc:Np},tne="return float(int(x));";function nne(e,t){let n=new Ho(e.shape,tne),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Dx(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=jt(r.shape),i=Dx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=jo({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=Dx({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 nne(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=XC({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var sne={kernelName:$a,backendName:"webgl",kernelFunc:Dx},KC="return ceil(x);",rne=at({opSnippet:KC,packedOpSnippet:KC,cpuKernelImpl:uQ}),ane={kernelName:Fa,backendName:"webgl",kernelFunc:rne},one=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));
|
|
}
|
|
`}},ine=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 lne(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 ine(r.shape):i=new one(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var une={kernelName:Xr,backendName:"webgl",kernelFunc:lne},cne=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 ZC(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function dne(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new cne(s.shape),o=[ZC(s,r.complexTensorInfos.real),ZC(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var pne={kernelName:cd,backendName:"webgl",kernelFunc:dne},hne=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;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},fne=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=vt(s),a=Hn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${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}(${n0(o,l,m)}),
|
|
vec2(${n0(c,l,m)}));
|
|
}`}let p=i.length,h=i[i.length-1];d+=`
|
|
return getChannel(
|
|
getT${p}(${n0(o,l,h)}),
|
|
vec2(${n0(c,l,h)}));`,this.userCode=`
|
|
float getValue(${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 n0(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function s0(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 mne={kernelName:fd,backendName:"webgl",kernelFunc:s0};function gc(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=>s0({inputs:{input:m},backend:n})),p=gc(u,t,n),h=gc(d,t,n),f=jo({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 ve({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=cQ(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=gc(e.slice(0,u),t,n),p=gc(e.slice(u),t,n),h=gc([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 fne(e.map(d=>d.shape),t);return n.runWebGLProgram(u,e,s)}let{tensors2D:a,outShape:o}=gne(e,t,n),i=new hne(a.map(u=>u.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let c=ve({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),c}function gne(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function YC(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),gc(i,a,n)}var yne={kernelName:hi,backendName:"webgl",kernelFunc:YC},JC=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}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}},Ane=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${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);
|
|
}
|
|
`}},xne=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 QC({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>VC)&&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},k=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 I=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(I);let E=e0({a:w,b:I,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=k,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=ve({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=e0({a:w,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ve({inputs:{x:I},backend:s,attrs:{shape:n.outShape}}),y.push(w),y.push(k),y.push(I)}for(let b of y)s.disposeIntermediateTensorInfo(b);return g}function e4({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=ve({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(k);let I=new xne(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(I,[w],"float32",E),P=ve({inputs:{x:R},backend:s,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(P);let D=r!=null,_=a!=null,T=i==="leakyrelu",O=i?Ym(i,!0):null,W=new MC(P.shape,k.shape,[1,g,n.outChannels],x,A,D,O,_,T),X=[P,k];if(r&&X.push(r),_&&X.push(a),T){let Q=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));X.push(Q),b.push(Q)}let z=s.runWebGLProgram(W,X,"float32"),j=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],Z=ve({inputs:{x:z},backend:s,attrs:{shape:j}});b.push(z);for(let Q of b)s.disposeIntermediateTensorInfo(Q);return Z}function bne(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=QC({x:r,filter:a,convInfo:p,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=e4({x:r,filter:a,convInfo:p,backend:n});else{let m=new JC(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=ve({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var vne={kernelName:Pa,backendName:"webgl",kernelFunc:bne},wne=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);
|
|
}
|
|
`}},kne=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);
|
|
}
|
|
`}},Sne=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Ine=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 Cne(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 wne(p);return n.runWebGLProgram(h,[r,a],"float32")}var Tne={kernelName:Ph,backendName:"webgl",kernelFunc:Cne};function Nne(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 kne(p);return n.runWebGLProgram(h,[r,a],"float32")}var Ene={kernelName:Oa,backendName:"webgl",kernelFunc:Nne};function Rne(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 Ane(c);return n.runWebGLProgram(u,[r,a],"float32")}var _ne={kernelName:dd,backendName:"webgl",kernelFunc:Rne};function Dne(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 Sne(c);return n.runWebGLProgram(u,[r,a],"float32")}var $ne={kernelName:Oh,backendName:"webgl",kernelFunc:Dne};function Fne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,c=N.computeConv3DInfo(l,a.shape,i,1,o),u=new Ine(c);return n.runWebGLProgram(u,[r,a],"float32")}var Pne={kernelName:Mh,backendName:"webgl",kernelFunc:Fne},One=OC+`
|
|
return cos(x);
|
|
`,Mne=at({opSnippet:One}),zne={kernelName:Ma,backendName:"webgl",kernelFunc:Mne},Lne=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,Bne=at({opSnippet:Lne}),Wne={kernelName:za,backendName:"webgl",kernelFunc:Bne},Vne=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[c]=t,[u,d]=n;this.outputShape=[c,u,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${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(${p} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},Une=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new Vne(r.shape,a.shape,i,l,c);return n.runWebGLProgram(u,[r,a,o],"float32")},Gne={kernelName:mi,backendName:"webgl",kernelFunc:Une},t4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${n4(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 = ${s4(s,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${o}) {
|
|
int idx = ${i};
|
|
${s4(s,"coords")} = idx;
|
|
val += getX(${n4(s,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}};function n4(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 ${e} is not yet supported`)}function s4(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function Hne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,c=N.getAxesPermutation([a],l),u=r;c!=null&&(u=jn({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=N.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=Ss({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new t4(u.shape,!1,i),g=[[f]],y=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(o){let f=new t4(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=N.getUndoAxesPermutation(c),m=jn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var jne={kernelName:fi,backendName:"webgl",kernelFunc:Hne};function qne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=bC(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=lQ(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var Xne={kernelName:zh,backendName:"webgl",kernelFunc:qne},Kne=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 Zne(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 Kne(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var Yne={kernelName:gi,backendName:"webgl",kernelFunc:Zne},r4=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}
|
|
${c}
|
|
setOutput(result);
|
|
}
|
|
`}},a4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ds(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,u=e.filterWidth,d=u,p=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<u;g++)p+=`
|
|
vec4 xTexelC${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 Jne(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 a4(d):p=new r4(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 Qne={kernelName:La,backendName:"webgl",kernelFunc:Jne},ese=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},tse=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${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 * ${i} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function nse(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s,d=N.computeConv2DInfo(r.shape,u,o,i,l,c,!0),p=new ese(d);return n.runWebGLProgram(p,[r,a],"float32")}var sse={kernelName:Lh,backendName:"webgl",kernelFunc:nse};function rse(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s,d=N.computeConv2DInfo(u,a.shape,o,i,l,c,!0),p=new tse(d);return n.runWebGLProgram(p,[r,a],"float32")}var ase={kernelName:Bh,backendName:"webgl",kernelFunc:rse},ose=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function ise(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=ve({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new ose(a),l=n.runWebGLProgram(i,[o],o.dtype),c=ve({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var lse={kernelName:Wh,backendName:"webgl",kernelFunc:ise},use=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:c}=e,{top:u,left:d}=s;this.userCode=`
|
|
const ivec2 strides = ivec2(${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 cse(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 use(c);u=n.runWebGLProgram(d,[r,a],"float32");let p=ve({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var dse={kernelName:pd,backendName:"webgl",kernelFunc:cse};function pse(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=ve({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),p===null?p=A:(p=Ex({inputs:{a:A,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Qm({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 hse={kernelName:hd,backendName:"webgl",kernelFunc:pse},fse="return (x >= 0.0) ? x : (exp(x) - 1.0);",mse=`
|
|
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;
|
|
`,gse=at({opSnippet:fse,packedOpSnippet:mse}),yse={kernelName:Wa,backendName:"webgl",kernelFunc:gse},Ase="return (b >= 1.0) ? a : a * (b + 1.0);",xse=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,bse=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Cp(xse,s.shape,r.shape):new fc(Ase,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},vse={kernelName:Gh,backendName:"webgl",kernelFunc:bse},wse=`
|
|
return vec4(equal(a, b));
|
|
`,kse="return float(a == b);",Sse=Cn({opSnippet:kse,packedOpSnippet:wse,dtype:"bool",cpuKernelImpl:dQ}),Ise={kernelName:yi,backendName:"webgl",kernelFunc:Sse},Cse=`
|
|
// 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));
|
|
`,Tse=at({opSnippet:Cse}),Nse={kernelName:gu,backendName:"webgl",kernelFunc:Tse},o4="return exp(x);",i4=at({opSnippet:o4,packedOpSnippet:o4,cpuKernelImpl:pQ,dtype:"float32"}),Ese={kernelName:Va,backendName:"webgl",kernelFunc:i4};function $x(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ve({inputs:{x:a},backend:s,attrs:{shape:i}})}var Rse={kernelName:Ai,backendName:"webgl",kernelFunc:$x},l4="return exp(x) - 1.0;",_se=at({opSnippet:l4,packedOpSnippet:l4,cpuKernelImpl:hQ}),Dse={kernelName:xi,backendName:"webgl",kernelFunc:_se},u4=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 c4(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=ve({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,c=new u4("real",l,t),u=new u4("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=jo({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=ve({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function $se(e){let{inputs:t,backend:n}=e,{input:s}=t;return c4(s,!1,n)}var Fse={kernelName:Hh,backendName:"webgl",kernelFunc:$se},Pse=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 Pse(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var Ose={kernelName:yu,backendName:"webgl",kernelFunc:Ep},Mse=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},zse={kernelName:bi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Mse(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},d4="return floor(x);",Lse=at({opSnippet:d4,packedOpSnippet:d4,cpuKernelImpl:fQ}),Bse={kernelName:Ua,backendName:"webgl",kernelFunc:Lse},Wse=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,Vse=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,Use=Cn({opSnippet:Wse,packedOpSnippet:Vse,dtype:"int32"}),Gse={kernelName:Ga,backendName:"webgl",kernelFunc:Use},Hse=class{constructor(e){this.variableNames=["A"];let t=Gn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},jse=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Gn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${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;
|
|
}
|
|
`}},qse={kernelName:Id,backendName:"webgl",kernelFunc:Xse},yc;function Xse(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)&&(yc==null&&(yc=document.createElement("canvas").getContext("2d")),yc.canvas.width=l,yc.canvas.height=c,yc.drawImage(r,0,0,l,c),r=yc.canvas);let p=n.makeTensorInfo(u,"int32");n.texData.get(p.dataId).usage=Vs.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=Y().getBool("WEBGL_PACK")?new jse(d):new Hse(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function Kse(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=QC({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=e4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else{let b=o!=null,w=i!=null,k=h==="leakyrelu",I=h?Ym(h,!1):null,E=new JC(g,b,I,w,k),R=[r,a];if(o&&R.push(o),i&&R.push(i),k){let P=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));R.push(P),x.push(P)}y=n.runWebGLProgram(E,R,"float32")}let A=ve({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return x.push(y),x.forEach(b=>n.disposeIntermediateTensorInfo(b)),A}var Zse={kernelName:wo,backendName:"webgl",kernelFunc:Kse};function Yse(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?Ym(p,y):null,A=[r,a],b=o!=null,w=i!=null,k=p==="leakyrelu";if(b&&A.push(o),w&&A.push(i),k){let P=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));A.push(P),f.push(P)}let I;y?I=new a4(g,b,x,w,k):I=new r4(g,b,x,w,k);let E=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=n.runWebGLProgram(I,A,"float32",E);return f.forEach(P=>n.disposeIntermediateTensorInfo(P)),R}var Jse={kernelName:ko,backendName:"webgl",kernelFunc:Yse},Qse=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 ere(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=ve({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=ve({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=mQ(y,x,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,A.values)}let f=new Qse(o,d,[c,u]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=ve({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var tre={kernelName:wi,backendName:"webgl",kernelFunc:ere},nre=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=vt(this.rank),s=sre(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function sre(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 p4(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=ve({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=ve({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),k=gQ(w,b,g);return h.forEach(I=>n.disposeIntermediateTensorInfo(I)),n.makeTensorInfo(d.outputShape,k.dtype,k.values)}let y=new nre(f.shape,g),x=n.runWebGLProgram(y,[f,m],f.dtype);h.push(x);let A=ve({inputs:{x},backend:n,attrs:{shape:d.outputShape}});return h.forEach(b=>n.disposeIntermediateTensorInfo(b)),A}var rre={kernelName:vi,backendName:"webgl",kernelFunc:p4},are="return float(a > b);",ore=`
|
|
return vec4(greaterThan(a, b));
|
|
`,ire=Cn({opSnippet:are,packedOpSnippet:ore,cpuKernelImpl:yQ,dtype:"bool"}),lre={kernelName:ki,backendName:"webgl",kernelFunc:ire},ure="return float(a >= b);",cre=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,dre=Cn({opSnippet:ure,packedOpSnippet:cre,dtype:"bool",cpuKernelImpl:AQ}),pre={kernelName:ja,backendName:"webgl",kernelFunc:dre};function hre(e){let{inputs:t,backend:n}=e,{input:s}=t;return c4(s,!0,n)}var fre={kernelName:jh,backendName:"webgl",kernelFunc:hre},mre="return float(!isnan(x) && !isinf(x));",gre=at({opSnippet:mre,dtype:"bool"}),yre={kernelName:Au,backendName:"webgl",kernelFunc:gre},Are="return float(isinf(x));",xre=at({opSnippet:Are,dtype:"bool"}),bre={kernelName:xu,backendName:"webgl",kernelFunc:xre},vre="return float(isnan(x));",wre=at({opSnippet:vre,dtype:"bool"}),kre={kernelName:bu,backendName:"webgl",kernelFunc:wre},Sre="return float(a < b);",Ire=`
|
|
return vec4(lessThan(a, b));
|
|
`,Cre=Cn({opSnippet:Sre,packedOpSnippet:Ire,cpuKernelImpl:xQ,dtype:"bool"}),Tre={kernelName:Ii,backendName:"webgl",kernelFunc:Cre},Nre="return float(a <= b);",Ere=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,Rre=Cn({opSnippet:Nre,packedOpSnippet:Ere,cpuKernelImpl:bQ,dtype:"bool"}),_re={kernelName:Ci,backendName:"webgl",kernelFunc:Rre};function Dre(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=vQ(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var $re={kernelName:qh,backendName:"webgl",kernelFunc:Dre},Fre=`if (x < 0.0) return NAN;
|
|
return log(x);`,Pre=`
|
|
vec4 result = log(x);
|
|
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
|
|
result.r = isNaN.r == 1.0 ? NAN : result.r;
|
|
result.g = isNaN.g == 1.0 ? NAN : result.g;
|
|
result.b = isNaN.b == 1.0 ? NAN : result.b;
|
|
result.a = isNaN.a == 1.0 ? NAN : result.a;
|
|
|
|
return result;
|
|
`,Ore=at({opSnippet:Fre,packedOpSnippet:Pre,cpuKernelImpl:wQ}),Mre={kernelName:Xa,backendName:"webgl",kernelFunc:Ore},zre="return log(1.0 + x);",Lre=at({opSnippet:zre}),Bre={kernelName:vu,backendName:"webgl",kernelFunc:Lre},Wre="return float(a >= 1.0 && b >= 1.0);",Vre=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,Ure=Cn({opSnippet:Wre,packedOpSnippet:Vre,dtype:"bool"}),Gre={kernelName:Ti,backendName:"webgl",kernelFunc:Ure},Hre="return float(!(x >= 1.0));",jre=at({opSnippet:Hre}),qre={kernelName:wu,backendName:"webgl",kernelFunc:jre},Xre="return float(a >= 1.0 || b >= 1.0);",Kre=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,Zre=Cn({opSnippet:Xre,packedOpSnippet:Kre,dtype:"bool"}),Yre={kernelName:md,backendName:"webgl",kernelFunc:Zre},Jre=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${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 * ${i};
|
|
setOutput(val);
|
|
}
|
|
`}},Qre=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${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);
|
|
}
|
|
`}},eae=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 Qre(r.shape,a,o,i,l):new Jre(r.shape,a,o,i,l);return n.runWebGLProgram(c,[r],r.dtype)},tae={kernelName:gd,backendName:"webgl",kernelFunc:eae},nae=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);
|
|
}
|
|
`}},sae=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 nae(r.shape,i,l,c,u);return n.runWebGLProgram(d,[r,a,o],r.dtype)},rae={kernelName:Xh,backendName:"webgl",kernelFunc:sae};function aae(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=Rl(i,e.dtype,"max",s),c=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}function h4(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 I=0;I<b.length;I++)b[I]=r.shape[u[I]];let w=Nx(A,r.shape,r.dtype,u,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=w}else h=Jm(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=kQ(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=aae(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),y}var oae={kernelName:Ka,backendName:"webgl",kernelFunc:h4},iae=_C+`
|
|
return max(a, b);
|
|
`,lae=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Zm+`
|
|
return result;
|
|
`,uae=Cn({opSnippet:iae,packedOpSnippet:lae,cpuKernelImpl:SQ}),cae={kernelName:Za,backendName:"webgl",kernelFunc:uae};function dae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;lc(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 pae={kernelName:Ya,backendName:"webgl",kernelFunc:dae};function hae(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 Rx(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var fae={kernelName:yd,backendName:"webgl",kernelFunc:hae},mae=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 * ${a} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},gae=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,h=i*l*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 yae(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 Rx(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new gae(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var Aae={kernelName:Zh,backendName:"webgl",kernelFunc:yae};function xae(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;lc([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 mae(p),y=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var bae={kernelName:Kh,backendName:"webgl",kernelFunc:xae};function vae(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 wae={kernelName:Yh,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]=vae(s,i,u,l);return[d,p]}};function kae(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=Rl(i,"float32","mean",s),c=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}var Sae={kernelName:Ja,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 k=Nx(b,s.shape,s.dtype,u,w);f=o.makeTensorInfo(w,s.dtype);let I=o.texData.get(f.dataId);I.values=k}else f=Jm(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=kae(f,g,y,o);for(let A of h)o.disposeIntermediateTensorInfo(A);return x}};function Iae(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=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Rl(m,m.dtype,"min",n),y;if(o){let x=N.expandShapeToKeepDim(p,l);y=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var Cae={kernelName:Qa,backendName:"webgl",kernelFunc:Iae},Tae=_C+`
|
|
return min(a, b);
|
|
`,Nae=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Zm+`
|
|
return result;
|
|
`,Eae=Cn({opSnippet:Tae,packedOpSnippet:Nae,cpuKernelImpl:IQ}),Rae={kernelName:eo,backendName:"webgl",kernelFunc:Eae},_ae=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}));
|
|
}
|
|
`}},Dae=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.);
|
|
${p}
|
|
setOutput(result);
|
|
}
|
|
`}},$ae=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Dae(s.shape,r,a):new _ae(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},Fae={kernelName:to,backendName:"webgl",kernelFunc:$ae},Pae=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,Oae=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+Zm+`
|
|
return result;
|
|
`,Mae=Cn({opSnippet:Pae,packedOpSnippet:Oae}),zae={kernelName:ku,backendName:"webgl",kernelFunc:Mae},Lae=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}));
|
|
}
|
|
`}},Bae=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,Wae=`
|
|
// 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;
|
|
`,f4=Cn({opSnippet:Bae,packedOpSnippet:Wae,checkOutOfBounds:!0}),Vae={kernelName:Ba,backendName:"webgl",kernelFunc:f4},m4="return a - b;",g4=Cn({opSnippet:m4,packedOpSnippet:m4,supportsComplex:!0,cpuKernelImpl:WQ}),Uae={kernelName:yo,backendName:"webgl",kernelFunc:g4};function y4(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=h4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,o),c=ve({inputs:{x:i},backend:n,attrs:{shape:l}}),u=g4({inputs:{a:r,b:c},backend:n}),d=i4({inputs:{x:u},backend:n}),p=Qm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=ve({inputs:{x:p},backend:n,attrs:{shape:l}}),f=f4({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 Gae={kernelName:mo,backendName:"webgl",kernelFunc:y4};function Hae(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:y4({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],d=new Lae(c,u,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var jae={kernelName:Jh,backendName:"webgl",kernelFunc:Hae},A4="return -x;";function qae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=TQ(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new hc(s.shape,A4):r=new Ho(s.shape,A4),n.runWebGLProgram(r,[s],s.dtype)}var Xae={kernelName:Ni,backendName:"webgl",kernelFunc:qae},Kae=tr.nonMaxSuppressionV3Impl;function Zae(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}=Kae(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Yae={kernelName:Ri,backendName:"webgl",kernelFunc:Zae},Jae=tr.nonMaxSuppressionV4Impl;function Qae(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}=Jae(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var eoe={kernelName:Su,backendName:"webgl",kernelFunc:Qae},toe=tr.nonMaxSuppressionV5Impl;function noe(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}=toe(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var soe={kernelName:_i,backendName:"webgl",kernelFunc:noe},roe=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)));
|
|
}
|
|
`}},aoe=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 roe(l,a,o,i),u=ve({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let p=[...r.shape,a],h=ve({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},ooe={kernelName:$i,backendName:"webgl",kernelFunc:aoe};function r0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Np({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=jo({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 ioe={kernelName:Zi,backendName:"webgl",kernelFunc:r0};function x4(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=x4({inputs:{x:r},backend:n}),o=s0({inputs:{input:s},backend:n}),i=r0({inputs:{x:o},backend:n}),l=jo({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 loe={kernelName:Di,backendName:"webgl",kernelFunc:x4};function uoe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return $x({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=$x({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=YC({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var coe={kernelName:Fi,backendName:"webgl",kernelFunc:uoe},doe=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}));
|
|
}
|
|
}
|
|
`}},poe=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);
|
|
}
|
|
`}},b4=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 poe(r.shape,a,o):new doe(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},hoe={kernelName:so,backendName:"webgl",kernelFunc:b4},foe=`
|
|
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);
|
|
`,moe=`
|
|
// 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));
|
|
`+Zm+`
|
|
return result;
|
|
`,goe=Cn({opSnippet:foe,packedOpSnippet:moe}),yoe={kernelName:ro,backendName:"webgl",kernelFunc:goe};function Aoe(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}=EQ(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=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),x=Fd(r.dtype),A=Rl(y,x,"prod",n);h=ve({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=ve({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var xoe={kernelName:Pi,backendName:"webgl",kernelFunc:Aoe},v4=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=RQ(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},boe={kernelName:Iu,backendName:"webgl",kernelFunc:v4},voe="return 1.0 / x;",woe=at({opSnippet:voe}),koe={kernelName:Cu,backendName:"webgl",kernelFunc:woe},Soe=kr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,Ioe=`
|
|
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;
|
|
`,Coe=at({opSnippet:Soe,packedOpSnippet:Ioe}),Toe={kernelName:oo,backendName:"webgl",kernelFunc:Coe},Noe=kr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Eoe=`
|
|
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;
|
|
`,Roe=at({opSnippet:Noe,packedOpSnippet:Eoe}),_oe={kernelName:lo,backendName:"webgl",kernelFunc:Roe},Doe=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 = ${d};
|
|
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
|
|
ivec2 sourceCeilRC = ivec2(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
|
|
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
|
|
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
|
|
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
|
|
|
|
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracRC.y;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
|
|
float newValue = top + (bottom - top) * fracRC.x;
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},$oe=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 Foe(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 $oe(r.shape,l,c,a,o):new Doe(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],"float32")}var Poe={kernelName:io,backendName:"webgl",kernelFunc:Foe},Ooe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${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 Moe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Ooe(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var zoe={kernelName:ef,backendName:"webgl",kernelFunc:Moe},Loe=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);
|
|
}
|
|
`}},Boe=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 < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function Woe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new Boe(r.shape,l,c,a,o):new Loe(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],r.dtype)}var Voe={kernelName:Tu,backendName:"webgl",kernelFunc:Woe},Uoe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${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),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function Goe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Uoe(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Hoe={kernelName:Qh,backendName:"webgl",kernelFunc:Goe},joe=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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}));
|
|
}
|
|
`}},qoe=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 Xoe(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 qoe(r.shape,i):new joe(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var Koe={kernelName:Mi,backendName:"webgl",kernelFunc:Xoe},Zoe=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 < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},Yoe={kernelName:Yi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Zoe(s.shape,a),[c,u]=N.getImageCenter(o,s.shape[1],s.shape[2]),d=[[c,u,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},Joe=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,Qoe=at({opSnippet:Joe}),eie={kernelName:zi,backendName:"webgl",kernelFunc:Qoe},tie="return inversesqrt(x);",nie=at({opSnippet:tie,cpuKernelImpl:_Q}),sie={kernelName:uo,backendName:"webgl",kernelFunc:nie},w4=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=vt(r.length),l=vt(a.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${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 rie(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=ve({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=ve({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new w4(l,i,h.shape.length,f.shape.length,u,p),y=n.runWebGLProgram(g,[f,h,m],f.dtype),x=ve({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),x}var aie={kernelName:Li,backendName:"webgl",kernelFunc:rie},oie=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 iie(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new oie(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],zn(r.dtype,a.dtype))}var lie={kernelName:Bi,backendName:"webgl",kernelFunc:iie},uie=`
|
|
// 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);
|
|
`,cie=at({opSnippet:uie}),die={kernelName:Nu,backendName:"webgl",kernelFunc:cie},k4="return 1.0 / (1.0 + exp(-1.0 * x));",pie=at({opSnippet:k4,packedOpSnippet:k4,cpuKernelImpl:DQ}),hie={kernelName:po,backendName:"webgl",kernelFunc:pie},fie=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,mie=at({opSnippet:fie}),gie={kernelName:Eu,backendName:"webgl",kernelFunc:mie},yie=OC+`
|
|
return sin(x);
|
|
`,Aie=at({opSnippet:yie}),xie={kernelName:co,backendName:"webgl",kernelFunc:Aie},bie=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,vie=at({opSnippet:bie}),wie={kernelName:Vi,backendName:"webgl",kernelFunc:vie},kie=`
|
|
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;
|
|
`,Sie=at({opSnippet:kie}),Iie={kernelName:Ru,backendName:"webgl",kernelFunc:Sie},Cie=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=b4({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=ve({inputs:{x:u},backend:n,attrs:{shape:d}}),m=jn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=ve({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},Tie={kernelName:Ui,backendName:"webgl",kernelFunc:Cie};function Nie(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]=FQ(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 Eie={kernelName:xd,backendName:"webgl",kernelFunc:Nie};function Rie(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]=PQ(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var _ie={kernelName:_u,backendName:"webgl",kernelFunc:Rie};function Die(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]=wC(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var $ie={kernelName:bd,backendName:"webgl",kernelFunc:Die};function Fie(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
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=wC(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var Pie={kernelName:vd,backendName:"webgl",kernelFunc:Fie};function Oie(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=N.calculateShapes(a,r,i),p=!1,h=new w4(c,l,r.shape.length,a.shape.length,u,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=ve({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var Mie={kernelName:wd,backendName:"webgl",kernelFunc:Oie};function zie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=N.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=mc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var Lie={kernelName:Gi,backendName:"webgl",kernelFunc:zie},S4="return sqrt(x);",Bie=at({opSnippet:S4,packedOpSnippet:S4,cpuKernelImpl:OQ}),Wie={kernelName:ho,backendName:"webgl",kernelFunc:Bie},Vie="return x * x;",Uie=at({opSnippet:Vie}),Gie={kernelName:Du,backendName:"webgl",kernelFunc:Uie},I4="return (a - b) * (a - b);",Hie=Cn({opSnippet:I4,packedOpSnippet:I4}),jie={kernelName:go,backendName:"webgl",kernelFunc:Hie};function qie({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=kr+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,a=new Ho(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var Xie={kernelName:bo,backendName:"webgl",kernelFunc:qie},Kie=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 Zie(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=ve({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 I=Mt.computeOutShape(x,A,b),E=mc({inputs:{x:r},backend:n,attrs:{begin:x,size:I}});w=ve({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=MQ(h,R,b,x);w=n.makeTensorInfo(f,r.dtype,P.values)}else{let E=new Kie(x,b,h);w=n.runWebGLProgram(E,[r],r.dtype)}let k=ve({inputs:{x:w},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(w),k}var Yie={kernelName:Hi,backendName:"webgl",kernelFunc:Zie};function Jie(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]=zQ(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Qie={kernelName:kd,backendName:"webgl",kernelFunc:Jie};function ele(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]=LQ(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 tle={kernelName:tf,backendName:"webgl",kernelFunc:ele};function nle(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=BQ(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var sle={kernelName:nf,backendName:"webgl",kernelFunc:nle},rle="return tan(x);",ale=at({opSnippet:rle}),ole={kernelName:ji,backendName:"webgl",kernelFunc:ale},ile=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,lle=at({opSnippet:ile}),ule={kernelName:Ao,backendName:"webgl",kernelFunc:lle},cle=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=dle(e);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function dle(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 C4(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=VQ(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new cle(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var ple={kernelName:Kr,backendName:"webgl",kernelFunc:C4},hle=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));
|
|
}
|
|
}
|
|
`}},fle=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 _l(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function T4(e){let t=1;for(;t<e;)t*=2;return t}function mle(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),[D,_]=UQ(P,c,r.dtype,a,o);return[n.makeTensorInfo(D.shape,D.dtype,D.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=ve({inputs:{x:h},attrs:{shape:[m,u]},backend:n});p&&_l(n,h);let y=T4(a),x=T4(u),A=null,b=()=>A===null?[g,g]:[g,A],w=(P,D,_)=>{let T=b(),O=new hle(_),X=[[u],[A===null?1:0],[Number.NEGATIVE_INFINITY],[P],[D]],z=A;A=n.runWebGLProgram(O,T,"int32",X),_l(n,z)};for(let P=1;P<y;P*=2){let D=P*2;for(let _=P;_>=1;_/=2)w(D,_,[m,x])}for(let P=x;P>y;P/=2){let D=b(),_=new fle([m,P/2]),O=[[u],[A===null?1:0],[y]],W=A;A=n.runWebGLProgram(_,D,"int32",O),_l(n,W);let X=y/2,z=X*2;for(let j=X;j>=1;j/=2)w(z,j,A.shape)}let k=A;A=mc({inputs:{x:A},backend:n,attrs:{begin:0,size:[m,a]}}),_l(n,k);let I=p4({inputs:{x:g,indices:A},backend:n,attrs:{axis:1,batchDims:1}});_l(n,g);let E=c.slice(0,-1);E.push(a),k=A,A=ve({inputs:{x:A},attrs:{shape:E},backend:n}),_l(n,k);let R=I;return I=ve({inputs:{x:I},attrs:{shape:E},backend:n}),_l(n,R),[I,A]}var gle={kernelName:qi,backendName:"webgl",kernelFunc:mle},yle=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 Ale(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 yle(d,p,o,i,l,g);return n.runWebGLProgram(y,[r,a],"float32")}var xle={kernelName:Xi,backendName:"webgl",kernelFunc:Ale};function ble(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;lc(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}=GQ(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var vle={kernelName:sf,backendName:"webgl",kernelFunc:ble};function wle(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=mc({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),y=ve({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=y,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var kle={kernelName:Ki,backendName:"webgl",kernelFunc:wle},Sle=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 Ile(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=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=Fd(r.dtype),g=(b,w,k,I,E)=>{let R=b.shape[0],P=b.shape[1],D=N.segment_util.segOpComputeOptimalWindowSize(P,E),_={windowSize:D,inSize:P,batchSize:R,numSegments:E},T=new Sle(_,w),O=n.compileAndRun(T,[b,k],I);if(l.push(O),O.shape[1]===E)return O;let W=v4({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=C4({inputs:{x:W},backend:n,attrs:{reps:[P/D]}});return l.push(W),l.push(X),g(O,w,X,I,E)},y=g(f,"unsortedSegmentSum",a,m,o),x=ve({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 Cle={kernelName:Sd,backendName:"webgl",kernelFunc:Ile},Tle=[tae,rae,Lee,Wee,Gee,qee,Kee,Jee,ete,nte,ote,lte,dte,fte,vte,yte,Ste,Nte,Cte,Dte,Fte,Ote,Bte,qte,Kte,Yte,sne,ane,une,pne,bee,yne,Tne,Ene,vne,$ne,Pne,_ne,zne,Wne,Gne,jne,Xne,Yne,sse,ase,Qne,lse,dse,hse,yse,vse,Ise,Nse,Ese,Rse,Dse,Fse,Ose,zse,Bse,Gse,qse,Zse,Jse,tre,rre,lre,pre,xee,fre,mne,yre,bre,kre,wee,Tre,_re,$re,Bre,Mre,Gre,qre,Yre,oae,fae,pae,Aae,bae,wae,cae,Sae,Cae,Rae,Fae,zae,jae,Tee,Xae,Yae,eoe,soe,Qte,ooe,loe,coe,hoe,yoe,See,xoe,boe,ene,Vae,koe,_oe,Toe,Eee,Poe,zoe,Voe,Hoe,Koe,Yoe,eie,sie,aie,lie,die,hie,gie,xie,wie,Hte,Gae,Iie,Tie,Eie,_ie,$ie,Pie,Mie,Lie,Wie,Gie,jie,Xie,Yie,Qie,tle,sle,Uae,Oee,ole,ule,ple,gle,xle,Mee,vle,kle,Cle,ioe];for(let e of Tle)dr(e);var Vr=Y();Vr.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Vr.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Vr.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Vr.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Vr.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Vr.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Vr.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Vr.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Vr.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Vr.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function Nle(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${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 a0(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function o0(){return`
|
|
[[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]]
|
|
`}function Fx(){return`
|
|
${o0()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(global_invocation_id)]] globalId : vec3<u32>,
|
|
[[builtin(num_workgroups)]] numWorkgroups: vec3<u32>)
|
|
`}function Ac(){return`
|
|
${o0()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(global_invocation_id)]] globalId : vec3<u32>)
|
|
`}function Ke(){return`
|
|
${Fx()} {
|
|
let index = getGlobalIndex(globalId, localId, numWorkgroups);
|
|
`}function Ele(e,t,n,s=!1){let r=`
|
|
let workGroupSizeX = ${n.workGroupSize[0]}u;
|
|
let workGroupSizeY = ${n.workGroupSize[1]}u;
|
|
let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(s===!0){let h=R4(t.shape),f=`
|
|
[[block]] struct Matrix0 {
|
|
numbers: array<${a0(t.dtype,n.isVec4)}>;
|
|
};
|
|
[[block]] 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[N4,f,r,E4,h,n.getUserCode()].join(`
|
|
`)}let a=[],o="[[block]] 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: ${wn(i)}; `,n.size&&(o+="size : i32; "),n.uniforms&&(o+=n.uniforms),o+="};",a.push(o),n.atomic?a.push(`
|
|
[[block]] struct Matrix0 {
|
|
numbers: array<atomic<i32>>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, read_write> result : Matrix0;
|
|
`):a.push(`
|
|
[[block]] struct Matrix0 {
|
|
numbers: array<${a0(t.dtype,n.isVec4)}>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
|
|
`),n.variableNames.forEach((h,f)=>{a.push(`
|
|
[[block]] struct Matrix${1+f} {
|
|
numbers: array<${a0(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]=Ple(t.shape,n.dispatchLayout),u=R4(t.shape),d=[N4,a.join(`
|
|
`),E4,u,l,Rle(t.shape.length)];if(n.atomic||d.push(_le(t.shape,t.dtype,n.isVec4)),c===t.shape.length){let h=e.map(f=>Dle(f,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
|
|
`);d.push(h)}return d.push(n.getUserCode()),d.join(`
|
|
`)}var N4=`
|
|
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 isNanCustomVec4F32(val : vec4<f32>) -> vec4<f32> {
|
|
var res = vec4<f32> (0.0);
|
|
for (var i = 0u; i < 4u; i = i + 1u) {
|
|
if (isNanCustom(val[i])) {
|
|
res[i] = 1.0;
|
|
} else {
|
|
res[i] = 0.0;
|
|
}
|
|
}
|
|
return res;
|
|
}
|
|
|
|
// 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);
|
|
}
|
|
`,E4=`
|
|
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(globalId : vec3<u32>, localId : vec3<u32>, numWorkgroups: vec3<u32>) -> 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 Rle(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 _le(e,t,n){let s=e.length,r=a0(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}(${o.join(", ")}));
|
|
setOutputFlatI32(flatIndex, value);
|
|
}
|
|
`}return a}function Dle(e,t,n,s){let r=$le(e,n);return e.shape.length<=t.length&&(r+=Fle(e,t,n,s)),r}function $le(e,t){let n=e.name,s=e.shape.length,r=wn(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,s),i=o.map(u=>`${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 Fle(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 Ple(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoordsWithFlatDispatchLayout(globalId : vec3<u32>, localId : vec3<u32>, numWorkgroups: vec3<u32>) -> ${wn(a)}{
|
|
let globalIndex = getGlobalIndex(globalId, localId, numWorkgroups);
|
|
return getCoordsFromFlatIndex(globalIndex);
|
|
}
|
|
`,a];let o="",i=[n,s,r],l=0;for(let p=0;p<i.length;p++){let h=i[p];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${p}]);`;else{let f=Nle(h,"uniforms.outShape");o+=`var index${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 getOutputCoordsWithNonFlatDispatchLayout(globalId : vec3<u32>) -> ${u} {
|
|
${o}
|
|
`;return c.length===0?d+=`return ${u}(0); }`:d+=`return ${u}(${c.join(",")}); }`,[d,l]}function R4(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 _4={};Me(_4,{ArrayBufferToTypedArray:()=>D4,GPUBytesPerElement:()=>zx,computeDispatch:()=>Oe,computeWorkGroupSizeForConv2d:()=>Px,computeWorkGroupSizeForMatMul:()=>Ox,computeWorkPerThreadForConv2d:()=>Mx,flatDispatchLayout:()=>He,isWebGPUSupported:()=>Lx,tilesFitEvenlyIntoShape:()=>ia});var xc=65535,Dl=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function ia(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(Dl(e.x.map(l=>t[l]))/(n[0]*s[0])),e.y?Math.ceil(Dl(e.y.map(l=>t[l]))/(n[1]*s[1])):1,e.z?Math.ceil(Dl(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 Px(e,t){let n=Dl(e.x.map(r=>t[r])),s=Dl(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function Ox(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function Mx(e,t){let n=Dl(e.x.map(r=>t[r])),s=Dl(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 zx(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function D4(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 Lx(){return!!navigator.gpu}var Gt;(function(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"})(Gt||(Gt={}));var Ole="return a + b;",Mle="return areal * breal - aimag * bimag;",zle="return areal * bimag + aimag * breal;",Lle="return a / b;",Ble="return a * b;",Wle="return (a - b) * (a - b);",Vle="return 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(a < b);",Zle="return vec4<f32>(a < b);",Yle="return f32(a <= b);",Jle="return vec4<f32>(a <= b);",Qle="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",eue=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
|
|
vec4<f32>(b >= vec4<f32>(1.0)));`,tue=`
|
|
if (isNanCustom(a)) { return a; }
|
|
if (isNanCustom(b)) { return b; }
|
|
`,$4=`
|
|
if (isNaN.r > 0.) {
|
|
resultTemp.r = uniforms.NAN;
|
|
}
|
|
if (isNaN.g > 0.) {
|
|
resultTemp.g = uniforms.NAN;
|
|
}
|
|
if (isNaN.b > 0.) {
|
|
resultTemp.b = uniforms.NAN;
|
|
}
|
|
if (isNaN.a > 0.) {
|
|
resultTemp.a = uniforms.NAN;
|
|
}
|
|
`,nue=`
|
|
let s = sign(a) * sign(b);
|
|
let ia = i32(round(a));
|
|
let ib = i32(round(b));
|
|
return f32(idiv(ia, ib, s));
|
|
`,sue=`
|
|
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);
|
|
`,rue="return f32(a != b);",aue="return vec4<f32>(a != b);",oue=`
|
|
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);
|
|
`,iue=`
|
|
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 = vec4<f32>(a < vec4<f32>(0.0)) * vec4<f32>(floor(b) < b);
|
|
${$4}
|
|
return resultTemp;
|
|
`,lue="if (a < 0.0) { return b * a; } return a;",uue=`
|
|
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
|
|
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
|
|
`;function F4(e,t){let n=t?$4:tue;return t?`
|
|
var resultTemp = vec4<f32>(${e}(a, b));
|
|
let isNaN = min(vec4<f32>(isNanCustomVec4F32(a)) + vec4<f32>(isNanCustomVec4F32(b)), vec4<f32>(1.0));
|
|
`+n+`
|
|
return resultTemp;
|
|
`:n+`
|
|
return ${e}(a, b);
|
|
`}function Rp(e,t){switch(e){case 0:return Ble;case 1:return Ole;case 2:return Vle;case 3:return Lle;case 4:return t?Gle:Ule;case 5:return t?jle:Hle;case 6:return t?Xle:qle;case 7:return t?Zle:Kle;case 8:return t?Jle:Yle;case 9:return t?eue:Qle;case 10:return t?aue:rue;case 11:return Wle;case 12:return t?sue:nue;case 14:return t?uue:lue;case 15:return F4("max",t);case 16:return F4("min",t);case 13:return t?iue:oue;case 17:return Mle;case 18:return zle;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var wt;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.LINEAR=8]="LINEAR",e[e.LOG=9]="LOG",e[e.LOGICAL_NOT=10]="LOGICAL_NOT",e[e.NEG=11]="NEG",e[e.PRELU=12]="PRELU",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.RSQRT=15]="RSQRT",e[e.SIN=16]="SIN",e[e.SINH=17]="SINH",e[e.SIGMOID=18]="SIGMOID",e[e.SQRT=19]="SQRT",e[e.SQUARE=20]="SQUARE",e[e.TANH=21]="TANH",e[e.TO_INT=22]="TO_INT"})(wt||(wt={}));var cue="return abs(a);",due="return ceil(a);",pue="return cos(a);",hue=`
|
|
let e2x = exp(-a);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,fue="return exp(a) - 1.0;",mue="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",gue=`
|
|
var resFloat = exp(a) - vec4<f32>(1.0);
|
|
if (a.r >= 0.0) {
|
|
resFloat.r = a.r;
|
|
}
|
|
if (a.g >= 0.0) {
|
|
resFloat.g = a.g;
|
|
}
|
|
if (a.b >= 0.0) {
|
|
resFloat.b = a.b;
|
|
}
|
|
if (a.a >= 0.0) {
|
|
resFloat.a = a.a;
|
|
}
|
|
return resFloat;
|
|
`,yue="return exp(a);",Aue="return floor(a);",xue="return a;",bue=`if (a < 0.0) { return 1.0/0.0; }
|
|
return log(a);`,vue="return f32(!(a >= 1.0));",wue="return -a;",kue="return (a < 0.0) ? b * a : a;",Sue="return max(a, 0.0);",Iue="return clamp(a, 0.0, 6.0);",Cue="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Tue=`
|
|
var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
|
|
let isNaN = isNan(a);
|
|
|
|
if (isNaN.r) {
|
|
resFloat.r = a.r;
|
|
}
|
|
if (isNaN.g) {
|
|
resFloat.g = a.g;
|
|
}
|
|
if (isNaN.b) {
|
|
resFloat.b = a.b;
|
|
}
|
|
if (isNaN.a) {
|
|
resFloat.a = a.a;
|
|
}
|
|
return resFloat;
|
|
`,Nue="return 1.0/sqrt(a);",Eue="return 1.0 / (1.0 + exp(-1.0 * a));",Rue="return sin(a);",_ue=`
|
|
let e2x = exp(a);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,Due="return sqrt(a);",$ue="return a * a;",Fue=`
|
|
let e2x = exp(-2.0 * abs(a));
|
|
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,Pue="return f32(i32((a)));";function bc(e,t){switch(e){case 0:return cue;case 2:return pue;case 3:return hue;case 1:return due;case 4:return t?gue:mue;case 5:return yue;case 6:return fue;case 7:return Aue;case 8:return xue;case 9:return bue;case 10:return vue;case 11:return wue;case 12:return kue;case 13:return t?Tue:Sue;case 14:return t?Cue:Iue;case 15:return Nue;case 18:return Eue;case 16:return Rue;case 17:return _ue;case 19:return Due;case 20:return $ue;case 21:return Fue;case 22:return Pue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function la(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(Gt.PRELU,t);if(e==="sigmoid")return bc(wt.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function P4(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};
|
|
|
|
${Ac()} {
|
|
|
|
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 Oue(e){return`
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
let tileSize = ${e[0]*4};
|
|
${Ac()} {
|
|
let tileCol = i32(localId.x);
|
|
let globalCol = i32(globalId.x);
|
|
let globalRow = i32(globalId.y);
|
|
|
|
let numTiles = (uniforms.dimInner - 1) / tileSize + 1;
|
|
|
|
// Without this initialization strange values show up in acc.
|
|
var acc = vec4<f32>(0.0);
|
|
|
|
// Loop over shared dimension.
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
// Load one tile of A into local memory.
|
|
let colA = t * tileSize / 4 + tileCol;
|
|
mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId);
|
|
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < tileSize / 4; k = k + 1) {
|
|
let rowB = t * tileSize + k * 4;
|
|
let BCached0 = mm_readB(rowB, globalCol, globalId);
|
|
let BCached1 = mm_readB(rowB + 1, globalCol, globalId);
|
|
let BCached2 = mm_readB(rowB + 2, globalCol, globalId);
|
|
let BCached3 = mm_readB(rowB + 3, globalCol, globalId);
|
|
|
|
let ACached = mm_Asub[k];
|
|
acc = acc + BCached0 * ACached.x;
|
|
acc = acc + BCached1 * ACached.y;
|
|
acc = acc + BCached2 * ACached.z;
|
|
acc = acc + BCached3 * ACached.w;
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
|
|
mm_write(globalRow, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var Mue=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=Ox(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${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[ia(o,this.aShape.slice(1)),ia(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=la(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?P4([this.vecSize,this.workPerThread,1],this.workGroupSize):Oue(this.workGroupSize)}
|
|
|
|
`}};function Bx(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}>;
|
|
${Ac()} {
|
|
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 zue(e){return`
|
|
let TileSize = ${e[0]*4};
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
|
|
${Ac()} {
|
|
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 O4=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=Ox(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[ia(r,this.aShape.slice(1)),ia(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=la(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?Bx([this.workPerThread,this.workPerThread,1],this.workGroupSize):zue(this.workGroupSize)}
|
|
`}};function Lue(){return`
|
|
var<workgroup> sumValues : array<f32, workGroupSizeX>;
|
|
${Ac()} {
|
|
let coords = getOutputCoordsWithNonFlatDispatchLayout(globalId);
|
|
let batch = coords[0];
|
|
let row = coords[1];
|
|
let col = coords[2];
|
|
var sum = 0.0;
|
|
let Length = uniforms.dimInner;
|
|
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
|
|
let dataA = mm_readA(batch, row, k);
|
|
let dataB = mm_readB(batch, k, col);
|
|
sum = sum + dataA * dataB;
|
|
}
|
|
sumValues[localId.x] = sum;
|
|
workgroupBarrier();
|
|
|
|
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
|
|
currentSize = currentSize / 2u) {
|
|
if (localId.x < currentSize)
|
|
{
|
|
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
|
|
}
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (localId.x == 0u) {
|
|
sum = sumValues[0] + sumValues[1];
|
|
mm_write(batch, row, col, sum);
|
|
}
|
|
}
|
|
`}var Bue=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulReduce_${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=la(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);
|
|
}
|
|
${Lue()}
|
|
`}};function Wue(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.
|
|
${Ac()} {
|
|
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 >= ${t} && writeCol >= 0) {
|
|
mm_write(writeCol, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var Vue=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${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=la(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);
|
|
}
|
|
}
|
|
${Wue(this.workGroupSize)}
|
|
`}};function qe(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Uue={kernelName:Oi,backendName:"webgpu",kernelFunc:qe};function Wx({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),b=sl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[y,d,h]:[y,h,d],k=s?[x,f,p]:[x,p,f],I=qe({inputs:{x:e},backend:r,attrs:{shape:w}}),E=qe({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[I,E],P=Math.max(y,x),D=d%4==0&&f%4==0&&!n&&!s&&f>=32,_;h*f<=32?_=new Bue([P,h,f],n,s,a,l,o):!n&&!s&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?_=new Vue(w,k,[P,h,f],a,l,o):D?_=new Mue(w,[P,h,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):_=new O4(w,[P,h,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,l,o);let T=[I,E];a&&T.push(a),o&&T.push(o);let O=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],W=r.runWebGPUProgram(_,T,e.dtype,O),X=qe({inputs:{x:W},backend:r,attrs:{shape:b}});R.push(W);for(let z of R)r.disposeData(z.dataId);return X}function Gue(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return Wx({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var Hue={kernelName:vo,backendName:"webgpu",kernelFunc:Gue},M4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
|
|
fn binaryOpComplex(
|
|
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
|
|
${Rp(this.op,!1)}
|
|
}
|
|
|
|
${Ke()}
|
|
if(index < uniforms.size) {
|
|
let areal = getARealAtOutCoordsByGlobalIndex(index);
|
|
let aimag = getAImagAtOutCoordsByGlobalIndex(index);
|
|
let breal = getBRealAtOutCoordsByGlobalIndex(index);
|
|
let bimag = getBImagAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
}
|
|
`}},jue=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=He(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${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);
|
|
|
|
${t}
|
|
setOutputFlat(flatIndex, binaryOperation(a, b));
|
|
}
|
|
}
|
|
}
|
|
`}},que=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
|
|
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
|
|
${Rp(this.op,this.isVec4)}
|
|
}
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
let b = getBAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}},z4=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
|
|
fn binaryOperation(a : f32, b : f32) -> f32 {
|
|
${Rp(this.op,!1)}
|
|
}
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
let b = getBAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}};function L4(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4==0)return new que(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new jue(e,t,n,a):new z4(e,t,n)}function or(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var Xue={kernelName:qa,backendName:"webgpu",kernelFunc:or};function vc(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=or({inputs:{x:s},backend:n}),l=or({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var Kue={kernelName:ud,backendName:"webgpu",kernelFunc:vc},i0=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${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 Tn({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 i0(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!==Gt.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=L4(e,o.shape,i.shape);return l.runWebGPUProgram(w,[A,b],zn(y.dtype,x.dtype))});else{let g=new M4(Gt.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),y=new M4(Gt.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||zn(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=L4(e,o.shape,i.shape);return l.runWebGPUProgram(u,[o,i],c)}}var{addImpl:Zue,ceilImpl:Yue,concatImpl:Jue,equalImpl:Que,expImpl:ece,expm1Impl:tce,floorImpl:nce,gatherNdImpl:sce,gatherV2Impl:rce,greaterEqualImpl:ace,greaterImpl:oce,lessEqualImpl:ice,lessImpl:lce,logImpl:uce,maxImpl:cce,maximumImpl:dce,minimumImpl:pce,multiplyImpl:hce,negImpl:fce,notEqualImpl:mce,prodImpl:gce,rangeImpl:yce,rsqrtImpl:Ace,simpleAbsImpl:xce,sliceImpl:bce,stridedSliceImpl:vce,stringNGramsImpl:wce,subImpl:kce,tileImpl:Sce,topKImpl:Ice,transposeImpl:Cce,uniqueImpl:Y1e}=Dm,Tce=Tn({opType:wt.ABS,cpuKernelImpl:xce}),Nce={kernelName:di,backendName:"webgpu",kernelFunc:Tce},Ece=qn({opSnippet:Gt.ADD,cpuKernelImpl:Zue,supportsComplex:!0}),Rce={kernelName:qr,backendName:"webgpu",kernelFunc:Ece},_ce=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 Dce(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return or({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>zn(i,l)),a=s.map(i=>i.shape),o=new _ce(a);return n.runWebGPUProgram(o,s,r)}var $ce={kernelName:Ea,backendName:"webgpu",kernelFunc:Dce},B4=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32; infinityValue : f32;",this.size=!0;let s=[t];N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=N.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${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]);
|
|
}
|
|
}
|
|
`}},Fce=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]}>;
|
|
${o0()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(workgroup_id)]] workgroupId : vec3<u32>) {
|
|
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
|
|
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
|
|
let width = uniforms.outShape[0];
|
|
let height = uniforms.outShape[1];
|
|
if (x < width && y < height) {
|
|
tile[localId.y][localId.x] =
|
|
A.numbers[y * width + x];
|
|
}
|
|
workgroupBarrier();
|
|
|
|
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
|
|
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
|
|
if (x < height && y < width) {
|
|
setOutputFlat((y * height + x), tile[localId.x]
|
|
[localId.y]);
|
|
}
|
|
}
|
|
`}},Pce=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=wn(this.outputShape.length),t=Oce(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 Oce(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 $l(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];if(n.shouldExecuteOnCPU([r])){let d=o.tensorMap.get(r.dataId).values,p=Cce(d,r.shape,r.dtype,a,l);return n.makeTensorInfo(l,r.dtype,p)}if(r.shape.length===2&&v.arraysEqual(a,[1,0])){let u=new Fce(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}let c=new Pce(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}var Mce={kernelName:xo,backendName:"webgpu",kernelFunc:$l};function zce(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=$l({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=N.getInnerMostAxes(o.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=new B4(l.shape,o[0],"max"),d=[{type:"int32",data:[o[0]]},{type:"float32",data:[Number.NEGATIVE_INFINITY]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var Lce={kernelName:Ra,backendName:"webgpu",kernelFunc:zce};function Bce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=$l({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 B4(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 Wce={kernelName:cu,backendName:"webgpu",kernelFunc:Bce},W4=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});
|
|
}
|
|
}
|
|
`}},V4=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 Vce(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 or({inputs:{x:r},backend:n});let d,p=[{type:"int32",data:[u.strideHeight,u.strideWidth]}];return u.filterHeight===1&&u.filterWidth===1?d=new V4(u):(d=new W4(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 Uce={kernelName:_a,backendName:"webgpu",kernelFunc:Vce};function Gce(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Wx({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var Hce={kernelName:Da,backendName:"webgpu",kernelFunc:Gce},jce=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 : ${wn(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=wn(this.rank),t=qce(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Vx[a]} = uniforms.start[${a}] + coords.${Vx[a]};`),`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
var sourceLoc : ${e};
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
${n.join(`
|
|
`)}
|
|
setOutputFlat(index, getSource(${t}));
|
|
}
|
|
}
|
|
`}},Vx=["x","y","z","w","u","v"];function qce(e){if(e===1)return"sourceLoc";if(e<=6)return Vx.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function wc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Mt.parseSliceParams(r,a,o);if(Mt.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.tensorMap.get(r.dataId),p=bce(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}if(v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);let c=new jce(i,l),u=[{type:"int32",data:i}];return n.runWebGPUProgram(c,[r],r.dtype,u)}var Xce={kernelName:Wi,backendName:"webgpu",kernelFunc:wc},Kce=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((x,A)=>x*A),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=[],f=qe({inputs:{x:r},backend:n,attrs:{shape:l}}),m=$l({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},Zce={kernelName:pi,backendName:"webgpu",kernelFunc:Kce},U4=qn({opSnippet:Gt.NOT_EQUAL,dtype:"bool",cpuKernelImpl:mce}),Yce={kernelName:Ei,backendName:"webgpu",kernelFunc:U4};function _p(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return or({inputs:{x:r.complexTensorInfos.real},backend:n})}var Jce={kernelName:Ad,backendName:"webgpu",kernelFunc:_p};function Qce(e,t){let n=new i0(e.shape,wt.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Ux(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return or({inputs:{x:r},backend:n});let o=jt(r.shape),i=Ux({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=_p({inputs:{input:r},backend:n}),i=Ux({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=or({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Qce(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=U4({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 ${a}`)}var ede={kernelName:$a,backendName:"webgpu",kernelFunc:Ux},tde=Tn({opType:wt.CEIL,cpuKernelImpl:Yue}),nde={kernelName:Fa,backendName:"webgpu",kernelFunc:tde},sde=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);
|
|
}
|
|
}
|
|
`}},rde=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`
|
|
${Ke()}
|
|
if(index < uniforms.size) {
|
|
let value = getAAtOutCoordsByGlobalIndex(index);
|
|
if (isNanCustom(value)) {
|
|
setOutputFlat(index, value);
|
|
return;
|
|
}
|
|
setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal));
|
|
}
|
|
}
|
|
`}};function ade(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4==0?i=new sde(r.shape):i=new rde(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var ode={kernelName:Xr,backendName:"webgpu",kernelFunc:ade},ide=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${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 l0(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return or({inputs:{x:r.complexTensorInfos.imag},backend:n})}var lde={kernelName:fd,backendName:"webgpu",kernelFunc:l0};function Gx(e,t,n){let s=e[0].dtype;if(s==="complex64"){let h=e.map(x=>_p({inputs:{input:x},backend:n})),f=e.map(x=>l0({inputs:{input:x},backend:n})),m=Gx(h,t,n),g=Gx(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=Jue(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}=ude(e,t,n),i=a.map(h=>h.shape),l=new ide(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 ude(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 G4(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 or({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return N.assertParamsConsistent(l,a),Gx(i,a,n)}var cde={kernelName:hi,backendName:"webgpu",kernelFunc:G4},dde=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 H4({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=Wx({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 pde({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,k=[w,b],I=!1,E=!1,R=[],P=qe({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),D=qe({inputs:{x:t},backend:s,attrs:{shape:[1,b,-1]}});R.push(P),R.push(D);let _=new dde(k,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),W=qe({inputs:{x:O},backend:s,attrs:{shape:[1,k[0],k[1]]}});R.push(O),R.push(W);let X=[1,k[0],k[1]],z=new O4(X,[1,w,n.outChannels],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),I,E),j=X[1],Z=X[2],Q=n.outChannels,ne=[{type:"int32",data:[j]},{type:"int32",data:[Q]},{type:"int32",data:[Z]}],ae=s.runWebGPUProgram(z,[W,D],W.dtype,ne),U=A?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],oe=qe({inputs:{x:ae},backend:s,attrs:{shape:U}});R.push(ae);for(let re of R)s.disposeData(re.dataId);return oe}var j4=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[ia(r,[o,l]),ia(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=P4([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=la(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);
|
|
}
|
|
}
|
|
${t}
|
|
`}},q4=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Px(this.dispatchLayout,this.outputShape),this.elementsPerThread=Mx(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[ia(s,[a,i]),ia(r,[i,o])]}getUserCode(){let e=Bx(this.elementsPerThread,this.workGroupSize),t=`
|
|
let outRow = row / uniforms.outShape[2];
|
|
let outCol = row % uniforms.outShape[2];
|
|
|
|
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
|
|
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
|
|
let coord = vec4<i32>(
|
|
batch,
|
|
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
|
|
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
|
|
col % uniforms.xShape[3]);
|
|
// The bounds checking is always needed since we use it to pad zero for the
|
|
// 'same' padding type.
|
|
if(coordsInBounds4D(coord, uniforms.xShape)) {
|
|
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
|
|
}
|
|
return 0.0;`,n=this.fitA?`${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=la(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;
|
|
}
|
|
${e}
|
|
`}},X4=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=la(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);
|
|
}
|
|
}
|
|
|
|
${Fx()} {
|
|
let coords = getOutputCoordsWithFlatDispatchLayout(globalId, localId, numWorkgroups);
|
|
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 hde(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 H4({x:r,filter:a,convInfo:p,backend:s});if(Y().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&r.shape[0]===1)return pde({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 X4(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new j4(p):h=new q4(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 fde={kernelName:Pa,backendName:"webgpu",kernelFunc:hde},mde=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=Px(this.dispatchLayout,this.outputShape),this.elementsPerThread=Mx(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;
|
|
}
|
|
|
|
${Bx(this.elementsPerThread,this.workGroupSize)}
|
|
`}},gde=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${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 (${this.isChannelsLast}) {
|
|
let xValue = getDy(batch, idyR, idyC, d2);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
} else {
|
|
let xValue = getDy(batch, d2, idyR, idyC);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutputFlat(index, dotProd);
|
|
}
|
|
}
|
|
`}};function yde(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=N.convertConv2DDataFormat(c),p=N.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Y().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new gde(p);else{f=new mde(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var Ade={kernelName:Oa,backendName:"webgpu",kernelFunc:yde},xde=Tn({opType:wt.COS}),bde={kernelName:Ma,backendName:"webgpu",kernelFunc:xde},vde=Tn({opType:wt.COSH}),wde={kernelName:za,backendName:"webgpu",kernelFunc:vde},kde=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${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);
|
|
}
|
|
}
|
|
}
|
|
`}},Sde=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 kde(r.shape[3],a.shape,i,l),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(u,[r,a,o],"float32",d)},Ide={kernelName:mi,backendName:"webgpu",kernelFunc:Sde},Cde=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 = ${this.getInputSamplingString()};
|
|
setOutputFlat(index, rlt);
|
|
}
|
|
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Tde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new Cde(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Nde={kernelName:gi,backendName:"webgpu",kernelFunc:Tde},K4=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=la(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}
|
|
|
|
${o0()}
|
|
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}
|
|
${t}
|
|
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
|
|
}
|
|
}
|
|
}
|
|
`}},Z4=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
|
|
inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;
|
|
channelMul : i32;`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=la(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);
|
|
}
|
|
}
|
|
|
|
${Fx()} {
|
|
let coords = getOutputCoordsWithFlatDispatchLayout(globalId,
|
|
localId, numWorkgroups);
|
|
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}
|
|
${t}
|
|
writeResult(batch, coords[1], coords[2], d2, dotProd);
|
|
}
|
|
`}};function Ede(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]);let d=N.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}],h;return d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?h=new K4(d):(h=new Z4(d),p.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.outChannels/d.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,p)}var Rde={kernelName:La,backendName:"webgpu",kernelFunc:Ede},Y4=qn({opSnippet:Gt.MUL,cpuKernelImpl:hce,supportsComplex:!0}),_de={kernelName:no,backendName:"webgpu",kernelFunc:Y4},Dde=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${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) {
|
|
${n}
|
|
}
|
|
}
|
|
`}};function Dp(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,c=N.getAxesPermutation(l,a),u=e;c!=null&&(u=$l({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=cce(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=gce(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":Fd(e.dtype),b=[{type:"int32",data:[m]}],w=new Dde(x,s),k=r.runWebGPUProgram(w,[u],A,b);o.push(k),f=qe({inputs:{x:k},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function Hx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Dp(r,a,o,"sum",n)}var $de={kernelName:fo,backendName:"webgpu",kernelFunc:Hx};function Fde(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=N.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:y,expandDims:x}=N.getEinsumPermutation(h,l[g]),A;N.isIdentityPermutation(y)?A=a[g]:(A=$l({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=Y4({inputs:{a:A,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Hx({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 Pde={kernelName:hd,backendName:"webgpu",kernelFunc:Fde},Ode=Tn({opType:wt.ELU}),Mde={kernelName:Wa,backendName:"webgpu",kernelFunc:Ode},zde=qn({opSnippet:Gt.EQUAL,dtype:"bool",cpuKernelImpl:Que}),Lde={kernelName:yi,backendName:"webgpu",kernelFunc:zde},J4=Tn({opType:wt.EXP,cpuKernelImpl:ece,dtype:"float32"}),Bde={kernelName:Va,backendName:"webgpu",kernelFunc:J4};function jx(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 Wde={kernelName:Ai,backendName:"webgpu",kernelFunc:jx},Vde=Tn({opType:wt.EXPM1,cpuKernelImpl:tce}),Ude={kernelName:xi,backendName:"webgpu",kernelFunc:Vde},Gde=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 Gde(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var Hde={kernelName:yu,backendName:"webgpu",kernelFunc:kc},jde=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);
|
|
}
|
|
}
|
|
`}},qde={kernelName:bi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new jde(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},Xde=Tn({opType:wt.FLOOR,cpuKernelImpl:nce}),Kde={kernelName:Ua,backendName:"webgpu",kernelFunc:Xde},Zde=qn({opSnippet:Gt.INT_DIV,dtype:"int32"}),Yde={kernelName:Ga,backendName:"webgpu",kernelFunc:Zde},Jde=(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}))})},Q4=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=Ele(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 e6(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 t6(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=e6(u,d,p),f=u.getLayout(n.device),m=n.getAndSavePipeline(h,()=>Q4(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 Qde={kernelName:Id,backendName:"webgpu",kernelFunc:epe},Sc;function epe(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 t6({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 t6({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 tpe=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)));
|
|
}
|
|
}
|
|
`}},npe={kernelName:Ha,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 tpe(s.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[l]}];return c.runWebGPUProgram(h,u,s.dtype,f)}};function spe(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 H4({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,k=[g.padInfo.top,g.padInfo.left],I=[{type:"int32",data:[g.filterHeight,g.filterWidth]},{type:"int32",data:[...k]},{type:"int32",data:[g.strideHeight,g.strideWidth]},{type:"int32",data:[g.dilationHeight,g.dilationWidth]}];if(b)A=new X4(g,y,h,x);else{w?A=new j4(g,y,h,x):A=new q4(g,y,h,x);let R=g.outShape[1]*g.outShape[2],P=g.outShape[3],D=g.filterHeight*g.filterWidth*g.inShape[3];I.push({type:"int32",data:[R]},{type:"int32",data:[P]},{type:"int32",data:[D]})}let E=[r,a];return y&&E.push(o),x&&E.push(i),n.runWebGPUProgram(A,E,r.dtype,I)}var rpe={kernelName:wo,backendName:"webgpu",kernelFunc:spe};function ape(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 K4(f,g,p,y):(A=new Z4(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 ope={kernelName:ko,backendName:"webgpu",kernelFunc:ape},ipe=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 lpe(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=sce(x,A,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new ipe(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 upe={kernelName:wi,backendName:"webgpu",kernelFunc:lpe},cpe=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=dpe(this.aShape,"i32");return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function dpe(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 n6(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),k=n.tensorMap.get(p.dataId).values,I=ze(p.shape,p.dtype,k),E=rce(I,b,f);return d.forEach(R=>n.disposeData(R.dataId)),n.makeTensorInfo(c.outputShape,E.dtype,E.values)}let m=new cpe(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 ppe={kernelName:vi,backendName:"webgpu",kernelFunc:n6},hpe=qn({opSnippet:Gt.GREATER,cpuKernelImpl:oce,dtype:"bool"}),fpe={kernelName:ki,backendName:"webgpu",kernelFunc:hpe},mpe=qn({opSnippet:Gt.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:ace}),gpe={kernelName:ja,backendName:"webgpu",kernelFunc:mpe},ype=qn({opSnippet:Gt.LESS,dtype:"bool",cpuKernelImpl:lce}),Ape={kernelName:Ii,backendName:"webgpu",kernelFunc:ype},xpe=qn({opSnippet:Gt.LESS_EQUAL,dtype:"bool",cpuKernelImpl:ice}),bpe={kernelName:Ci,backendName:"webgpu",kernelFunc:xpe},vpe=Tn({opType:wt.LOG,cpuKernelImpl:uce}),wpe={kernelName:Xa,backendName:"webgpu",kernelFunc:vpe},kpe=qn({opSnippet:Gt.LOGICAL_AND,dtype:"bool"}),Spe={kernelName:Ti,backendName:"webgpu",kernelFunc:kpe},Ipe=Tn({opType:wt.LOGICAL_NOT}),Cpe={kernelName:wu,backendName:"webgpu",kernelFunc:Ipe};function s6(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return Dp(r,a,o,"max",n)}var Tpe={kernelName:Ka,backendName:"webgpu",kernelFunc:s6},Npe=qn({opSnippet:Gt.MAX,cpuKernelImpl:dce}),Epe={kernelName:Za,backendName:"webgpu",kernelFunc:Npe};function Rpe(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 or({inputs:{x:r},backend:n});d=new V4(u),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else d=new W4(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 _pe={kernelName:Ya,backendName:"webgpu",kernelFunc:Rpe};function Dpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return Dp(r,o,a,"mean",n)}var $pe={kernelName:Ja,backendName:"webgpu",kernelFunc:Dpe};function Fpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Dp(r,a,o,"min",n)}var Ppe={kernelName:Qa,backendName:"webgpu",kernelFunc:Fpe},Ope=qn({opSnippet:Gt.MIN,cpuKernelImpl:pce}),Mpe={kernelName:eo,backendName:"webgpu",kernelFunc:Ope},zpe=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${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}));
|
|
}
|
|
}
|
|
`}},Lpe={kernelName:to,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 zpe(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function Bpe(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=fce(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new i0(s.shape,wt.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var Wpe={kernelName:Ni,backendName:"webgpu",kernelFunc:Bpe};function Vpe(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}=tr.nonMaxSuppressionV3Impl(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Upe={kernelName:Ri,backendName:"webgpu",kernelFunc:Vpe};function Gpe(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}=tr.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 Hpe={kernelName:_i,backendName:"webgpu",kernelFunc:Gpe};function u0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=_p({inputs:{input:s},backend:n}),a=u0({inputs:{x:r},backend:n}),o=l0({inputs:{input:s},backend:n}),i=u0({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 jpe={kernelName:Zi,backendName:"webgpu",kernelFunc:u0};function r6(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=_p({inputs:{input:s},backend:n}),a=r6({inputs:{x:r},backend:n}),o=l0({inputs:{input:s},backend:n}),i=u0({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 qpe={kernelName:Di,backendName:"webgpu",kernelFunc:r6};function Xpe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return jx({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=jx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=G4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var Kpe={kernelName:Fi,backendName:"webgpu",kernelFunc:Xpe},Zpe=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}));
|
|
}
|
|
}
|
|
}
|
|
`}},a6=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 or({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 Zpe(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},Ype={kernelName:so,backendName:"webgpu",kernelFunc:a6},Jpe=qn({opSnippet:Gt.POW}),Qpe={kernelName:ro,backendName:"webgpu",kernelFunc:Jpe};function ehe(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new z4(Gt.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var the={kernelName:ao,backendName:"webgpu",kernelFunc:ehe};function nhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Dp(r,a,o,"prod",n)}var she={kernelName:Pi,backendName:"webgpu",kernelFunc:nhe},rhe=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=yce(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},ahe={kernelName:Iu,backendName:"webgpu",kernelFunc:rhe},o6=qn({opSnippet:Gt.DIV}),ohe={kernelName:Ba,backendName:"webgpu",kernelFunc:o6},ihe=Tn({opType:wt.RELU}),lhe={kernelName:oo,backendName:"webgpu",kernelFunc:ihe},uhe=Tn({opType:wt.RELU6}),che={kernelName:lo,backendName:"webgpu",kernelFunc:uhe},dhe=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 phe(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 dhe(r.shape,l,c);return n.runWebGPUProgram(f,[r],"float32",h)}var hhe={kernelName:io,backendName:"webgpu",kernelFunc:phe},fhe=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 mhe(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 fhe(r.shape,l,c,o);return n.runWebGPUProgram(f,[r],r.dtype,h)}var ghe={kernelName:Tu,backendName:"webgpu",kernelFunc:mhe},yhe=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));
|
|
${this.fillSnippet}
|
|
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
|
|
coordY < uniforms.xShape[1]) {
|
|
outputValue = getX(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutputFlat(index, outputValue);
|
|
}
|
|
}
|
|
`}},Ahe={kernelName:Yi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new yhe(s.shape,a),[c,u]=N.getImageCenter(o,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,d)}},xhe=Tn({opType:wt.RSQRT,cpuKernelImpl:Ace}),bhe={kernelName:uo,backendName:"webgpu",kernelFunc:xhe},vhe=class{constructor(e,t,n,s,r,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=He(e),this.dispatch=Oe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${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 whe(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 vhe(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 khe={kernelName:Li,backendName:"webgpu",kernelFunc:whe},She=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 Ihe(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new She(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],zn(r.dtype,a.dtype))}var Che={kernelName:Bi,backendName:"webgpu",kernelFunc:Ihe},The=Tn({opType:wt.SIGMOID}),Nhe={kernelName:po,backendName:"webgpu",kernelFunc:The},Ehe=Tn({opType:wt.SIN}),Rhe={kernelName:co,backendName:"webgpu",kernelFunc:Ehe},_he=Tn({opType:wt.SINH}),Dhe={kernelName:Vi,backendName:"webgpu",kernelFunc:_he},i6=qn({opSnippet:Gt.SUB,cpuKernelImpl:kce,supportsComplex:!0}),$he={kernelName:yo,backendName:"webgpu",kernelFunc:i6};function Fhe(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=s6({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,o),c=qe({inputs:{x:i},backend:n,attrs:{shape:l}}),u=i6({inputs:{a:r,b:c},backend:n}),d=J4({inputs:{x:u},backend:n}),p=Hx({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=qe({inputs:{x:p},backend:n,attrs:{shape:l}}),f=o6({inputs:{a:d,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(c.dataId),n.disposeData(u.dataId),n.disposeData(d.dataId),n.disposeData(p.dataId),n.disposeData(h.dataId),f}var Phe={kernelName:mo,backendName:"webgpu",kernelFunc:Fhe},Ohe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((y,x)=>y*x),l=[[0,0]];l.push(...o);for(let y=1+a.length;y<r.shape.length;++y)l.push([0,0]);let c=[],u=a6({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,a,i,!1),p=N.getPermuted(d.length,a.length,!1),h=N.getReshapedPermuted(u.shape,a,i,!1),f=qe({inputs:{x:u},backend:n,attrs:{shape:d}}),m=$l({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},Mhe={kernelName:Ui,backendName:"webgpu",kernelFunc:Ohe},zhe=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 Lhe(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 zhe(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 Bhe={kernelName:wd,backendName:"webgpu",kernelFunc:Lhe};function Whe(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 Vhe={kernelName:Gi,backendName:"webgpu",kernelFunc:Whe},Uhe=Tn({opType:wt.SQRT}),Ghe={kernelName:ho,backendName:"webgpu",kernelFunc:Uhe},Hhe={kernelName:Du,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new i0(n.shape,wt.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},jhe=qn({opSnippet:Gt.SQUARED_DIFFERENCE}),qhe={kernelName:go,backendName:"webgpu",kernelFunc:jhe},Xhe=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 Khe(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 k=Mt.computeOutShape(x,A,b),I=wc({inputs:{x:r},backend:n,attrs:{begin:x,size:k}});w=qe({inputs:{x:I},backend:n,attrs:{shape:f}}),n.disposeData(I.dataId)}else if(n.shouldExecuteOnCPU([r])){let I=n.readSync(r.dataId),E=ze(r.shape,r.dtype,I),R=vce(h,E,b,x);w=n.makeTensorInfo(f,r.dtype,R.values)}else{let I=new Xhe(h),E=[{type:"int32",data:x},{type:"int32",data:b}],R=n.runWebGPUProgram(I,[r],r.dtype,E);w=qe({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeData(R.dataId)}return w}var Zhe={kernelName:Hi,backendName:"webgpu",kernelFunc:Khe};function Yhe(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]=wce(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Jhe={kernelName:kd,backendName:"webgpu",kernelFunc:Yhe},Qhe=Tn({opType:wt.TANH}),efe={kernelName:Ao,backendName:"webgpu",kernelFunc:Qhe},tfe=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=nfe(this.rank,"uniforms.");return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function nfe(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 sfe(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=Sce(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new tfe(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var rfe={kernelName:Kr,backendName:"webgpu",kernelFunc:sfe},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; 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));
|
|
}
|
|
}
|
|
}
|
|
`}},ofe=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 l6(e){let t=1;for(;t<e;)t*=2;return t}function ife(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),[k,I]=Ice(w,i,r.dtype,a,o);return[n.makeTensorInfo(k.shape,k.dtype,k.values),n.makeTensorInfo(I.shape,I.dtype,I.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=l6(a),h=l6(l),f=null,m=()=>f===null?[d,d]:[d,f],g=(w,k,I)=>{let E=m(),R=new afe(I),D=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"float32",data:[Number.NEGATIVE_INFINITY]},{type:"int32",data:[w]},{type:"int32",data:[k]}],_=f;f=n.runWebGPUProgram(R,E,"int32",D),Ic(n,_)};for(let w=1;w<p;w*=2){let k=w*2;for(let I=w;I>=1;I/=2)g(k,I,[u,h])}for(let w=h;w>p;w/=2){let k=m(),I=new ofe([u,w/2]),R=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[p]}],P=f;f=n.runWebGPUProgram(I,k,"int32",R),Ic(n,P);let D=p/2,_=D*2;for(let T=D;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=n6({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 lfe={kernelName:qi,backendName:"webgpu",kernelFunc:ife},ufe=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 cfe(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 ufe(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 dfe={kernelName:Xi,backendName:"webgpu",kernelFunc:cfe};function pfe(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 hfe={kernelName:Ki,backendName:"webgpu",kernelFunc:pfe},ffe=[Hue,Nce,Rce,$ce,Lce,Wce,Uce,Hce,Zce,ede,nde,ode,Kue,cde,fde,Ade,bde,wde,Ide,Nde,Rde,Pde,Mde,Lde,Wde,Bde,Ude,Hde,qde,Qde,Kde,Yde,npe,rpe,ope,upe,ppe,fpe,gpe,Xue,lde,Ape,bpe,wpe,Spe,Cpe,Tpe,Epe,_pe,$pe,Ppe,Mpe,Lpe,_de,Wpe,Upe,Hpe,Yce,qpe,Kpe,Ype,the,she,Qpe,ahe,Jce,ohe,lhe,che,Uue,hhe,ghe,Ahe,bhe,khe,Che,Nhe,Rhe,Dhe,Xce,Zhe,Jhe,Phe,Mhe,Vhe,Bhe,Ghe,Hhe,qhe,$he,$de,efe,rfe,lfe,dfe,Mce,hfe,jpe];for(let e of ffe)dr(e);var mfe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t){let n=u6(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let r=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(r),r}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,n){if(this.freeBuffers==null)return;let s=u6(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}reset(){this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}dispose(){this.freeBuffers==null&&this.usedBuffers==null||(this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=null,this.usedBuffers=null,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0)}};function u6(e,t){return`${e}_${t}`}var c6=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}}},gfe=class extends c6{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}}},yfe=Y().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),d6=class extends nu{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,!Lx())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 mfe(this.device),this.tensorMap=new ad(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 d6.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)*zx(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)*zx(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 c6),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new gfe),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 ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=N.mergeRealAndImagArrays(a,o)}else{let r=await this.getBufferData(t);s=D4(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${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=e6(e,o,h,g,m),{bindGroupLayout:x,pipelineLayout:A}=this.getCachedOrCreateLayout(e.variableNames.length),b=this.getAndSavePipeline(y,()=>Q4(this.device,e,A,p,r)),w=this.activeTimers!=null,k=Jde(this.device,x,t.map(E=>this.tensorToBinding(E)),this.tensorToBinding(r),c);this.ensureCommandEncoderReady();let I=this.getComputePass();if(w&&this.supportTimeQuery&&I.writeTimestamp(this.querySet,0),I.setPipeline(b),I.setBindGroup(0,k),I.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),w&&this.supportTimeQuery&&I.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=yfe){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)}},qx=d6;qx.nextDataId=0;var p6={};Me(p6,{WebGPUBackend:()=>qx,webgpu_util:()=>_4});Pu.isBrowser()&&Lx()&&ol("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 qx(r,s)},3);var Ht;(function(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"})(Ht||(Ht={}));var $p;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})($p||($p={}));var h6;function Afe(e){h6=e.wasm.cwrap(vo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function xfe(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 ${E.shape.length}.`);f=E.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=$p[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=sl.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,k=new Uint8Array(new Int32Array(r.shape).buffer),I=new Uint8Array(new Int32Array(a.shape).buffer);return h6(p,k,r.shape.length,h,I,a.shape.length,l,c,g,f,m,d||0,w),b}var bfe={kernelName:vo,backendName:"wasm",setupFunc:Afe,kernelFunc:xfe};function Nn(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,Ht[i.dtype],u),c}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var vfe=Nn(di);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,Ht[c.dtype],x))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var wfe=!0,kfe=Xn(qr,wfe),f6;function Sfe(e){f6=e.wasm.cwrap(Ea,null,["array","number","number","number"])}function Ife(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 f6(a,r.length,Ht[s.dtype],o),s}var Cfe={kernelName:Ea,backendName:"wasm",setupFunc:Sfe,kernelFunc:Ife};function c0(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 Tfe={kernelName:qa,backendName:"wasm",kernelFunc:c0},m6;function Nfe(e){m6=e.wasm.cwrap(xo,null,["number","array","number","number","number","array","number"])}function Cc(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=Rfe(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=Efe(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=c0({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 m6(u,h,l.shape.length,Ht[l.dtype],d,p,a.length),c}function Efe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function Rfe(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 _fe={kernelName:xo,backendName:"wasm",kernelFunc:Cc,setupFunc:Nfe};function qo(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 g6;function Dfe(e){g6=e.wasm.cwrap(lu,null,["number, number, number"])}function $fe(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=qo(o,r,t);if(h){let A=t.dataIdMap.get(u.dataId).id;c=u,l=A}let f=c.shape.length;N.assertAxesAreInnerMostDims("all",d,f);let[m,g]=N.computeOutAndReduceShapes(c.shape,d),y=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let A=t.dataIdMap.get(x.dataId).id;g6(l,y,A)}if(h&&t.disposeData(u.dataId),a){let A=N.expandShapeToKeepDim(x.shape,p);x.shape=A}return x}var Ffe={kernelName:lu,backendName:"wasm",setupFunc:Dfe,kernelFunc:$fe},y6;function Pfe(e){y6=e.wasm.cwrap(uu,null,["number, number, number"])}function Ofe(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}=qo(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;y6(l,y,A)}if(h&&t.disposeData(u.dataId),a){let A=N.expandShapeToKeepDim(x.shape,p);x.shape=A}return x}var Mfe={kernelName:uu,backendName:"wasm",setupFunc:Pfe,kernelFunc:Ofe},A6;function zfe(e){A6=e.wasm.cwrap(Ra,null,["number","number","number","number","number"])}function Lfe(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}=qo(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 A6(i,Ht[l.dtype],m,g,f),d&&t.disposeData(c.dataId),h}var Bfe={kernelName:Ra,backendName:"wasm",kernelFunc:Lfe,setupFunc:zfe},x6;function Wfe(e){x6=e.wasm.cwrap(_a,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Vfe(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 x6(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,y,x,A,w),b}var Ufe={kernelName:_a,backendName:"wasm",setupFunc:Wfe,kernelFunc:Vfe};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: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Gfe={kernelName:Oi,backendName:"wasm",kernelFunc:ps},b6;function Hfe(e){b6=e.wasm.cwrap(Da,null,["number","array","number","number","array","number","number","number","number"])}function jfe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=a.shape.length,u=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[c-1]:a.shape[c-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[c-2]:a.shape[c-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),y=v.sizeFromShape(m),A=sl.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([p,h]);v.assert(u===d,()=>`Error in matMul: inner shapes (${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],k=ps({inputs:{x:r},backend:n,attrs:{shape:b}}),I=ps({inputs:{x:a},backend:n,attrs:{shape:w}}),E=n.dataIdMap.get(k.dataId).id,R=n.dataIdMap.get(I.dataId).id,P=o?k.shape[2]:k.shape[1],D=i?I.shape[1]:I.shape[2],_=Math.max(g,y),T=n.makeOutput([_,P,D],k.dtype),O=n.dataIdMap.get(T.dataId).id,W=new Uint8Array(new Int32Array(k.shape).buffer),X=new Uint8Array(new Int32Array(I.shape).buffer);return b6(E,W,k.shape.length,R,X,I.shape.length,o,i,O),n.disposeData(k.dataId),n.disposeData(I.dataId),T.shape=A,T}var qfe={kernelName:Da,backendName:"wasm",setupFunc:Hfe,kernelFunc:jfe};function Fl(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=Pm(l,a,o,t.shape,t.dtype);return d.stringBytes=f,c}let p=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)Xfe(l,u[0],p,a,o);else if(h===3)Kfe(l,u[0],u[1],p,a,o);else if(h===4)Zfe(l,u[0],u[1],u[2],p,a,o);else{let f=Pm(l,a,o,t.shape,t.dtype);p.set(f)}return c}function Xfe(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 Kfe(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 Zfe(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 Yfe={kernelName:Wi,backendName:"wasm",kernelFunc:Fl};function Jfe(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=Fl({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 Qfe={kernelName:pi,backendName:"wasm",kernelFunc:Jfe};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 eme={kernelName:$a,backendName:"wasm",kernelFunc:Fp},tme=Nn(Fa),v6;function nme(e){v6=e.wasm.cwrap(Xr,null,["number","number","number","number"])}function sme(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 v6(i,a,o,c),l}var rme={kernelName:Xr,backendName:"wasm",setupFunc:nme,kernelFunc:sme};function w6(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 c0({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=nx(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 x.stringBytes=N.fromStringArrayToUint8(g),h.forEach(A=>n.disposeData(A.dataId)),o}let l=v.sizeFromShape(a[0].shape.slice(0,s)),c=0,u=a.map(h=>{let f=v.sizeFromShape(h.shape.slice(s));return c+=f,f}),d=a.map(h=>n.typedArrayFromHeap(h)),p=n.typedArrayFromHeap(o);for(let h=0;h<l;h++){let f=h*c;for(let m=0;m<d.length;m++){let g=u[m],y=h*g,x=d[m].subarray(y,y+g);p.set(x,f),f+=g}}return o}var ame={kernelName:hi,backendName:"wasm",kernelFunc:w6},k6;function ome(e){k6=e.wasm.cwrap(Pa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function ime(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,k=f.dilationWidth,I=f.strideHeight,E=f.strideWidth,R=f.inChannels,P=f.outChannels,D=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let _=s.makeOutput(f.outShape,"float32"),T=s.dataIdMap.get(_.dataId).id;return k6(o,r.shape[0],r.shape[1],r.shape[2],i,m,g,y,x,A,b,D,w,k,I,E,R,P,T),_}var lme={kernelName:Pa,backendName:"wasm",setupFunc:ome,kernelFunc:ime},S6;function ume(e){S6=e.wasm.cwrap(Oa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function cme(e){let{backend:t,inputs:n,attrs:s}=e,{dy:r,filter:a}=n,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,inputShape:u}=s,d=1,p=N.convertConv2DDataFormat(l),h=N.computeConv2DInfo(u,a.shape,o,d,i,c,!1,p),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:y,inHeight:x,inWidth:A,outChannels:b,outHeight:w,outWidth:k,strideHeight:I,strideWidth:E}=h,R=m-1-h.padInfo.top,P=g-1-h.padInfo.left,D=h.dataFormat==="channelsLast",_=v.computeStrides(h.inShape),T=v.computeStrides(r.shape),[O,W,X]=v.computeStrides(a.shape),z=_[0],j=D?_[1]:_[2],Z=D?_[2]:1,Q=D?1:_[1],ne=T[0],ae=D?T[1]:T[2],U=D?T[2]:1,oe=D?1:T[1],re=t.makeOutput(h.inShape,"float32"),me=t.dataIdMap.get(re.dataId).id,Ae=t.dataIdMap.get(r.dataId).id,Te=t.dataIdMap.get(a.dataId).id;return S6(Ae,Te,f,m,g,x,A,y,w,k,b,I,E,R,P,O,W,X,z,j,Z,Q,ne,ae,U,oe,me),re}var dme={kernelName:Oa,backendName:"wasm",setupFunc:ume,kernelFunc:cme},pme=Nn(Ma),hme=Nn(za),Xx;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Xx||(Xx={}));var I6;function fme(e){I6=e.wasm.cwrap(mi,null,["number","number","number","number","array","number","number","number","number","number"])}function mme(e){let{backend:t,inputs:n,attrs:s}=e,{method:r,extrapolationValue:a,cropSize:o}=s,{image:i,boxes:l,boxInd:c}=n,u=l.shape[0],[d,p]=o,h=[u,d,p,i.shape[3]],f=t.dataIdMap.get(i.dataId),m;i.dtype!=="float32"&&(m=Fp({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,y=t.dataIdMap.get(l.dataId).id,x=t.dataIdMap.get(c.dataId).id,A=t.makeOutput(h,"float32"),b=t.dataIdMap.get(A.dataId).id,w=new Uint8Array(new Int32Array(i.shape).buffer);return I6(g,y,x,u,w,d,p,Xx[r],a,b),m!=null&&t.disposeData(m.dataId),A}var gme={kernelName:mi,backendName:"wasm",setupFunc:fme,kernelFunc:mme},C6;function yme(e){C6=e.wasm.cwrap(fi,null,["number","number","number","number","number","number"])}function Ame(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length;v.assert(r.dtype==="float32"||r.dtype==="int32",()=>`cumsum does not support ${r.dtype} tensors in the WASM backend`);let c=N.getAxesPermutation([a],l),u=r;c!==null&&(u=Cc({inputs:{x:r},attrs:{perm:c},backend:n}));let d=N.getInnerMostAxes(1,l)[0];N.assertAxesAreInnerMostDims("cumsum",[d],l);let p=n.makeOutput(u.shape,u.dtype),h=u.shape[d],f=n.dataIdMap.get(u.dataId).id,m=n.dataIdMap.get(p.dataId).id;C6(f,o?1:0,i?1:0,h,m,Ht[r.dtype]);let g=p;if(c!==null){let y=N.getUndoAxesPermutation(c);g=Cc({inputs:{x:p},attrs:{perm:y},backend:n}),n.disposeData(u.dataId),n.disposeData(p.dataId)}return g}var xme={kernelName:fi,backendName:"wasm",setupFunc:yme,kernelFunc:Ame},T6;function bme(e){T6=e.wasm.cwrap(gi,null,["number","number","number","array","number","array","array","number","number"])}function vme(e){let{backend:t,inputs:n,attrs:s}=e,{x:r}=n,{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=t.makeOutput(f,"float32"),y=t.dataIdMap.get(r.dataId).id,x=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),A=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),w=t.dataIdMap.get(m.dataId).id;return T6(y,a,o==="NHWC"?1:0,x,r.shape.length-1,A,b,f.length,w),m}var wme={kernelName:gi,backendName:"wasm",setupFunc:bme,kernelFunc:vme},N6;function kme(e){N6=e.wasm.cwrap(La,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Sme(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}=n,p=c==null?[1,1]:c,h=N.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,y=h.padInfo.right,x=h.padInfo.bottom,A=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,k=h.strideHeight,I=h.strideWidth,E=h.inChannels,R=h.outChannels,P=h.padInfo.type==="SAME"?1:0;if(h.dataFormat!=="channelsLast")throw new Error(`wasm backend DepthwiseConv2dNative does not support dataFormat:'${h.dataFormat}'. Please use 'channelsLast'.`);let D=s.makeOutput(h.outShape,"float32"),_=s.dataIdMap.get(D.dataId).id;return N6(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,y,x,A,P,b,w,k,I,E,R,_),D}var Ime={kernelName:La,backendName:"wasm",setupFunc:kme,kernelFunc:Sme},Cme=Nn(Wa),Tme=!1,Nme=Xn(yi,Tme,"bool"),Eme=Nn(Va,"float32");function Kx(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 Rme={kernelName:Ai,backendName:"wasm",kernelFunc:Kx};function E6(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 _me={kernelName:yu,backendName:"wasm",kernelFunc:E6},R6;function Dme(e){R6=e.wasm.cwrap(bi,null,["number","number","number","number","number","number"])}function $me(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,c,u]=s.shape;return R6(a,i,l,c,u,o),r}var Fme={kernelName:bi,backendName:"wasm",kernelFunc:$me,setupFunc:Dme},Pme=Nn(Ua),Ome=!1,Mme=Xn(Ga,Ome),_6;function zme(e){_6=e.wasm.cwrap(Ha,null,["number","number","number","number","number","number","number"])}function Lme(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 _6(u,d,p,h,f,r,g),m}var Bme={kernelName:Ha,backendName:"wasm",setupFunc:zme,kernelFunc:Lme},D6;function Wme(e){D6=e.wasm.cwrap(wo,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 Vme(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=$p[h];if(g==null)throw new Error(`${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 U=s.dataIdMap.get(o.dataId);if(U.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${U.shape.length}.`);if(U.shape[0]!==A)throw new Error(`FusedConv2D bias shape (${U.shape}) does not match the number of output channels (${A})`);b=U.id}let w=m.filterHeight,k=m.filterWidth,I=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,P=m.padInfo.left,D=m.dilationHeight,_=m.dilationWidth,T=m.strideHeight,O=m.strideWidth,W=m.inChannels,X=m.padInfo.type==="SAME"?1:0,z=m.batchSize,j=m.inHeight,Z=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let Q=s.makeOutput(m.outShape,"float32"),ne=s.dataIdMap.get(Q.dataId).id,ae=i==null?0:s.dataIdMap.get(i.dataId).id;return D6(y,z,j,Z,x,w,k,b,I,E,R,P,X,D,_,T,O,W,A,g,ae,f||0,ne),Q}var Ume={kernelName:wo,backendName:"wasm",setupFunc:Wme,kernelFunc:Vme},$6;function Gme(e){$6=e.wasm.cwrap(ko,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Hme(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,l,u,c,p,!0),g=$p[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 U=s.dataIdMap.get(o.dataId);if(U.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${U.shape.length}.`);if(U.shape[0]!==A)throw new Error(`FusedDepthwiseConv2D bias shape (${U.shape}) does not match the number of output channels (${A})`);b=U.id}let w=m.filterHeight,k=m.filterWidth,I=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,P=m.padInfo.left,D=m.dilationHeight,_=m.dilationWidth,T=m.strideHeight,O=m.strideWidth,W=m.inChannels,X=m.padInfo.type==="SAME"?1:0,z=m.batchSize,j=m.inHeight,Z=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. 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C0e={kernelName:Qa,backendName:"wasm",setupFunc:S0e,kernelFunc:I0e},T0e=!1,N0e=Xn(eo,T0e),Zx;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})(Zx||(Zx={}));var W6;function E0e(e){W6=e.wasm.cwrap(to,null,["number","array","number","number","array","array","number","number"])}function R0e(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 W6(o,c,t.shape.length,Ht[t.dtype],p,h,Zx[r],l),i}var _0e={kernelName:to,backendName:"wasm",kernelFunc:R0e,setupFunc:E0e},D0e=!0,$0e=Xn(no,D0e),F0e=Nn(Ni);function Yx(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 V6;function P0e(e){V6=e.wasm.cwrap(Ri,"number",["number","number","number","number","number"])}function O0e(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=V6(c,u,a,r,o),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Yx(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var M0e={kernelName:Ri,backendName:"wasm",setupFunc:P0e,kernelFunc:O0e},U6;function z0e(e){U6=e.wasm.cwrap(Su,"number",["number","number","number","number","number","bool"])}function 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Error(P);let D=f,_=g;return E!==u[0]&&(D=Fl({inputs:{x:f},attrs:{begin:0,size:[E,l]},backend:t}),_=Fl({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[D,_,x,b]}var Uge={kernelName:xd,backendName:"wasm",setupFunc:Wge,kernelFunc:Vge},r8;function Gge(e){r8=e.wasm.cwrap(_u,null,["number","number","number","number","number","number","number"])}function Hge(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
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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=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;r8(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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h=0;h<u.length;h++)d[a]=h,u[h]=Fl({inputs:{x:r},attrs:{begin:d,size:p},backend:n});return u.map(({dataId:h,dtype:f})=>({dataId:h,dtype:f,shape:l}))}var C2e={kernelName:Ki,backendName:"wasm",kernelFunc:I2e};function T2e(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(0),s}var N2e={kernelName:Zi,backendName:"wasm",kernelFunc:T2e},E2e=[vfe,kfe,Cfe,Ffe,Mfe,Bfe,Ufe,qfe,Qfe,eme,tme,rme,ame,lme,dme,pme,hme,gme,xme,wme,Ime,Cme,Nme,Eme,Rme,_me,Fme,Pme,Mme,bfe,Bme,Ume,jme,Kme,Jme,e0e,n0e,Tfe,a0e,i0e,u0e,c0e,p0e,m0e,y0e,b0e,k0e,C0e,N0e,_0e,$0e,F0e,M0e,B0e,U0e,H0e,X0e,Z0e,J0e,q6,nge,age,lge,cge,pge,hge,fge,Gfe,yge,bge,kge,Ige,Sge,Nge,_ge,Fge,Pge,Yfe,zge,Bge,Uge,jge,Xge,Zge,Jge,Qge,e2e,n2e,a2e,l2e,c2e,h2e,f2e,m2e,A2e,v2e,S2e,_fe,C2e,N2e];for(let e of E2e)dr(e);var Jx=Y();Jx.registerFlag("WASM_HAS_SIMD_SUPPORT",async()=>WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,10,9,1,7,0,65,0,253,15,26,11])));Jx.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Jx.get("IS_NODE"))return!1;try{return new MessageChannel().port1.postMessage(new SharedArrayBuffer(1)),WebAssembly.validate(new Uint8Array([0,97,115,109,1,0,0,0,1,4,1,96,0,0,3,2,1,0,5,4,1,3,1,1,10,11,1,9,0,65,0,254,16,2,0,26,11]))}catch(e){return!1}});var f8=li(o9()),R2e='var Module={};function threadPrintErr(){var text=Array.prototype.slice.call(arguments).join(" ");console.error(text)}function threadAlert(){var text=Array.prototype.slice.call(arguments).join(" ");postMessage({cmd:"alert",text:text,threadId:Module["_pthread_self"]()})}var err=threadPrintErr;this.alert=threadAlert;Module["instantiateWasm"]=function(info,receiveInstance){var instance=new WebAssembly.Instance(Module["wasmModule"],info);Module["wasmModule"]=null;receiveInstance(instance);return instance.exports};function 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()}}}}',_2e=li(i9()),m8=class extends nu{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(A8),eb=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new ad(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 F2e(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 ${t}`)}}};function D2e(e){return(t,n)=>(v.fetch(e,{credentials:"same-origin"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at '${e}'`),s.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(a=>{n(a.instance,a.module)})})}),{})}function g8(e,t,n){if(d0!=null)return d0;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 $2e(){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=R2e,u=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(u)}return i.endsWith(".wasm")?g8(e,t,Pp!=null?Pp:l):l+i},Qx&&(r.instantiateWasm=D2e(g8(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. For more details see https://github.com/tensorflow/tfjs/blob/master/tfjs-backend-wasm/README.md#using-bundlers"})};let o;t&&e&&d0==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+f8.default.toString()],{type:"text/javascript"}),o=(0,f8.default)(r)):o=(0,_2e.default)(r),o.then(i=>{a=!0,Mp=!1;let l=null;i.tfjs={init:i.cwrap("init",null,[]),initWithThreadsCount:i.cwrap("init_with_threads_count",null,["number"]),getThreadsCount:i.cwrap("get_threads_count","number",[]),registerTensor:i.cwrap("register_tensor",null,["number","number","number"]),disposeData:i.cwrap("dispose_data",l,["number"]),dispose:i.cwrap("dispose",l,[])},n({wasm:i})})})}function F2e(e,t){switch(t){case"float32":return new Float32Array(e);case"int32":return new Int32Array(e);case"bool":return new Uint8Array(e);default:throw new Error(`Unknown dtype ${t}`)}}var P2e=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],d0=null,Pp=null,Op={},Mp=!1,Qx=!1;function O2e(e,t=!1){if(a1("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Mp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");d0=e,Qx=t}function y8(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=P2e.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.`)}Qx=t}var A8=-1,eb=-1;function M2e(e){A8=e}function z2e(){if(eb===-1)throw new Error("WASM backend not initialized.");return eb}var L2e="0.0.0",B2e=2;ol("wasm",async()=>{let{wasm:e}=await $2e();return new m8(e)},B2e);var Xo="3.12.0-20211209",p0={tfjs:Xo,"tfjs-core":Xo,"tfjs-data":Xo,"tfjs-layers":Xo,"tfjs-converter":Xo,"tfjs-backend-cpu":Xo,"tfjs-backend-webgl":Xo,"tfjs-backend-wasm":Xo};var x8=`
|
|
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 b8=`
|
|
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];
|
|
}
|
|
`,v8=`
|
|
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;
|
|
}
|
|
`,w8=`
|
|
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);
|
|
}
|
|
`,k8=`
|
|
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;
|
|
}
|
|
`,S8=`
|
|
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 tb=(e,t,n)=>{let s=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(s,(r,a)=>(n[a]=0,r))},I8=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),tb(n,"attribute",this.attribute);for(let o in 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Bp=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],A0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2],gb=(e,t)=>e?[Math.trunc(Math.max(0,e.startPoint[0])),Math.trunc(Math.max(0,e.startPoint[1])),Math.trunc(Math.min(t.shape[2]||0,e.endPoint[0])-Math.max(0,e.startPoint[0])),Math.trunc(Math.min(t.shape[1]||0,e.endPoint[1])-Math.max(0,e.startPoint[1]))]:[0,0,0,0],yb=(e,t)=>e?[e.startPoint[0]/(t.shape[2]||0),e.startPoint[1]/(t.shape[1]||0),(e.endPoint[0]-e.startPoint[0])/(t.shape[2]||0),(e.endPoint[1]-e.startPoint[1])/(t.shape[1]||0)]:[0,0,0,0],U8=(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]];return{startPoint:n,endPoint:s,landmarks:e.landmarks,confidence:e.confidence}},Ab=(e,t,n)=>{let s=t.shape[1],r=t.shape[2],a=Se.cropAndResize(t,[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]],[0],n),o=de(a,Xe.tf255);return te(a),o},Wp=(e,t)=>{let n=A0(e),s=Bp(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}},Vp=e=>{let t=A0(e),n=Bp(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}},x0=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}},xb=[[1,0,0],[0,1,0],[0,0,1]],q2e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),X2e=(e,t)=>q2e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var G8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Ml=(e,t)=>{let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n},K2e=(e,t)=>{let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n},H8=(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(Ml(e[r],K2e(t,a)))}return n},j8=(e,t)=>{let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=G8(t[0],t[1]),o=H8(a,r),i=G8(-t[0],-t[1]);return H8(o,i)},Z2e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Ml(t[0],n),-Ml(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]},Y2e=(e,t)=>[Ml(e,t[0]),Ml(e,t[1])];function q8(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 X8(e,t,n,s,r){let a=Bp(t),o=e.map(p=>[a[0]/r*(p[0]-r/2),a[1]/r*(p[1]-r/2),p[2]||0]),i=n&&n!==0&&Math.abs(n)>.2,l=i?j8(n,[0,0]):xb,c=i?o.map(p=>[...Y2e(p,l),p[2]]):o,u=i?Z2e(s):xb,d=[...A0({startPoint:t.startPoint,endPoint:t.endPoint}),1];return c.map(p=>[Math.round(p[0]+Ml(d,u[0])),Math.round(p[1]+Ml(d,u[1])),Math.round(p[2]||0)])}function bb(e,t,n,s){let r=t.landmarks.length>=fb.count?fb.symmetryLine:zp.symmetryLine,a=0,o=xb,i;if(e&&pe.kernels.includes("rotatewithoffset"))if(a=X2e(t.landmarks[r[0]],t.landmarks[r[1]]),a&&a!==0&&Math.abs(a)>.2){let c=A0({startPoint:t.startPoint,endPoint:t.endPoint}),u=[c[0]/n.shape[2],c[1]/n.shape[1]],d=Se.rotateWithOffset(n,a,0,u);o=j8(-a,c),i=Ab(t,d,[s,s]),te(d)}else i=Ab(t,n,[s,s]);else i=Ab(t,n,[s,s]);return[a,o,i]}var K8=6,Gs,Z8=null,Ko=0,Up=null,b0=()=>Ko;async function Y8(e){var t,n;return pe.initial&&(Gs=null),Gs?e.debug&&J("cached model:",Gs.modelUrl):(Gs=await Be(We(e.modelBasePath,((t=e.face.detector)==null?void 0:t.modelPath)||"")),!Gs||!Gs.modelUrl?J("load model failed:",(n=e.face.detector)==null?void 0:n.modelPath):e.debug&&J("load model:",Gs.modelUrl)),Ko=Gs.inputs[0].shape?Gs.inputs[0].shape[2]:0,Up=Ce(Ko,"int32"),Z8=mr(q8(Ko)),Gs}function J2e(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=ue(t.boxStarts,Z8),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=de(t.boxSizes,Up),t.centersNormalized=de(t.centers,Up),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,Up),t.endNormalized=L(t.ends,Up);let n=Wu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(s=>te(t[s])),n}async function J8(e,t){var i,l,c,u;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return{boxes:[]};let n={};n.resized=Se.resizeBilinear(e,[Ko,Ko]),n.div=de(n.resized,Xe.tf127),n.normalized=he(n.div,Xe.tf05);let s=Gs==null?void 0:Gs.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 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vb=["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"],wb={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 Q8=224,Q2e,e1e=5,v0=[8,16,32,32,32];async function eT(){let e=[],t=0;for(;t<e1e;){let n=0,s=t;for(;s<v0.length&&v0[s]===v0[t];)n+=2,s++;let r=v0[t],a=Math.ceil(Q8/r),o=Math.ceil(Q8/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}Q2e={x:Ct(e.map(n=>n.x)),y:Ct(e.map(n=>n.y))}}function ca(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 tT(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 w0(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 Sb(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 nT={initial:!0},pn={detector:null,landmarks:null},Ec={detector:[224,224],landmarks:[256,256]},Ib=Number.MAX_SAFE_INTEGER,t1e={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},k0=null,Gp,Zo=[[0,0],[0,0],[0,0],[0,0]],sT=0,rT=e=>1-1/(1+Math.exp(e));async function aT(e){if(nT.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);Ec.detector[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ec.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 eT(),pn.detector}async function oT(e){if(nT.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);Ec.landmarks[0]=Array.isArray(t)?parseInt(t[0].tensorShape.dim[1].size):0,Ec.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 n1e(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let s;if(Gp&&(n.cropped=Se.cropAndResize(e,[Gp],[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];Zo=[[0,0],r,a,[0,0]],n.pad=er(n.cropped||e,Zo),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=>te(n[r])),s}function s1e(e,t){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+Zo[2][0]+Zo[2][1])/t[0]-Zo[2][0]),Math.trunc(n.position[1]*(t[1]+Zo[1][0]+Zo[1][1])/t[1]-Zo[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],n.position[2]];if(Gp)for(let n of e)n.positionRaw=[n.positionRaw[0]+Gp[1],n.positionRaw[1]+Gp[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 r1e(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,t1e.landmarks);let r=(await s.poseflag.data())[0],a=await s.ld.data();Object.keys(s).forEach(f=>te(s[f]));let o=[],i=5;for(let f=0;f<a.length/i;f++){let m=rT(a[i*f+3]),g=rT(a[i*f+4]),y=Math.trunc(100*m*g*r)/100,x=[a[i*f+0]/Ec.landmarks[0],a[i*f+1]/Ec.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:vb[f],positionRaw:x,position:A,score:y})}if(r<(t.body.minConfidence||0))return null;let l=s1e(o,n),c=l.map(f=>f.position),u=ca(c,[n[0],n[1]]),d={};for(let[f,m]of Object.entries(wb)){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 Cb(e,t){let n=[e.shape[2]||0,e.shape[1]||0],s=(t.body.skipTime||0)>ie()-sT,r=Ib<(t.body.skipFrames||0);if(t.skipAllowed&&s&&r&&k0!==null)Ib++;else{let a={};a.landmarks=await n1e(e,256),k0=await r1e(a.landmarks,t,n),Object.keys(a).forEach(o=>te(a[o])),sT=ie(),Ib=0}return k0?[k0]:[]}var Rc=[{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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glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var lr,zl=0,Tb=[],iT=0,Nb=Number.MAX_SAFE_INTEGER;async function lT(e){if(pe.initial&&(lr=null),lr)e.debug&&J("cached model:",lr.modelUrl);else{lr=await Be(We(e.modelBasePath,e.object.modelPath||""));let t=Object.values(lr.modelSignature.inputs);zl=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!lr||!lr.modelUrl?J("load model failed:",e.object.modelPath):e.debug&&J("load model:",lr.modelUrl)}return lr}async function a1e(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]),te([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=Rc[d].label,[h,f]=[a[0][c][0]/zl,a[0][c][1]/zl],m=[h,f,a[0][c][2]/zl-h,a[0][c][3]/zl-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=>te(s[c])),r}async function Eb(e,t){let n=(t.object.skipTime||0)>ie()-iT,s=Nb<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&Tb.length>0?(Nb++,Tb):(Nb=0,new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=Se.resizeBilinear(e,[zl,zl]),i=t.object.enabled?lr==null?void 0:lr.execute(o,["tower_0/detections"]):null;iT=ie(),te(o);let l=await a1e(i,a,t);Tb=l,r(l)}))}var Db={};ed(Db,{connected:()=>_b,kpt:()=>Rb});var Rb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],_b={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,uT=0,Zn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},$b=Number.MAX_SAFE_INTEGER;async function cT(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 o1e(e,t){let[n,s]=e.shape,r=H(e,[s*n]),a=An(r,0),o=(await a.data())[0];if(te([r,a]),o>t){let i=Fs(r,0),l=Gd(i,n),c=(await 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0:d.minConfidence)||0)&&Zn.keypoints.push({score:Math.round(100*y)/100,part:Rb[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=>te(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(_b)){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 i1e=["angry","disgust","fear","happy","sad","surprise","neutral"],Yn,S0=[],dT=0,pT=0,Pb=Number.MAX_SAFE_INTEGER;async function hT(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 Ob(e,t,n,s){var o,i;if(!Yn)return[];let r=Pb<(((o=t.face.emotion)==null?void 0:o.skipFrames)||0),a=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>ie()-pT;return t.skipAllowed&&a&&r&&dT===s&&S0[n]&&S0[n].length>0?(Pb++,S0[n]):(Pb=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 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0:o.skipFrames)||0),a=(((i=t.face.embedding)==null?void 0:i.skipTime)||0)>ie()-mT;return t.skipAllowed&&a&&r&&fT===s&&Mb[n]?(gT++,Mb[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)}Mb[n]=c,fT=s,mT=ie(),l(c)})}var ur,Yo=0,l1e=2.3,Lb=ir.leftEyeLower0,Bb=ir.rightEyeLower0,_c={leftBounds:[Lb[0],Lb[Lb.length-1]],rightBounds:[Bb[0],Bb[Bb.length-1]]},Dc={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function AT(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.iris)==null?void 0:t.modelPath)||"")),!ur||!ur.modelUrl?J("load model failed:",(n=e.face.iris)==null?void 0:n.modelPath):e.debug&&J("load 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0:l.skipFrames)||0);if(!t.skipAllowed||!n||!s||$c.length===0){let x=await J8(e,t);kT=ie(),$c=[];for(let A of x.boxes){let b=U8(A,x.scaleFactor),w=(b.endPoint[0]-b.startPoint[0])/(e.shape[2]||1e3),k=(((c=t.face.detector)==null?void 0:c.cropFactor)||1.6)/(w+.75)/1.34,I=Wp(b,k),E=Vp(I);$c.push(E)}Wb=0}else Wb++;let r=[],a=[],o=0;for(let x=0;x<$c.length;x++){let A=$c[x],b=0,w,k={id:o++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([b,w,k.tensor]=bb((u=t.face.detector)==null?void 0:u.rotation,A,e,((d=t.face.mesh)==null?void 0:d.enabled)?Ll:b0()),(p=t==null?void 0:t.filter)==null?void 0:p.equalization){let I=await h0(k.tensor);te(k.tensor),k.tensor=I}if(k.boxScore=Math.round(100*A.confidence)/100,(h=t.face.mesh)==null?void 0:h.enabled)if(!cr)t.debug&&J("face mesh detection requested, but model is not loaded");else{let[I,E,R]=cr.execute(k.tensor),P=await E.data();k.faceScore=Math.round(100*P[0])/100;let D=H(R,[-1,3]),_=await D.array();if(te([R,D,E,I]),k.faceScore<(((f=t.face.detector)==null?void 0:f.minConfidence)||1))A.confidence=k.faceScore;else{((m=t.face.iris)==null?void 0:m.enabled)&&(_=await wT(_,k.tensor,t,Ll)),k.mesh=X8(_,A,b,w,Ll),k.meshRaw=k.mesh.map(X=>[X[0]/(e.shape[2]||0),X[1]/(e.shape[1]||0),(X[2]||0)/Ll]);for(let X of Object.keys(ir))k.annotations[X]=ir[X].map(z=>k.mesh[z]);let T=x0(k.mesh),O=Wp(T,((g=t.face.detector)==null?void 0:g.cropFactor)||1.6);A={...Vp(O),confidence:A.confidence},k.box=gb(A,e),k.boxRaw=yb(A,e),k.score=k.faceScore,a.push(A),te(k.tensor),[b,w,k.tensor]=bb((y=t.face.detector)==null?void 0:y.rotation,A,e,Ll)}}else{k.box=gb(A,e),k.boxRaw=yb(A,e),k.score=k.boxScore,k.mesh=A.landmarks.map(I=>[(A.startPoint[0]+A.endPoint[0])/2+(A.endPoint[0]+A.startPoint[0])*I[0]/b0(),(A.startPoint[1]+A.endPoint[1])/2+(A.endPoint[1]+A.startPoint[1])*I[1]/b0()]),k.meshRaw=k.mesh.map(I=>[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/Ll]);for(let I of Object.keys(zp))k.annotations[I]=[k.mesh[zp[I]]]}r.push(k)}return $c=[...a],r}async function IT(e){var t,n;return pe.initial&&(cr=null),cr?e.debug&&J("cached model:",cr.modelUrl):(cr=await Be(We(e.modelBasePath,((t=e.face.mesh)==null?void 0:t.modelPath)||"")),!cr||!cr.modelUrl?J("load model failed:",(n=e.face.mesh)==null?void 0:n.modelPath):e.debug&&J("load model:",cr.modelUrl)),Ll=cr.inputs[0].shape?cr.inputs[0].shape[2]:0,cr}var CT=Ol,TT=Lp;var Cs,C0=[],NT=0,ET=0,Vb=Number.MAX_SAFE_INTEGER;async function RT(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 Ub(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 te(n),s}async function Gb(e,t,n,s){var o,i,l,c;if(!Cs)return{age:0,gender:"unknown",genderScore:0,descriptor:[]};let r=Vb<(((o=t.face.description)==null?void 0:o.skipFrames)||0),a=(((i=t.face.description)==null?void 0:i.skipTime)||0)>ie()-NT;return t.skipAllowed&&r&&a&&ET===s&&((l=C0[n])==null?void 0:l.age)&&((c=C0[n])==null?void 0:c.age)>0?(Vb++,C0[n]):(Vb=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=Ub(e),m=Cs==null?void 0:Cs.execute(f);NT=ie(),te(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];te(A);let k=await m.find(R=>R.shape[1]===100).data();d.age=Math.round(k[b-1]>k[b+1]?10*b-100*k[b-1]:10*b+100*k[b+1])/10;let I=m.find(R=>R.shape[1]===1024),E=I?await I.data():[];d.descriptor=Array.from(E),m.forEach(R=>te(R))}C0[n]=d,ET=s,u(d)}))}function T0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Hp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function _T(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 DT(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 N0(e,t=1.5){let n=Hp(e),s=T0(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 E0(e){let t=Hp(e),n=T0(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 c1e(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function $T(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return c1e(n)}var FT=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Jo(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function d1e(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function PT(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(Jo(e[r],d1e(t,a)))}return n}function Hb(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=FT(t[0],t[1]),o=PT(a,r),i=FT(-t[0],-t[1]);return PT(o,i)}function OT(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Jo(t[0],n),-Jo(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function jb(e,t){return[Jo(e,t[0]),Jo(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=>te(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=os(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=DT(h,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);o.push(f),Object.keys(l).forEach(m=>te(l[m]))}return Object.keys(s).forEach(i=>te(s[i])),o}};var p1e=5,zT=1.65,LT=[0,5,9,13,17,1,2],h1e=0,f1e=2,BT=0,Xb=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=>jb([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return N0(E0(r),p1e)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=N0(E0(n),zT);s.palmLandmarks=[];for(let r=0;r<LT.length;r++)s.palmLandmarks.push(t[LT[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=T0(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=Hb(s,[0,0]),c=i.map(h=>[...jb(h,l),h[2]]),u=OT(r),d=[...Hp(n),1],p=[Jo(d,u[0]),Jo(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()-BT,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?$T(c.palmLandmarks[h1e],c.palmLandmarks[f1e]):0,d=Hp(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=Hb(-u,d),m=s?this.getBoxForPalmLandmarks(c.palmLandmarks,f):c,g=_T(m,h,[this.inputSize,this.inputSize]),y=de(g,Xe.tf255);te(g),te(h);let[x,A]=this.handPoseModel.execute(y);BT=ie(),te(y);let b=(await x.data())[0];if(te(x),b>=n.hand.minConfidence/4){let w=H(A,[-1,3]),k=await w.array();te(A),te(w);let I=this.transformRawCoords(k,m,u,f),E=this.getBoxForHandLandmarks(I);this.storedBoxes[l]={...E,confidence:b};let R={landmarks:I,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:E.startPoint,bottomRight:E.endPoint}};i.push(R)}else this.storedBoxes[l]=null;te(A)}else{let u=N0(E0(c),zT),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]},Qo={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>Qo.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]},Bl=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:da,middle:pa,ring:Wl,pinky:Vl}=Jn,{none:Ir,half:m1e,full:Cr}=Qo,{verticalUp:Fc,verticalDown:uxe,horizontalLeft:Kb,horizontalRight:g1e,diagonalUpRight:y1e,diagonalUpLeft:Pc,diagonalDownRight:cxe,diagonalDownLeft:dxe}=Bt,ei=new Bl("thumbs up");ei.curl(Sr,Ir,1);ei.direction(Sr,Fc,1);ei.direction(Sr,Pc,.25);ei.direction(Sr,y1e,.25);for(let e of[Jn.index,Jn.middle,Jn.ring,Jn.pinky])ei.curl(e,Cr,1),ei.direction(e,Kb,1),ei.direction(e,g1e,1);var en=new Bl("victory");en.curl(Sr,m1e,.5);en.curl(Sr,Ir,.5);en.direction(Sr,Fc,1);en.direction(Sr,Pc,1);en.curl(da,Ir,1);en.direction(da,Fc,.75);en.direction(da,Pc,1);en.curl(pa,Ir,1);en.direction(pa,Fc,1);en.direction(pa,Pc,.75);en.curl(Wl,Cr,1);en.direction(Wl,Fc,.2);en.direction(Wl,Pc,1);en.direction(Wl,Kb,.2);en.curl(Vl,Cr,1);en.direction(Vl,Fc,.2);en.direction(Vl,Pc,1);en.direction(Vl,Kb,.2);en.weight(da,2);en.weight(pa,2);var ti=new Bl("point");ti.curl(Sr,Cr,1);ti.curl(da,Ir,.5);ti.curl(pa,Cr,.5);ti.curl(Wl,Cr,.5);ti.curl(Vl,Cr,.5);ti.weight(da,2);ti.weight(pa,2);var ni=new Bl("middle finger");ni.curl(Sr,Ir,1);ni.curl(da,Cr,.5);ni.curl(pa,Cr,.5);ni.curl(Wl,Cr,.5);ni.curl(Vl,Cr,.5);ni.weight(da,2);ni.weight(pa,2);var Oc=new Bl("open palm");Oc.curl(Sr,Ir,.75);Oc.curl(da,Ir,.75);Oc.curl(pa,Ir,.75);Oc.curl(Wl,Ir,.75);Oc.curl(Vl,Ir,.75);var WT=[ei,en,ti,ni,Oc];var A1e=.7,Ul={HALF_CURL_START_LIMIT:60,NO_CURL_START_LIMIT:130,DISTANCE_VOTE_POWER:1.1,SINGLE_ANGLE_VOTE_POWER:.9,TOTAL_ANGLE_VOTE_POWER:1.6};function VT(e,t,n,s){let r=(t-s)/(e-n),a=Math.atan(r)*180/Math.PI;return a<=0?a=-a:a>0&&(a=180-a),a}function UT(e,t){if(!e||!t)return[0,0];let n=VT(e[0],e[1],t[0],t[1]);if(e.length===2)return n;let s=VT(e[1],e[2],t[1],t[2]);return[n,s]}function GT(e,t=1){let n=0,s=0,r=0;return e>=75&&e<=105?n=1*t:e>=25&&e<=155?s=1*t:r=1*t,[n,s,r]}function x1e(e,t,n){let s=e[0]-t[0],r=e[0]-n[0],a=t[0]-n[0],o=e[1]-t[1],i=e[1]-n[1],l=t[1]-n[1],c=e[2]-t[2],u=e[2]-n[2],d=t[2]-n[2],p=Math.sqrt(s*s+o*o+c*c),h=Math.sqrt(r*r+i*i+u*u),f=Math.sqrt(a*a+l*l+d*d),m=(f*f+p*p-h*h)/(2*f*p);m>1?m=1:m<-1&&(m=-1);let g=Math.acos(m);g=57.2958*g%180;let y;return g>Ul.NO_CURL_START_LIMIT?y=Qo.none:g>Ul.HALF_CURL_START_LIMIT?y=Qo.half:y=Qo.full,y}function HT(e,t,n,s){let r;return s===Math.abs(e)?e>0?r=Bt.horizontalLeft:r=Bt.horizontalRight:s===Math.abs(t)?t>0?r=Bt.horizontalLeft:r=Bt.horizontalRight:n>0?r=Bt.horizontalLeft:r=Bt.horizontalRight,r}function jT(e,t,n,s){let r;return s===Math.abs(e)?e<0?r=Bt.verticalDown:r=Bt.verticalUp:s===Math.abs(t)?t<0?r=Bt.verticalDown:r=Bt.verticalUp:n<0?r=Bt.verticalDown:r=Bt.verticalUp,r}function b1e(e,t,n,s,r,a,o,i){let l,c=jT(e,t,n,s),u=HT(r,a,o,i);return c===Bt.verticalUp?u===Bt.horizontalLeft?l=Bt.diagonalUpLeft:l=Bt.diagonalUpRight:u===Bt.horizontalLeft?l=Bt.diagonalDownLeft:l=Bt.diagonalDownRight,l}function v1e(e,t,n,s){let r=e[0]-t[0],a=e[0]-n[0],o=t[0]-n[0],i=e[1]-t[1],l=e[1]-n[1],c=t[1]-n[1],u=Math.max(Math.abs(r),Math.abs(a),Math.abs(o)),d=Math.max(Math.abs(i),Math.abs(l),Math.abs(c)),p=0,h=0,f=0,m=d/(u+1e-5);m>1.5?p+=Ul.DISTANCE_VOTE_POWER:m>.66?h+=Ul.DISTANCE_VOTE_POWER:f+=Ul.DISTANCE_VOTE_POWER;let 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],k=n[0],I=n[1];A===g?(k=n[0],I=n[1]):A===x&&(b=t[0],w=t[1]);let P=UT([b,w],[k,I]),D=GT(P,Ul.TOTAL_ANGLE_VOTE_POWER);p+=D[0],h+=D[1],f+=D[2];for(let T of s){let O=GT(T,Ul.SINGLE_ANGLE_VOTE_POWER);p+=O[0],h+=O[1],f+=O[2]}let _;return p===Math.max(p,h,f)?_=jT(l,i,c,d):f===Math.max(h,f)?_=HT(a,r,o,u):_=b1e(l,i,c,d,a,r,o,u),_}function qT(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=UT(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=x1e(l,c,u),p=v1e(l,c,u,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function R0(e){if(!e||e.length===0)return null;let t=qT(e),n={};for(let s of Jn.all)n[Jn.getName(s)]={curl:Qo.getName(t.curls[s]),direction:Bt.getName(t.directions[s])};return n}function XT(e){let t=[];if(!e||e.length===0)return t;let n=qT(e);for(let s of WT){let r=s.matchAgainst(n.curls,n.directions);r>=A1e&&t.push({name:s.name,confidence:r})}return t}var KT={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]},ha,fa,ZT;async function Zb(e,t){let n=await ZT.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(KT))a[u]=KT[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=R0(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 Yb(e){var n,s,r,a,o,i;pe.initial&&(ha=null,fa=null),!ha||!fa?([ha,fa]=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 0:s.modelPath)||"").includes("tfhub.dev")}):null,e.hand.landmarks?Be(We(e.modelBasePath,((r=e.hand.skeleton)==null?void 0:r.modelPath)||""),{fromTFHub:(((a=e.hand.skeleton)==null?void 0:a.modelPath)||"").includes("tfhub.dev")}):null]),e.hand.enabled&&(!ha||!ha.modelUrl?J("load model failed:",((o=e.hand.detector)==null?void 0:o.modelPath)||""):e.debug&&J("load model:",ha.modelUrl),!fa||!fa.modelUrl?J("load model failed:",((i=e.hand.skeleton)==null?void 0:i.modelPath)||""):e.debug&&J("load model:",fa.modelUrl))):(e.debug&&J("cached model:",ha.modelUrl),e.debug&&J("cached model:",fa.modelUrl));let t=new qb(ha);return ZT=new Xb(t,fa),[ha,fa]}var Et=[null,null],w1e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],si=[[0,0],[0,0]],k1e=["hand","fist","pinch","point","face","tip","pinchtip"],YT=4,JT=1.6,S1e=512,I1e=1.4,_0=Number.MAX_SAFE_INTEGER,Jb=0,ma=[0,0],Xt={boxes:[],hands:[]},QT={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 eN(e){var t,n;if(pe.initial&&(Et[0]=null),Et[0])e.debug&&J("cached model:",Et[0].modelUrl);else{D0(["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);si[0][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,si[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 tN(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);si[1][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,si[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 C1e(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,S1e),o=Math.round(a*r/8)*8;s.resize=Se.resizeBilinear(e,[a,o]),s.cast=ge(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await Et[0].executeAsync(s.cast,w1e),s.boxes=rt(s.rawBoxes,[0,2]),s.scores=rt(s.rawScores,[0]);let i=is(s.scores,1);te(i[YT]),i.splice(YT,1),s.filtered=an(i,1),te(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();te(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=w0(m,I1e),y=Sb(g),x=[Math.trunc(m[0]*ma[0]),Math.trunc(m[1]*ma[1]),Math.trunc(m[2]*ma[0]),Math.trunc(m[3]*ma[1])],A=u[p],b=k1e[d[p]],w={id:l++,score:A,box:x,boxRaw:g,boxCrop:y,label:b};n.push(w)}return Object.keys(s).forEach(p=>te(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 Qb(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],[si[1][0],si[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]/si[1][1],u[1]/si[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=>[ma[0]*(u[0]+t.boxRaw[0]),ma[1]*(u[1]+t.boxRaw[1]),u[2]||0]),s.landmarks=R0(s.keypoints);for(let u of Object.keys(QT))s.annotations[u]=QT[u].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(i=>te(r[i]))}return s}async function e5(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[];ma=[e.shape[2]||0,e.shape[1]||0],_0++;let n=(t.hand.skipTime||0)>ie()-Jb,s=_0<(t.hand.skipFrames||0);return t.skipAllowed&&n&&s?Xt.hands:new Promise(async o=>{let i=3*(t.hand.skipTime||0)>ie()-Jb,l=_0<3*(t.hand.skipFrames||0);t.skipAllowed&&Xt.hands.length===t.hand.maxDetected?Xt.hands=await Promise.all(Xt.boxes.map(u=>Qb(e,u,t))):t.skipAllowed&&i&&l&&Xt.hands.length>0?Xt.hands=await Promise.all(Xt.boxes.map(u=>Qb(e,u,t))):(Xt.boxes=await C1e(e,t),Jb=ie(),Xt.hands=await Promise.all(Xt.boxes.map(u=>Qb(e,u,t))),_0=0);let c=[...Xt.boxes];if(Xt.boxes.length=0,t.cacheSensitivity>0)for(let u=0;u<Xt.hands.length;u++){let d=tT(Xt.hands[u].keypoints,ma);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=w0(d.box,JT),h=w0(d.boxRaw,JT),f=Sb(h);Xt.boxes.push({...c[u],box:p,boxRaw:h,boxCrop:f})}}for(let u=0;u<Xt.hands.length;u++){let d=ca(Xt.hands[u].keypoints,ma);Xt.hands[u].box=d.box,Xt.hands[u].boxRaw=d.boxRaw}o(Xt.hands)})}var fn,$0=[],t5=Number.MAX_SAFE_INTEGER,nN=0,sN=0;async function rN(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 n5(e,t,n,s){var o,i;if(!fn)return 0;let r=(((o=t.face.liveness)==null?void 0:o.skipTime)||0)>ie()-sN,a=t5<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&r&&a&&nN===s&&$0[n]?(t5++,$0[n]):(t5=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];$0[n]=Math.round(100*d)/100,nN=s,sN=ie(),te([c,u]),l($0[n])}))}var o5={};ed(o5,{connected:()=>P0,horizontal:()=>s5,kpt:()=>F0,relative:()=>a5,vertical:()=>r5});var F0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],s5=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],r5=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],a5=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],P0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var aN=.005,Ts={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function i5(e){for(let t of s5){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 r5){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 a5){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 i=e.keypoints[s]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[s].position[0]),Math.abs(e.keypoints[o].position[0]-e.keypoints[s].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[o].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[r].position[0])]:[0,0];if(i[0]>i[1]||l[0]>l[1]){let c=e.keypoints[s];e.keypoints[s]=e.keypoints[r],e.keypoints[r]=c}}}function oN(e){for(let t=0;t<e.length;t++)if(e[t]&&Ts.keypoints[t]){let n=[Math.abs(e[t].positionRaw[0]-Ts.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-Ts.keypoints[t].positionRaw[1])];n[0]<aN&&n[1]<aN?e[t]=Ts.keypoints[t]:Ts.keypoints[t]=e[t]}else Ts.keypoints[t]=e[t];return e}function iN(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;Ts.padding=[[0,0],[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],[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],[0,0]],n.pad=er(e,Ts.padding),n.resize=Se.resizeBilinear(n.pad,[t,t]);let s=ge(n.resize,"int32");return Object.keys(n).forEach(r=>te(n[r])),s}function lN(e,t){e.keypoints=e.keypoints.filter(s=>s&&s.position);for(let s of e.keypoints)s.position=[s.position[0]*(t[0]+Ts.padding[2][0]+Ts.padding[2][1])/t[0]-Ts.padding[2][0],s.position[1]*(t[1]+Ts.padding[1][0]+Ts.padding[1][1])/t[1]-Ts.padding[1][0]],s.positionRaw=[s.position[0]/t[0],s.position[1]/t[1]];let n=ca(e.keypoints.map(s=>s.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var Fn,uN=0,l5=Number.MAX_SAFE_INTEGER,Gl={boxes:[],bodies:[],last:0};async function cN(e){return pe.initial&&(Fn=null),Fn?e.debug&&J("cached model:",Fn.modelUrl):(D0(["size"],e),Fn=await Be(We(e.modelBasePath,e.body.modelPath||"")),!Fn||!Fn.modelUrl?J("load model failed:",e.body.modelPath):e.debug&&J("load model:",Fn.modelUrl)),uN=Fn.inputs[0].shape?Fn.inputs[0].shape[2]:0,Fn}async function T1e(e,t,n){let s=e[0][0],r=[],a=0;for(let u=0;u<s.length;u++)if(a=s[u][2],a>t.body.minConfidence){let d=[s[u][1],s[u][0]];r.push({score:Math.round(100*a)/100,part:F0[u],positionRaw:d,position:[Math.round((n.shape[2]||0)*d[0]),Math.round((n.shape[1]||0)*d[1])]})}a=r.reduce((u,d)=>d.score>u?d.score:u,0);let o=[],i=ca(r.map(u=>u.position),[n.shape[2],n.shape[1]]),l={};for(let[u,d]of Object.entries(P0)){let p=[];for(let h=0;h<d.length-1;h++){let f=r.find(g=>g.part===d[h]),m=r.find(g=>g.part===d[h+1]);f&&m&&f.score>(t.body.minConfidence||0)&&m.score>(t.body.minConfidence||0)&&p.push([f.position,m.position])}l[u]=p}let c={id:0,score:a,box:i.box,boxRaw:i.boxRaw,keypoints:r,annotations:l};return i5(c),o.push(c),o}async function N1e(e,t,n){let s=[];for(let r=0;r<e[0].length;r++){let 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:F0[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=ca(i.map(d=>d.position),[n.shape[2],n.shape[1]]),c={};for(let[d,p]of Object.entries(P0)){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};i5(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 u5(e,t){if(!Fn||!(Fn==null?void 0:Fn.inputs[0].shape))return[];t.skipAllowed||(Gl.boxes.length=0),l5++;let n=(t.body.skipTime||0)>ie()-Gl.last,s=l5<(t.body.skipFrames||0);return t.skipAllowed&&n&&s?Gl.bodies:new Promise(async r=>{let a={};l5=0,a.input=iN(e,uN),a.res=Fn==null?void 0:Fn.execute(a.input),Gl.last=ie();let o=await a.res.array();Gl.bodies=a.res.shape[2]===17?await T1e(o,t,e):await N1e(o,t,e);for(let i of Gl.bodies)lN(i,[e.shape[2]||1,e.shape[1]||1]),oN(i.keypoints);Object.keys(a).forEach(i=>te(a[i])),r(Gl.bodies)})}var Hs,O0=[],dN=0,c5=Number.MAX_SAFE_INTEGER,M0=2.5;async function pN(e){if(!Hs||pe.initial){Hs=await Be(We(e.modelBasePath,e.object.modelPath||""));let t=Object.values(Hs.modelSignature.inputs);Hs.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Hs||!Hs.modelUrl?J("load model failed:",e.object.modelPath):e.debug&&J("load model:",Hs.modelUrl)}else e.debug&&J("cached model:",Hs.modelUrl);return Hs}async function E1e(e,t,n,s){let r=0,a=[];for(let c of[1,2,4])K(async()=>{var g,y;let u=c*13,d=(g=e.find(x=>x.shape[1]===u**2&&x.shape[2]===Rc.length))==null?void 0:g.squeeze(),p=(y=e.find(x=>x.shape[1]===u**2&&x.shape[2]<Rc.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,k=(.5+Math.trunc(x/u))/u,I=f[x].map(W=>W*(u/c/t)),[E,R]=[w-M0/c*I[0],k-M0/c*I[1]],[P,D]=[w+M0/c*I[2]-E,k+M0/c*I[3]-R],_=[E,R,P,D];_=_.map(W=>Math.max(0,Math.min(W,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:Rc[A].label,box:T.map(W=>Math.trunc(W)),boxRaw:_};a.push(O)}}});e.forEach(c=>te(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(),te(c)}return a=a.filter((c,u)=>l.includes(u)).sort((c,u)=>u.score-c.score),a}async function d5(e,t){let n=(t.object.skipTime||0)>ie()-dN,s=c5<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&O0.length>0?(c5++,O0):(c5=0,!pe.kernels.includes("mod")||!pe.kernels.includes("sparsetodense")?O0:new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=Se.resizeBilinear(e,[Hs.inputSize,Hs.inputSize],!1),i=de(o,Xe.tf255),l=i.transpose([0,3,1,2]);te(i),te(o);let c;t.object.enabled&&(c=Hs.execute(l)),dN=ie(),te(l);let u=await E1e(c,Hs.inputSize,a,t);O0=u,r(u)}))}var jp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],R1e=jp.length,qp=jp.reduce((e,t,n)=>(e[t]=n,e),{}),_1e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],Qxe=_1e.map(([e,t])=>[qp[e],qp[t]]),hN=[["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 fN(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 mN(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]}))});return e.map((c,u)=>i(c,u))}var p5=class{constructor(t,n){fe(this,"priorityQueue");fe(this,"numberOfElements");fe(this,"getElementValue");this.priorityQueue=new 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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 IN(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 S5(e,t,n){let s=Rn(ga,n);if(!(!t||!e)&&s.drawGestures){let r=Hl(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 I5(e,t,n){var a,o,i,l,c;let s=Rn(ga,n);if(!t||!e)return;let r=Hl(e);if(!!r)for(let u of t){if(r.font=s.font,r.strokeStyle=s.color,r.fillStyle=s.color,s.drawBoxes&&Xp(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: ${zc(u.rotation.angle.roll)}\xB0 yaw:${zc(u.rotation.angle.yaw)}\xB0 pitch:${zc(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&d.push(`gaze: ${zc(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=1,u.mesh&&u.mesh.length>0){if(s.drawPoints)for(let d of u.mesh)k5(r,d[0],d[1],d[2],s);if(s.drawPolygons){if(r.lineWidth=1,u.mesh.length>450)for(let d=0;d<Ol.length/3;d++){let p=[Ol[d*3+0],Ol[d*3+1],Ol[d*3+2]].map(h=>u.mesh[h]);SN(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]*zc(u.rotation.angle.yaw)/90,p=u.box[1]+u.box[3]/2+u.box[2]*zc(u.rotation.angle.pitch)/90,h=new Path2D(`
|
|
M ${u.box[0]+u.box[2]/2} ${u.box[1]}
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|
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]];IN(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]];IN(r,[u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]],[p[0],p[1]],4)}}}}}async function C5(e,t,n){var a;let s=Rn(ga,n);if(!t||!e)return;let r=Hl(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&&(Xp(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)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:s.color,k5(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s));if(s.drawLabels&&t[o].keypoints){r.font=s.font;for(let i of t[o].keypoints)!i.score||i.score===0||(r.fillStyle=s.useDepth&&i.position[2]?`rgba(${127.5+2*i.position[2]}, ${127.5-2*i.position[2]}, 255, 0.5)`:s.color,r.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4))}if(s.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let i of Object.values(t[o].annotations))for(let l of i)W1e(r,l,s)}}}async function T5(e,t,n){let s=Rn(ga,n);if(!t||!e)return;let r=Hl(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,Xp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`hand:${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)}, ${127.5-2*(o[2]||0)}, 255, 0.5)`:s.color,k5(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{!i||i.length===0||!i[0]||(r.fillStyle=s.useDepth?`rgba(${127.5+2*i[i.length-1][2]}, ${127.5-2*i[i.length-1][2]}, 255, 0.5)`:s.color,r.fillText(l,i[i.length-1][0]+4,i[i.length-1][1]+4))};r.font=s.font,o(a.annotations.index,"index"),o(a.annotations.middle,"middle"),o(a.annotations.ring,"ring"),o(a.annotations.pinky,"pinky"),o(a.annotations.thumb,"thumb"),o(a.annotations.palm,"palm")}if(s.drawPolygons&&a.annotations){let o=i=>{if(!(!i||i.length===0||!i[0]))for(let l=0;l<i.length;l++)r.beginPath(),r.strokeStyle=s.useDepth?`rgba(${127.5+l*i[l][2]}, ${127.5-l*i[l][2]}, 255, 0.5)`:s.color,r.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()};r.lineWidth=s.lineWidth,o(a.annotations.index),o(a.annotations.middle),o(a.annotations.ring),o(a.annotations.pinky),o(a.annotations.thumb)}}}}async function N5(e,t,n){let s=Rn(ga,n);if(!t||!e)return;let r=Hl(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,Xp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${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 CN(e,t,n){let s=Rn(ga,n);if(!t||!e)return;let r=Hl(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,Xp(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person 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ir.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]});Lc&&Lc>0&&(r=r.map(o=>({x:o.x>.5?o.x+Lc:o.x-Lc,y:o.y>.5?o.y+Lc:o.y-Lc})));for(let o=0;o<t;o++)for(let i=0;i<n;i++)V1e(o/t,i/t,r)||(s.set(E5*s.get(0,i,o,0),0,i,o,0),s.set(E5*s.get(0,i,o,1),0,i,o,1),s.set(E5*s.get(0,i,o,2),0,i,o,2));let a=s.toTensor();return te(s),a}var U1e=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 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l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,c=[i[10],i[152],i[234],i[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),u=n(s(c[1],c[0])),d=n(s(c[3],c[2])),p=n(r(d,u));d=r(u,p);let h=[d[0],d[1],d[2],u[0],u[1],u[2],p[0],p[1],p[2]],f=a(h),m=i.length===478?U1e(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}};var R5=async(e,t)=>{var h,f,m,g,y,x,A,b,w,k,I,E,R,P,D,_,T,O,W,X,z,j,Z,Q,ne,ae;let n,s,r,a,o,i,l,c,u,d=[];e.state="run:face",n=ie();let p=await ST(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 U=0;U<p.length;U++){if(e.analyze("Get Face"),!p[U].tensor||p[U].tensor.isDisposedInternal){J("Face object is disposed:",p[U].tensor);continue}if((h=e.config.face.detector)==null?void 0:h.mask){let Te=await EN(p[U]);te(p[U].tensor),p[U].tensor=Te}let oe=p[U].mesh&&p[U].mesh.length>200?RN(p[U],[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)?Ob(p[U].tensor||pt([]),e.config,U,p.length):null:(e.state="run:emotion",n=ie(),o=((m=e.config.face.emotion)==null?void 0:m.enabled)?await Ob(p[U].tensor||pt([]),e.config,U,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)?hb(p[U].tensor||pt([]),e.config,U,p.length):null:(e.state="run:antispoof",n=ie(),l=((y=e.config.face.antispoof)==null?void 0:y.enabled)?await hb(p[U].tensor||pt([]),e.config,U,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)?n5(p[U].tensor||pt([]),e.config,U,p.length):null:(e.state="run:liveness",n=ie(),c=((A=e.config.face.liveness)==null?void 0:A.enabled)?await n5(p[U].tensor||pt([]),e.config,U,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)?ob(p[U].tensor||pt([]),e.config,U,p.length):{}:(e.state="run:gear",n=ie(),r=((w=e.config.face.gear)==null?void 0:w.enabled)?await ob(p[U].tensor||pt([]),e.config,U,p.length):{},e.performance.gear=Math.trunc(ie()-n)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(s=((k=e.config.face.ssrnet)==null?void 0:k.enabled)?lb(p[U].tensor||pt([]),e.config,U,p.length):{},a=((I=e.config.face.ssrnet)==null?void 0:I.enabled)?db(p[U].tensor||pt([]),e.config,U,p.length):{}):(e.state="run:ssrnet",n=ie(),s=((E=e.config.face.ssrnet)==null?void 0:E.enabled)?await lb(p[U].tensor||pt([]),e.config,U,p.length):{},a=((R=e.config.face.ssrnet)==null?void 0:R.enabled)?await db(p[U].tensor||pt([]),e.config,U,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)?zb(p[U].tensor||pt([]),e.config,U,p.length):{}:(e.state="run:mobilefacenet",n=ie(),i=((D=e.config.face.mobilefacenet)==null?void 0:D.enabled)?await zb(p[U].tensor||pt([]),e.config,U,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)?Gb(p[U].tensor||pt([]),e.config,U,p.length):null:(e.state="run:description",n=ie(),u=((T=e.config.face.description)==null?void 0:T.enabled)?await Gb(p[U].tensor||pt([]),e.config,U,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}),((W=e.config.face.gear)==null?void 0:W.enabled)&&r&&(u={age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&i&&(u.descriptor=i),!((z=e.config.face.iris)==null?void 0:z.enabled)&&((Z=(j=p[U])==null?void 0:j.annotations)==null?void 0:Z.leftEyeIris)&&((ne=(Q=p[U])==null?void 0:Q.annotations)==null?void 0:ne.rightEyeIris)&&(delete p[U].annotations.leftEyeIris,delete p[U].annotations.rightEyeIris);let re=p[U].annotations&&p[U].annotations.leftEyeIris&&p[U].annotations.leftEyeIris[0]&&p[U].annotations.rightEyeIris&&p[U].annotations.rightEyeIris[0]&&p[U].annotations.leftEyeIris.length>0&&p[U].annotations.rightEyeIris.length>0&&p[U].annotations.leftEyeIris[0]!==null&&p[U].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[U].annotations.leftEyeIris[3][0]-p[U].annotations.leftEyeIris[1][0]),Math.abs(p[U].annotations.rightEyeIris[4][1]-p[U].annotations.rightEyeIris[2][1]))/t.shape[2]:0,me=((ae=e.config.face.detector)==null?void 0:ae.return)?rt(p[U].tensor):null;te(p[U].tensor),p[U].tensor&&delete p[U].tensor;let Ae={...p[U],id:U};(u==null?void 0:u.age)&&(Ae.age=u.age),(u==null?void 0:u.gender)&&(Ae.gender=u.gender),(u==null?void 0:u.genderScore)&&(Ae.genderScore=u==null?void 0:u.genderScore),(u==null?void 0:u.descriptor)&&(Ae.embedding=u==null?void 0:u.descriptor),(u==null?void 0:u.race)&&(Ae.race=u==null?void 0:u.race),o&&(Ae.emotion=o),l&&(Ae.real=l),c&&(Ae.live=c),re&&re!==0&&(Ae.iris=Math.trunc(500/re/11.7)/100),oe&&(Ae.rotation=oe),me&&(Ae.tensor=me),d.push(Ae),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 _N=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},DN=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 ${l<0?"up":"down"}`})}return t},$N=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},FN=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=XT(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},_5=0;function PN(e,t){var o,i,l,c,u,d,p,h,f,m,g,y,x,A,b,w,k,I,E,R,P,D,_,T,O,W,X;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((U,oe)=>((r-1)*De.body[z].box[oe]+U)/r),Z=e.body[z].boxRaw.map((U,oe)=>((r-1)*De.body[z].boxRaw[oe]+U)/r),Q=e.body[z].keypoints.map((U,oe)=>({score:U.score,part:U.part,position:[De.body[z].keypoints[oe]?((r-1)*(De.body[z].keypoints[oe].position[0]||0)+(U.position[0]||0))/r:U.position[0],De.body[z].keypoints[oe]?((r-1)*(De.body[z].keypoints[oe].position[1]||0)+(U.position[1]||0))/r:U.position[1],De.body[z].keypoints[oe]?((r-1)*(De.body[z].keypoints[oe].position[2]||0)+(U.position[2]||0))/r:U.position[2]],positionRaw:[De.body[z].keypoints[oe]?((r-1)*(De.body[z].keypoints[oe].positionRaw[0]||0)+(U.positionRaw[0]||0))/r:U.position[0],De.body[z].keypoints[oe]?((r-1)*(De.body[z].keypoints[oe].positionRaw[1]||0)+(U.positionRaw[1]||0))/r:U.position[1],De.body[z].keypoints[oe]?((r-1)*(De.body[z].keypoints[oe].positionRaw[2]||0)+(U.positionRaw[2]||0))/r:U.position[2]]})),ne={},ae={connected:{}};((i=(o=t.body)==null?void 0:o.modelPath)==null?void 0:i.includes("efficientpose"))?ae=Db:((c=(l=t.body)==null?void 0:l.modelPath)==null?void 0:c.includes("blazepose"))?ae=kb:((d=(u=t.body)==null?void 0:u.modelPath)==null?void 0:d.includes("movenet"))&&(ae=o5);for(let[U,oe]of Object.entries(ae.connected)){let re=[];for(let me=0;me<oe.length-1;me++){let Ae=Q.find(Ne=>Ne.part===oe[me]),Te=Q.find(Ne=>Ne.part===oe[me+1]);Ae&&Te&&re.push([Ae.position,Te.position])}ne[U]=re}De.body[z]={...e.body[z],box:j,boxRaw:Z,keypoints:Q,annotations:ne}}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,U)=>((r-1)*De.hand[z].box[U]+ae)/r),Z=e.hand[z].boxRaw.map((ae,U)=>((r-1)*De.hand[z].boxRaw[U]+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,U)=>ae.map((oe,re)=>((r-1)*(De.hand[z].keypoints[U][re]||1)+(oe||0))/r)):[],ne={};if(Object.keys(De.hand[z].annotations).length!==Object.keys(e.hand[z].annotations).length)De.hand[z].annotations=e.hand[z].annotations,ne=De.hand[z].annotations;else if(e.hand[z].annotations)for(let ae of Object.keys(e.hand[z].annotations))ne[ae]=e.hand[z].annotations[ae]&&e.hand[z].annotations[ae][0]?e.hand[z].annotations[ae].map((U,oe)=>U.map((re,me)=>((r-1)*De.hand[z].annotations[ae][oe][me]+re)/r)):null;De.hand[z]={...e.hand[z],box:j,boxRaw:Z,keypoints:Q,annotations:ne}}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,ne)=>((r-1)*De.face[z].box[ne]+Q)/r),Z=e.face[z].boxRaw.map((Q,ne)=>((r-1)*De.face[z].boxRaw[ne]+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)*(((k=(w=De.face[z].rotation)==null?void 0:w.angle)==null?void 0:k.pitch)||0)+(((E=(I=e.face[z].rotation)==null?void 0:I.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)+(((_=(D=e.face[z].rotation)==null?void 0:D.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)+(((X=(W=e.face[z].rotation)==null?void 0:W.gaze)==null?void 0:X.strength)||0))/r},De.face[z]={...e.face[z],rotation:Q,box:j,boxRaw:Z}}De.face[z]={...e.face[z],box:j,boxRaw:Z}}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,ne)=>((r-1)*De.object[z].box[ne]+Q)/r),Z=e.object[z].boxRaw.map((Q,ne)=>((r-1)*De.object[z].boxRaw[ne]+Q)/r);De.object[z]={...e.object[z],box:j,boxRaw:Z}}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((Z,Q)=>((r-1)*De.persons[j].box[Q]+Z)/r)}e.gesture&&(De.gesture=e.gesture);let a=ie();return _5=pe.perfadd?_5+Math.round(a-n):Math.round(a-n),e.performance&&(De.performance={...e.performance,interpolate:_5}),De}function B0(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 ON=(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 MN(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let s=B0(e,t,n);return ON(s,n.order||2,n.min||0,n.max||1)}function zN(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=B0(e,t[o],n);if(i<s&&(s=i,r=o),s<(n.threshold||0))break}let a=ON(s,n.order||2,n.min||0,n.max||1);return{index:r,distance:s,similarity:a}}function LN(e,t,n,s,r){var i,l,c,u,d,p,h,f,m,g,y,x,A,b,w,k;let a=0,o=[];for(let I of e){let E={id:a++,face:I,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let O of t)I.box[0]>O.box[0]&&I.box[0]<O.box[0]+O.box[2]&&I.box[1]+I.box[3]>O.box[1]&&I.box[1]+I.box[3]<O.box[1]+O.box[3]&&(E.body=O);if(E.body)for(let O of n)O.box[0]+O.box[2]>E.body.box[0]&&O.box[0]+O.box[2]<E.body.box[0]+E.body.box[2]&&O.box[1]+O.box[3]>E.body.box[1]&&O.box[1]+O.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.left=O),O.box[0]<E.body.box[0]+E.body.box[2]&&O.box[0]>E.body.box[0]&&O.box[1]+O.box[3]>E.body.box[1]&&O.box[1]+O.box[3]<E.body.box[1]+E.body.box[3]&&E.hands&&(E.hands.right=O);for(let O of s)O.face!==void 0&&O.face===I.id?(i=E.gestures)==null||i.push(O):O.iris!==void 0&&O.iris===I.id?(l=E.gestures)==null||l.push(O):O.body!==void 0&&O.body===((c=E.body)==null?void 0:c.id)?(u=E.gestures)==null||u.push(O):O.hand!==void 0&&O.hand===((p=(d=E.hands)==null?void 0:d.left)==null?void 0:p.id)?(h=E.gestures)==null||h.push(O):O.hand!==void 0&&O.hand===((m=(f=E.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=E.gestures)==null||g.push(O));let R=[],P=[],D=O=>{O&&O.length===4&&(R.push(O[0],O[0]+O[2]),P.push(O[1],O[1]+O[3]))};D((y=E.face)==null?void 0:y.box),D((x=E.body)==null?void 0:x.box),D((b=(A=E.hands)==null?void 0:A.left)==null?void 0:b.box),D((k=(w=E.hands)==null?void 0:w.right)==null?void 0:k.box);let _=Math.min(...R),T=Math.min(...P);E.box=[_,T,Math.max(...R)-_,Math.max(...P)-T],r&&r[1]&&r[2]&&(E.boxRaw=[E.box[0]/r[2],E.box[1]/r[1],E.box[2]/r[2],E.box[3]/r[1]]),o.push(E)}return o}var W0=`
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2Q==`;async function G1e(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(W0);break;case"body":case"full":n=await t(V0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function H1e(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+W0;break;case"full":case"body":n="data:image/jpeg;base64,"+V0;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({});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(null)})}async function j1e(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(W0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(V0)),!n)return null;let s;if(typeof void 0!="undefined"){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 BN(e,t){let n=ie();if(e.state="warmup",t&&(e.config=Rn(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 G1e(e):typeof Image!="undefined"||pe.Canvas!==void 0?s=await H1e(e):s=await j1e(e);let a=ie();e.config.debug&&J("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var Bc,Kp,Zp,U0,VN=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");nd(this,Bc,void 0);nd(this,Kp,void 0);nd(this,Zp,void 0);fe(this,"gl");fe(this,"analyze",(...t)=>{if(!td(this,Kp))return;let n=this.tf.engine().state.numTensors,s=td(this,Bc);sd(this,Bc,n);let r=n-s;r!==0&&J(...t,r)});nd(this,U0,t=>{if(!td(this,Zp))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",MN);fe(this,"distance",B0);fe(this,"match",zN);fe(this,"emit",t=>{var n;this.events&&this.events.dispatchEvent&&((n=this.events)==null||n.dispatchEvent(new Event(t)))});this.env=pe,Ia.wasmPath=p0["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${iv}/dist/`,Ia.modelBasePath=pe.browser?"../models/":"file://models/",Ia.backend=pe.browser?"humangl":"tensorflow",this.version=sb,Object.defineProperty(this,"version",{value:sb}),this.config=JSON.parse(JSON.stringify(Ia)),Object.seal(this.config),t&&(this.config=Rn(this.config,t)),this.tf=Pl,this.state="idle",sd(this,Bc,0),sd(this,Kp,!1),sd(this,Zp,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new b5,this.draw={options:ga,canvas:(n,s)=>TN(n,s),face:(n,s,r)=>I5(n,s,r),body:(n,s,r)=>C5(n,s,r),hand:(n,s,r)=>T5(n,s,r),gesture:(n,s,r)=>S5(n,s,r),object:(n,s,r)=>N5(n,s,r),person:(n,s,r)=>CN(n,s,r),all:(n,s,r)=>NN(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=CT,this.faceUVMap=TT,this.gl=Rt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ia)),this.config.backend=t}validate(t){return y2(Ia,t||this.config)}now(){return ie()}image(t,n=!0){return Nc(t,this.config,n)}async segmentation(t,n){return bN(t,n,this.config)}enhance(t){return Ub(t)}compare(t,n){return N8(this.config,t,n)}async init(){await L0(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=Rn(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 L0(this)||J("error: backend check failed"),await ff(),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 vN(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 wN(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 PN(t,this.config)}async warmup(t){let n=ie(),s=await BN(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,k,I,E,R,P,D,_,T,O,W,X,z,j,Z,Q,ne;this.state="config";let r;this.config=Rn(this.config,n),this.state="check";let a=td(this,U0).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 L0(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 T8(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?R5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=ie(),l=this.config.face.enabled?await R5(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?Rn(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?y5(i.tensor,p):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("blazepose"))?c=this.config.body.enabled?Cb(i.tensor,p):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?c=this.config.body.enabled?Fb(i.tensor,p):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("movenet"))&&(c=this.config.body.enabled?u5(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 y5(i.tensor,p):[]:((w=this.config.body.modelPath)==null?void 0:w.includes("blazepose"))?c=this.config.body.enabled?await Cb(i.tensor,p):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("efficientpose"))?c=this.config.body.enabled?await Fb(i.tensor,p):[]:((I=this.config.body.modelPath)==null?void 0:I.includes("movenet"))&&(c=this.config.body.enabled?await u5(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?Rn(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?Zb(i.tensor,h):[]:((D=(P=this.config.hand.detector)==null?void 0:P.modelPath)==null?void 0:D.includes("handtrack"))&&(u=this.config.hand.enabled?e5(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 Zb(i.tensor,h):[]:((W=(O=this.config.hand.detector)==null?void 0:O.modelPath)==null?void 0:W.includes("handtrack"))&&(u=this.config.hand.enabled?await e5(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?(((X=this.config.object.modelPath)==null?void 0:X.includes("nanodet"))?d=this.config.object.enabled?d5(i.tensor,this.config):[]:((z=this.config.object.modelPath)==null?void 0:z.includes("centernet"))&&(d=this.config.object.enabled?Eb(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 d5(i.tensor,this.config):[]:((Z=this.config.object.modelPath)==null?void 0:Z.includes("centernet"))&&(d=this.config.object.enabled?await Eb(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=[...DN(l),..._N(c),...FN(u),...$N(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=((ne=(Q=this.process)==null?void 0:Q.tensor)==null?void 0:ne.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 LN(l,c,u,f,m)}},te(i.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};Bc=new WeakMap,Kp=new WeakMap,Zp=new WeakMap,U0=new WeakMap;return q1e;})();
|
|
/**
|
|
* @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
|
|
*
|
|
* 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
|
|
*
|
|
* 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 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use backend 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 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.
|
|
* =============================================================================
|
|
*/
|
|
/**
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|
* Human main module
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* @default Human Library
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* @summary <https://github.com/vladmandic/human>
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* @author <https://github.com/vladmandic>
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* @copyright <https://github.com/vladmandic>
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* @license MIT
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*/
|
|
/**
|
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* @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. */
|