mirror of https://github.com/vladmandic/human
8034 lines
1.6 MiB
8034 lines
1.6 MiB
/*
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Human
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homepage: <https://github.com/vladmandic/human>
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author: <https://github.com/vladmandic>'
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*/
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Actual: ${r}.
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Actual: ${r}.
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o=F(e,"x","batchNorm"),i=F(t,"mean","batchNorm"),l=F(n,"variance","batchNorm"),c;r!=null&&(c=F(r,"scale","batchNorm"));let u;return s!=null&&(u=F(s,"offset","batchNorm")),M(o.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${o.rank}.`),M(i.rank===4||i.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${i.rank}.`),M(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${l.rank}.`),c!=null&&M(c.rank===4||c.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${c.rank}.`),u!=null&&M(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${u.rank}.`),Wu(o,i,l,u,c,a)}var bv=G({batchNorm4d_:v$});function w$(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 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Got strides ${n} and dilations '${a}'`);let p={x:c,filter:l},h={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},f=U.runKernel(Oa,p,h);return u?H(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Do=G({conv2d_:_$});function $$(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. 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Got strides ${n} and dilations '${a}'`),M(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let u={x:l,filter:i},d={strides:n,pad:s,dataFormat:r,dilations:a},p=U.runKernel(dd,u,d);return c?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var ly=G({conv3d_:P$});function O$(e,t,n,s,r){M(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=H(t,[1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]]),a=[1,e[0],e[1],e[2],e[3]]);let l=a[4],c=o.shape[4];M(a.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`),M(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),M(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),M(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),M(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:o,filter:n},d={pad:r,strides:s,inputShape:a},p=U.runKernel(Oh,u,d);return i?H(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Cv=G({conv3DBackpropInput_:O$});function M$(e,t,n,s,r){let a=F(e,"x","conv3dTranspose"),o=F(t,"filter","conv3dTranspose");return Cv(n,a,o,s,r)}var Tv=G({conv3dTranspose_:M$});function z$(e){let n={x:F(e,"x","cos","float32")};return U.runKernel(za,n)}var bf=G({cos_:z$});function L$(e){let n={x:F(e,"x","cosh","float32")};return U.runKernel(La,n)}var uy=G({cosh_:L$});function B$(e,t=0,n=!1,s=!1){let a={x:F(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:s};return U.runKernel(gi,a,o)}var cy=G({cumsum_:B$});function W$(e,t,n,s=!1){let r=F(e,"x","denseBincount"),a=F(t,"weights","denseBincount");M(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),M(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),M(n>=0,()=>`size must be non-negative, but got ${n}.`),M(a.size===r.size||a.size===0,()=>`Error in denseBincount: weights must have the same shape as x or 0-length, but got x shape: ${r.shape}, weights shape: ${a.shape}.`);let o={x:r,weights:a},i={size:n,binaryOutput:s};return U.runKernel(Mh,o,i)}var Nv=G({denseBincount_:W$});function V$(e,t,n="NHWC"){let s=F(e,"x","depthToSpace","float32"),r=n==="NHWC"?s.shape[1]:s.shape[2],a=n==="NHWC"?s.shape[2]:s.shape[3],o=n==="NHWC"?s.shape[3]:s.shape[1];M(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),M(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${r} and ${t} for depthToSpace with input shape
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${s.shape}`),M(a*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${a} and ${t} for depthToSpace with input shape
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|
${s.shape}`),M(o%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${o} for depthToSpace with input shape ${s.shape}`);let i={x:s},l={blockSize:t,dataFormat:n};return U.runKernel(Ai,i,l)}var Ev=G({depthToSpace_:V$});function U$(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 ${l.shape[2]}.`),Bn("depthwiseConv2d",s,o);let d={x:c,filter:l},p={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},h=U.runKernel(Ba,d,p);return u?H(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Bd=G({depthwiseConv2d_:U$});function G$(e){let n={x:F(e,"x","diag")};return U.runKernel(Bh,n)}var H$=G({diag_:G$});function j$(e,t,n,s,r=[1,1],a="NHWC"){let o=F(e,"x","dilation2d"),i=F(t,"filter","dilation2d");M(o.rank===3||o.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${o.rank}.`),M(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),M(a==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);let l=o,c=!1;o.rank===3&&(l=H(o,[1,o.shape[0],o.shape[1],o.shape[2]]),c=!0);let u={x:l,filter:i},d={strides:n,pad:s,dilations:r},p=U.runKernel(pd,u,d);return c?H(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var Rv=G({dilation2d_:j$});function q$(e,t){let n=F(e,"a","equal","string_or_numeric"),s=F(t,"b","equal","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return U.runKernel(xi,r)}var Ps=G({equal_:q$});function X$(e,t,n){let s=F(t,"a","where"),r=F(n,"b","where"),a=F(e,"condition","where","bool"),o=At(At(a.shape,s.shape),r.shape),i=Ld(a,o),l=Ld(s,o),c=Ld(r,o),u={condition:i,t:l,e:c};return U.runKernel(Wi,u)}var Wn=G({where_:X$});function K$(e){let n={x:F(e,"x","zerosLike")};return U.runKernel(Yi,n)}var nt=G({zerosLike_:K$});function Z$(e,t){let n=F(e,"a","div"),s=F(t,"b","div");[n,s]=Ot(n,s);let r=de(n,s),a=nt(r),o=Ps(s,a);return Wn(o,a,r)}var _v=G({divNoNan_:Z$});function Y$(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 ${a}.`),n.rank===1&&s.rank===1){let o=H(n,[1,-1]),i=H(s,[-1,1]),l=je(o,i);return H(l,[])}else if(n.rank===1&&s.rank===2){let o=H(n,[1,-1]),i=H(s,[s.shape[0],s.shape[1]]),l=je(o,i);return H(l,[l.size])}else if(n.rank===2&&s.rank===1){let o=H(s,[-1,1]),i=je(n,o);return H(i,[i.size])}else{let o=H(s,[s.shape[0],s.shape[1]]);return je(n,o)}}var J$=G({dot_:Y$});function Q$(e,...t){let n=t.map((r,a)=>F(r,`tensors${a}`,"einsum")),s={equation:e};return U.runKernel(hd,n,s)}var $v=G({einsum_:Q$});function eD(e){let n={x:F(e,"x","elu","float32")};return U.runKernel(Va,n)}var Wd=G({elu_:eD});function tD(e){let t=F(e,"x","erf");M(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=me(t,"float32"));let n={x:t};return U.runKernel(yu,n)}var Dv=G({erf_:tD});function nD(e){let n={x:F(e,"x","exp")};return U.runKernel(Ua,n)}var Os=G({exp_:nD});function sD(e,t=0){let n=F(e,"x","expandDims","string_or_numeric");M(t<=n.rank,()=>"Axis must be <= rank of the tensor");let s={input:n},r={dim:t};return U.runKernel(bi,s,r)}var Zt=G({expandDims_:sD});function rD(e){let n={x:F(e,"x","expm1")};return U.runKernel(vi,n)}var Fv=G({expm1_:rD});function aD(e,t){let n=F(e,"x","tile","string_or_numeric");M(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let s={x:n},r={reps:t};return U.runKernel(Kr,s,r)}var Js=G({tile_:aD});function oD(e,t,n,s="float32"){t==null&&(t=e);let r=ze([e,t],s),a=e<=t?e:t;for(let i=0;i<a;++i)r.set(1,i,i);let o=H(r.toTensor(),[e,t]);if(n==null)return o;if(n.length===1)return Js(Zt(o,0),[n[0],1,1]);if(n.length===2)return Js(Zt(Zt(o,0),0),[n[0],n[1],1,1]);if(n.length===3)return Js(Zt(Zt(Zt(o,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${n.length}D.`)}var dy=G({eye_:oD});function Uu(e,t,n){let s={shape:e,value:t,dtype:n};return U.runKernel(Au,{},s)}function iD(e){let n={x:F(e,"x","floor","float32")};return U.runKernel(Ga,n)}var Vd=G({floor_:iD});function lD(e,t,n=0,s=0){let r=F(e,"x","gather"),a=F(t,"indices","gather","int32"),o={x:r,indices:a},i={axis:n,batchDims:s};return U.runKernel(ki,o,i)}var Gu=G({gather_:lD});function uD(e,t){let n=F(e,"a","greater","string_or_numeric"),s=F(t,"b","greater","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return U.runKernel(Ii,r)}var xs=G({greater_:uD});function cD(e,t){let n=F(e,"a","greaterEqual","string_or_numeric"),s=F(t,"b","greaterEqual","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return U.runKernel(qa,r)}var ul=G({greaterEqual_:cD});function dD(e){let n={input:F(e,"input","imag")};return U.runKernel(fd,n)}var vf=G({imag_:dD});function pD(e){let n={x:F(e,"x","isFinite")};return U.runKernel(xu,n)}var hD=G({isFinite_:pD});function fD(e){let n={x:F(e,"x","isInf")};return U.runKernel(bu,n)}var mD=G({isInf_:fD});function gD(e){let n={x:F(e,"x","isNaN")};return U.runKernel(vu,n)}var Pv=G({isNaN_:gD});function yD(e,t=.2){let s={x:F(e,"x","leakyRelu")},r={alpha:t};return U.runKernel(Ka,s,r)}var wf=G({leakyRelu_:yD});function AD(e,t){let n=F(e,"a","less","string_or_numeric"),s=F(t,"b","less","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return U.runKernel(Ci,r)}var py=G({less_:AD});function xD(e,t){let n=F(e,"a","lessEqual","string_or_numeric"),s=F(t,"b","lessEqual","string_or_numeric");[n,s]=Ot(n,s),At(n.shape,s.shape);let r={a:n,b:s};return U.runKernel(Ti,r)}var cl=G({lessEqual_:xD});function Ov(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let s={start:e,stop:t,num:n};return U.runKernel(jh,{},s)}function bD(e,t=5,n=1,s=1,r=.5){let a=F(e,"x","localResponseNormalization");M(a.rank===4||a.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
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rank ${a.rank}.`),M(ru(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let o=a,i=!1;a.rank===3&&(i=!0,o=H(a,[1,a.shape[0],a.shape[1],a.shape[2]]));let l={x:o},c={depthRadius:t,bias:n,alpha:s,beta:r},u=U.runKernel(gd,l,c);return i?H(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var Mv=G({localResponseNormalization_:bD});function vD(e){let n={x:F(e,"x","log","float32")};return U.runKernel(Za,n)}var Ms=G({log_:vD});function wD(e){let n={x:F(e,"x","log1p")};return U.runKernel(wu,n)}var kf=G({log1p_:wD});function kD(e){return M(Ea(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let s=F(t,"x","tf.grad","string_or_numeric"),r=n!=null?F(n,"dy","tf.grad"):null;return U.tidy(()=>{let{value:a,grads:o}=U.gradients(()=>e(s),[s],r);return r!=null&&On(a.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Sf(o),o[0]})}}function SD(e){return M(Ea(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 U.tidy(()=>{let{value:a,grads:o}=U.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,...])"),Sf(o),o})}}function ID(e){return M(Ea(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{M(t instanceof et,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),M(n==null||n instanceof et,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:s,value:r}=U.gradients(()=>e(t),[t],n);return Sf(s),{grad:s[0],value:r}}}function CD(e){return M(Ea(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{M(Array.isArray(t)&&t.every(r=>r instanceof et),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),M(n==null||n instanceof et,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let s=U.gradients(()=>e(...t),t,n);return n!=null&&On(s.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Sf(s.grads),s}}function zv(e,t){M(Ea(e),()=>"The f passed in variableGrads(f) must be a function"),M(t==null||Array.isArray(t)&&t.every(c=>c instanceof Dd),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let c in U.registeredVariables)t.push(U.registeredVariables[c])}let s=n?t.filter(c=>!c.trainable):null,r=t.length;t=t.filter(c=>c.trainable),M(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let a=!0,{value:o,grads:i}=U.gradients(e,t,null,a);M(i.some(c=>c!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),M(o.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${o.rank} tensor`);let l={};return t.forEach((c,u)=>{i[u]!=null&&(l[c.name]=i[u])}),s!=null&&s.forEach(c=>l[c.name]=null),{value:o,grads:l}}function $r(e){return U.customGrad(e)}function Sf(e){if(e.filter(n=>n==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
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the f you passed encloses all operations that lead from x to y.`)}function TD(e){let n={x:F(e,"x","neg")};return U.runKernel(Ei,n)}var zt=G({neg_:TD});function ND(e){let n={x:F(e,"x","softplus")};return U.runKernel(_u,n)}var Hu=G({softplus_:ND});function ED(e){let t=F(e,"x","logSigmoid");return $r(s=>({value:zt(Hu(zt(s))),gradFunc:o=>L(o,as(zt(s)))}))(t)}var RD=G({logSigmoid_:ED});function _D(e,t=null,n=!1){let r={x:F(e,"x","max")},a={reductionIndices:t,keepDims:n};return U.runKernel(Ya,r,a)}var An=G({max_:_D});function $D(e,t){let n=F(e,"a","sub"),s=F(t,"b","sub");[n,s]=Ot(n,s);let r={a:n,b:s};return U.runKernel(xo,r)}var he=G({sub_:$D});function DD(e,t=null,n=!1){let s=F(e,"x","sum");s.dtype==="bool"&&(s=me(s,"int32"));let r={x:s},a={axis:t,keepDims:n};return U.runKernel(go,r,a)}var we=G({sum_:DD});function FD(e,t=-1){let n=F(e,"logits","logSoftmax");if(t===-1&&(t=n.rank-1),t!==n.rank-1)throw Error(`Log Softmax along a non-last dimension is not yet supported. 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a=r,o=!1;r.rank===3&&(o=!0,a=H(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:s,size:t},c=U.runKernel(Nu,i,l);return o?H(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var EO=G({resizeNearestNeighbor_:NO});function RO(e,t="binary",n=!1,s=.5){let r=F(e,"image","threshold"),a=.2989,o=.587,i=.114,l=r.shape[0]*r.shape[1],c=L(Ct([s]),255),u,d,p,h;if(M(r.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${r.rank}.`),M(r.shape[2]===3||r.shape[2]===1,()=>`Error in threshold: image color channel must be equal to 3 or 1but got ${r.shape[2]}.`),M(r.dtype==="int32"||r.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`),M(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),r.shape[2]===3){[u,d,p]=Yt(r,[1,1,1],-1);let g=L(u,a),y=L(d,o),x=L(p,i);h=ue(ue(g,y),x)}else h=e;if(t==="otsu"){let g=ry(me(wy(h),"int32"),pt([]),256);c=_O(g,l)}let f=n?cl(h,c):xs(h,c);return 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FO=G({transform_:DO});function PO(e,t,n){M(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),M(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let s=F(e,"a","bandPart");M(s.rank>=2,()=>`bandPart(): Rank must be at least 2, got ${s.rank}.`);let r=s.shape,[a,o]=s.shape.slice(-2);if(!(t<=a))throw new Error(`bandPart(): numLower (${t}) must not be greater than the number of rows (${a}).`);if(!(n<=o))throw new Error(`bandPart(): numUpper (${n}) must not be greater than the number of columns (${o}).`);t<0&&(t=a),n<0&&(n=o);let i=H(Xu(0,a,1,"int32"),[-1,1]),l=Xu(0,o,1,"int32"),c=he(i,l),u=hr(cl(c,Ce(+t,"int32")),ul(c,Ce(-n,"int32"))),d=Gt([a,o],s.dtype);return H(an(os(H(s,[-1,a,o])).map(p=>Wn(u,p,d))),r)}var OO=G({bandPart_:PO});function MO(e){let t;if(Array.isArray(e)){t=!1,M(e!=null&&e.length>0,()=>"Gram-Schmidt process: input must not be null, undefined, or empty");let r=e[0].shape[0];for(let a=1;a<e.length;++a)M(e[a].shape[0]===r,()=>`Gram-Schmidt: Non-unique lengths found in the input vectors: (${e[a].shape[0]} vs. ${r})`)}else t=!0,e=Yt(e,e.shape[0],0).map(r=>rt(r,[0]));M(e.length<=e[0].shape[0],()=>`Gram-Schmidt: Number of vectors (${e.length}) exceeds number of dimensions (${e[0].shape[0]}).`);let n=[],s=e;for(let r=0;r<e.length;++r)n.push(U.tidy(()=>{let a=s[r];if(r>0)for(let o=0;o<r;++o){let i=L(we(L(n[o],a)),n[o]);a=he(a,i)}return de(a,$y(a,"euclidean"))}));return t?an(n,0):n}var zO=G({gramSchmidt_:MO});function LO(e,t=!1){if(M(e.rank>=2,()=>`qr() requires input tensor to have a rank >= 2, but got rank ${e.rank}`),e.rank===2)return gw(e,t);{let n=e.shape.slice(0,e.shape.length-2).reduce((l,c)=>l*c),s=os(H(e,[n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0),r=[],a=[];s.forEach(l=>{let[c,u]=gw(l,t);r.push(c),a.push(u)});let o=H(an(r,0),e.shape),i=H(an(a,0),e.shape);return[o,i]}}function gw(e,t=!1){return U.tidy(()=>{M(e.shape.length===2,()=>`qr2d() requires a 2D Tensor, but got a ${e.shape.length}D Tensor.`);let n=e.shape[0],s=e.shape[1],r=dy(n),a=Ln(e),o=fr([[1]],[1,1]),i=Ln(o),l=n>=s?s:n;for(let c=0;c<l;++c){let u=a,d=i,p=r;[i,a,r]=U.tidy(()=>{let h=Pe(a,[c,c],[n-c,1]),f=$y(h),m=Pe(a,[c,c],[1,1]),g=Wn(xs(m,0),fr([[-1]]),fr([[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 BO=G({qr_:LO}),is=(e=>(e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS",e))(is||{});function WO(e,t,n=is.SUM_BY_NONZERO_WEIGHTS){let s=F(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=F(t,"weights","computeWeightedLoss"));let a=r==null?s:L(s,r);if(n===is.NONE)return a;if(n===is.SUM)return we(a);if(n===is.MEAN){if(r==null)return Ut(a);{let o=s.size/r.size,i=de(we(a),we(r));return o>1?de(i,Ce(o)):i}}if(n===is.SUM_BY_NONZERO_WEIGHTS){if(r==null)return de(we(a),Ce(s.size));{let o=L(r,bs(s.shape)),i=me(we(ju(o,Ce(0))),"float32");return de(we(a),i)}}throw Error(`Unknown reduction: ${n}`)}var Qr=G({computeWeightedLoss_:WO});function VO(e,t,n,s=is.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","absoluteDifference"),a=F(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=F(n,"weights","absoluteDifference")),On(r.shape,a.shape,"Error in absoluteDifference: ");let i=rn(he(r,a));return Qr(i,o,s)}var UO=G({absoluteDifference_:VO});function GO(e,t,n,s,r=is.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","cosineDistance"),o=F(t,"predictions","cosineDistance"),i=null;s!=null&&(i=F(s,"weights","cosineDistance")),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 HO=G({cosineDistance_:GO});function jO(e,t,n,s=is.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","hingeLoss"),a=F(t,"predictions","hingeLoss"),o=null;n!=null&&(o=F(n,"weights","hingeLoss")),On(r.shape,a.shape,"Error in hingeLoss: ");let i=Ce(1);r=he(L(Ce(2),r),i);let l=Dr(he(i,L(r,a)));return Qr(l,o,s)}var qO=G({hingeLoss_:jO});function XO(e,t,n,s=1,r=is.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","huberLoss"),o=F(t,"predictions","huberLoss"),i=null;n!=null&&(i=F(n,"weights","huberLoss")),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 KO=G({huberLoss_:XO});function ZO(e,t,n,s=1e-7,r=is.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"labels","logLoss"),o=F(t,"predictions","logLoss"),i=null;n!=null&&(i=F(n,"weights","logLoss")),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 YO=G({logLoss_:ZO});function JO(e,t,n,s=is.SUM_BY_NONZERO_WEIGHTS){let r=F(e,"labels","meanSquaredError"),a=F(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=F(n,"weights","meanSquaredError")),On(r.shape,a.shape,"Error in meanSquaredError: ");let i=Ey(r,a);return Qr(i,o,s)}var QO=G({meanSquaredError_:JO});function eM(e,t){let n=F(e,"labels","sigmoidCrossEntropyWithLogits"),s=F(t,"logits","sigmoidCrossEntropyWithLogits");On(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=Dr(s),a=L(s,n),o=kf(Os(zt(rn(s))));return ue(he(r,a),o)}function tM(e,t,n,s=0,r=is.SUM_BY_NONZERO_WEIGHTS){let a=F(e,"multiClassLabels","sigmoidCrossEntropy"),o=F(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=F(n,"weights","sigmoidCrossEntropy")),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=eM(a,o);return Qr(l,i,r)}var nM=G({sigmoidCrossEntropy_:tM});function sM(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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${r.shape}`);if(a.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${a.shape}`);if(o.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${o.shape}`);if(i.rank!==0)throw new Error(`Default value should be a scalar but received shape ${i.shape}`);let l={indices:r,values:a,denseShape:o,defaultValue:i},c=U.runKernel(xd,l);return{outputIndices:c[0],outputValues:c[1],emptyRowIndicator:c[2],reverseIndexMap:c[3]}}var iM=G({sparseFillEmptyRows_:oM});function lM(e,t,n){let s=F(e,"inputIndices","sparseReshape","int32"),r=F(t,"inputShape","sparseReshape","int32"),a=F(n,"newShape","sparseReshape","int32");if(s.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
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|
${s.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(a.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${a.shape}`);let o={inputIndices:s,inputShape:r,newShape:a},i=U.runKernel($u,o);return{outputIndices:i[0],outputShape:i[1]}}var uM=G({sparseReshape_:lM});function cM(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
|
|
${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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|
${a.shape}`);let o={data:s,indices:r,segmentIds:a};return U.runKernel(bd,o)}var dM=G({sparseSegmentMean_:cM});function pM(e,t,n){let s=F(e,"data","sparseSegmentSum"),r=F(t,"indices","sparseSegmentSum","int32"),a=F(n,"segmentIds","sparseSegmentSum","int32");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${r.shape}`);if(a.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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${a.shape}`);let o={data:s,indices:r,segmentIds:a};return U.runKernel(vd,o)}var hM=G({sparseSegmentSum_:pM});function fM(e,t,n,s,r,a,o,i){let l=F(e,"data","stringNGrams","string");if(l.dtype!=="string")throw new Error("Data must be of datatype string");if(l.shape.length!==1)throw new Error(`Data must be a vector, saw: ${l.shape}`);let c=F(t,"dataSplits","stringNGrams");if(c.dtype!=="int32")throw new Error("Data splits must be of datatype int32");let u={separator:n,nGramWidths:s,leftPad:r,rightPad:a,padWidth:o,preserveShortSequences:i},d={data:l,dataSplits:c},p=U.runKernel(kd,d,u);return{nGrams:p[0],nGramsSplits:p[1]}}var mM=G({stringNGrams_:fM});function gM(e,t,n=!0){let s=F(e,"input","stringSplit","string"),r=F(t,"delimiter","stringSplit","string");if(s.rank!==1)throw new Error(`Input should be Tensor1D but received shape ${s.shape}`);if(r.rank!==0)throw new Error(`Delimiter should be a scalar but received shape ${r.shape}`);let a={skipEmpty:n},o={input:s,delimiter:r},i=U.runKernel(ef,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var yM=G({stringSplit_:gM});function AM(e,t){let n=F(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return U.runKernel(tf,r,s)}var xM=G({stringToHashBucketFast_:AM}),bM={fft:_f,ifft:jd,rfft:$f,irfft:Ny},vM={hammingWindow:KP,hannWindow:cw,frame:dw,stft:QP},Se={flipLeftRight:sO,grayscaleToRGB:aO,resizeNearestNeighbor:EO,resizeBilinear:TO,rotateWithOffset:iO,cropAndResize:tO,nonMaxSuppression:uO,nonMaxSuppressionAsync:yO,nonMaxSuppressionWithScore:xO,nonMaxSuppressionWithScoreAsync:vO,nonMaxSuppressionPadded:kO,nonMaxSuppressionPaddedAsync:IO,threshold:$O,transform:FO},yw={bandPart:OO,gramSchmidt:zO,qr:BO},wM={absoluteDifference:UO,computeWeightedLoss:Qr,cosineDistance:HO,hingeLoss:qO,huberLoss:KO,logLoss:YO,meanSquaredError:QO,sigmoidCrossEntropy:nM,softmaxCrossEntropy:aM},Xd={sparseFillEmptyRows:iM,sparseReshape:uM,sparseSegmentMean:dM,sparseSegmentSum:hM},zf={stringNGrams:mM,stringSplit:yM,stringToHashBucketFast:xM},ea=class extends ev{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 this.applyGradients(r);return te(r),t?s:(s.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return zv(e,t)}dispose(){this.iterations_!=null&&te(this.iterations_)}async saveIterations(){return this.iterations_==null&&(this.iterations_=0),{name:"iter",tensor:Ce(this.iterations_,"int32")}}async getWeights(){throw new Error("getWeights() is not implemented for this optimizer yet.")}async setWeights(e){throw new Error(`setWeights() is not implemented for this optimizer class ${this.getClassName()}`)}async extractIterations(e){return this.iterations_=(await e[0].tensor.data())[0],e.slice(1)}};Object.defineProperty(ea,Symbol.hasInstance,{value:e=>e.minimize!=null&&e.computeGradients!=null&&e.applyGradients!=null});var Lf=class extends ea{constructor(e,t,n=null){super();this.learningRate=e,this.rho=t,this.epsilon=n,this.accumulatedGrads=[],this.accumulatedUpdates=[],n==null&&(this.epsilon=U.backend.epsilon())}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=U.registeredVariables[n],a=!1;this.accumulatedGrads[s]==null&&(this.accumulatedGrads[s]={originalName:`${n}/accum_grad`,variable:K(()=>nt(r).variable(a))}),this.accumulatedUpdates[s]==null&&(this.accumulatedUpdates[s]={originalName:`${n}/accum_var`,variable:K(()=>nt(r).variable(a))});let o=Array.isArray(e)?e[s].tensor:e[n];if(o==null)return;let i=this.accumulatedGrads[s].variable,l=this.accumulatedUpdates[s].variable;K(()=>{let c=ue(L(i,this.rho),L(xt(o),1-this.rho)),u=L(de(_n(ue(l,this.epsilon)),_n(ue(i,this.epsilon))),o),d=ue(L(l,this.rho),L(xt(u),1-this.rho));i.assign(c),l.assign(d);let 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ea{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=U.registeredVariables[n];if(this.accumulatedGrads[s]==null){let i=!1;this.accumulatedGrads[s]={originalName:`${n}/accumulator`,variable:K(()=>Uu(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,_n(ue(i,U.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)}};Bf.className="Adagrad";_o(Bf);var Wf=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=U.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=U.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(_n(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(Po(this.beta1,this.iterations_+1)),this.accBeta2.assign(Po(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)}};Wf.className="Adam";_o(Wf);var Vf=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=U.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)}};Vf.className="Adamax";_o(Vf);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=U.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=U.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),_n(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),_n(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] = ${e}`}function nz(e,t){return`indices(${e}, 0) is invalid: ${t} < 0`}function sz(e,t,n){return`indices(${e}, 0) is invalid: ${t} >= ${n}`}function rz(e,t){return`only one output dimension may be -1, not both ${e} and ${t}`}function az(e,t){return`size ${e} must be non-negative, not ${t}`}function oz(){return"reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero"}function iz(e,t){let n=It(e),s=It(t);return`Input to reshape is a SparseTensor with ${n}
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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}}},cW=0,st=class extends ce.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=cW++,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)+"_"+sm(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 gr(`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 xr)){s=!1;break}let r=!0;for(let a of n)if(a instanceof xr){r=!1;break}if(s===r)throw new q("Arguments to apply() must be all SymbolicTensors or all Tensors");return Al(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=dW(e),o=this.computeOutputShape(a),i,l=pW(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 xr(l,c,this,Tt(e),t,this.name,u)):i=new xr(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 gr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return am(this.weights)}build(e){this.built=!0}getWeights(e=!1){return o1(e?this.trainableWeights:this.weights)}setWeights(e){K(()=>{let t=this.weights;if(t.length!==e.length)throw new q(`You called setWeights(weights) on layer "${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=o1(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])}i1(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():Dt("zeros"));let l=s.apply(t,n),c=new Qw(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,o=null){let i=Tt(e);t=Tt(t),n=Tt(n),s=Tt(s),r=rm(r),a=rm(a);let l=[],c=[],u=[];for(let d of i)l.push(d.sourceLayer),c.push(d.nodeIndex),u.push(d.tensorIndex);new om({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},o);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 dW(e){e=Tt(e);let t=[];for(let n of e)t.push(n.shape);return ls(t)}function pW(e){return"float32"}function ek(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=ek(o,i,l);for(let u of c)r.indexOf(u)===-1&&r.push(u)}return r}}}var Qu=class extends st{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:sm("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 xr(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new om({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=sm(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],zo(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 Qu))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 gr(`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],$=I.inboundLayers[R],_=I.nodeIndices[R],T=I.tensorIndices[R];i(P,x,A,$,_,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(Hf);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(Hf);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 gr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${A.name}". The following previous layers were accessed without issue: ${m}`);for(let b of x.outputTensors)f.push(b);m.push(A.name)}}this.nodesByDepth=d;let g=this.layers.map(y=>y.name);for(let y of g){let x=g.filter(A=>A===y).length;if(x!==1)throw new gr(`The name "${y}" is used ${x} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new om({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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}`)}i1(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${g1}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=m1(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return K(()=>{e=Tt(e);let n=new vl;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=ml(null,e.length):n=Tt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=rm(e);if(t.length!==this.inputLayers.length)throw new q(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],c=i.name+"_0_0";n[c]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Hf);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],y=l.tensorIndices[f],x=`${m.name}_${g}_${y}`,A=n[x];u.push(A)}let d=c.computeOutputShape(ls(u)),p=rm(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=ml(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(Hf);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=br(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(;!bB(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 GW(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: ${JSON.stringify(e)}`)}function Ak(e,t){return GW(e,t,"classWeight")}async function xk(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=K(()=>{if(e.shape.length===1)return Ln(e);if(e.shape.length===2){if(e.shape[1]>1)return Fs(e,1);if(e.shape[1]===1)return H(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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 HW(e,t){return L(e,t)}var jW=32;function bk(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=vk("input",e.inputNames,n),o=vk("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 vk(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 qW(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 XW(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(wk(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=qW(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=ok(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=ik(u,d,n.epochs,null,null,KW(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). 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Interrupting evalution. Make sure that your dataset can generate at least \`batches\` batches (in this case, ${n.batches} batches). 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),s=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),a=[],o=()=>{let u=[];for(let f=0;f<this.inputs.length;++f)u.push({key:this.inputs[f],value:n[f]});let d=new vl(u),p=rp(this.outputs,d,{training:!0}),h;for(let f=0;f<this.lossFunctions.length;++f){let g=this.lossFunctions[f](s[f],p[f]);r[f]!=null&&(g=HW(g,r[f]));let y=Ut(g);t.push(y),f===0?h=g:h=ue(h,g)}for(let f=0;f<this.metricsTensors.length;++f){let m;if(this.outputs.length>1&&f<this.outputs.length)m=t[f];else{let g=this.metricsTensors[f][0],y=this.metricsTensors[f][1];m=Ut(g(s[y],p[y]))}yn(m),a.push(m)}return h=Ut(h),this.calculateLosses().forEach(f=>{h=ue(h,f)}),h},i=this.collectedTrainableWeights.map(u=>u.read()),l=!0;return[this.optimizer_.minimize(o,l,i)].concat(a)}}makeTestFunction(){this.testFunction=e=>K(()=>{let t=[],n,s=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),a=[];for(let l=0;l<this.inputs.length;++l)a.push({key:this.inputs[l],value:s[l]});let o=new vl(a),i=rp(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=Ut(c(r[l],i[l]));l===0?n=u:n=ue(n,u),t.push(n)}for(let l=0;l<this.metricsTensors.length;++l){let c=this.metricsTensors[l][0],u=this.metricsTensors[l][1],d=Ut(c(r[u],i[u]));t.push(d)}return t})}async fit(e,t,n={}){return QW(this,e,t,n)}async fitDataset(e,t){return XW(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),s=n[0],r=n[1],o=this.makeTrainFunction()(s.concat(r)),i=[];for(let l of o){let c=await l.data();i.push(c[0])}return te(o),ls(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,s=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let a=0;a<s.length;++a)n&&!s[a].trainable||t.push({name:s[a].originalName,tensor:r[a]});return t}set 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=pf().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-pf().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 this.metrics=="function")return[na(hm(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>na(hm(e)));{let e={};for(let t in this.metrics)e[t]=na(hm(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=sp(e.optimizer_config),n=br(t),s;if(typeof e.loss=="string")s=gl(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>gl(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=gl(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>gl(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=gl(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=ss.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 ss.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:rV,generatedBy:`TensorFlow.js tfjs-layers v${g1}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:c,specs:u}=await ss.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...u),n.data=ss.concatenateArrayBuffers([n.data,c])}if(this.userDefinedMetadata!=null){let l=!0;fk(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){fk(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};sa.className="Model";ce.registerClass(sa);var Tk=class extends sa{};Tk.className="Functional";ce.registerClass(Tk);async function aV(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=sp(n),r=br(s,t);if(e.weightsManifest!=null){let a=await ss.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(i=>i.originalName)),o={};for(let i of r.weights)o[i.originalName]=a[i.originalName];r.loadWeights(o),te(a)}return r}async function oV(e,t){if(t==null&&(t={}),typeof e=="string"){let n=ss.getLoadHandlers(e,t);if(n.length===0)n.push(ss.browserHTTPRequest(e,t));else if(n.length>1)throw new q(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return iV(e,void 0,t)}async function iV(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=br(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}=lV(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 lV(e,t){let n=ss.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 w1=class extends sa{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:sm("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 w1||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. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new q("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new q("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=tk({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(s)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new q(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new q("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=ek(this.outputs[0])}this.inboundNodes=[],new om({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:ml(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(s=>s.shape),outputShapes:this.outputs[0].shape})}else{let s=e.apply(this.outputs[0]);if(Array.isArray(s))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[s],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(ft(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. 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 gr("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 gr("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 gr("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 gr("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 w1))throw new Le(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let c=br(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}}},mm=w1;mm.className="Sequential";ce.registerClass(mm);function uV(e){return new sa(e)}function cV(e){return new mm(e)}function dV(e,t){return t==null&&(t={}),oV(e,t)}function Nk(e){return tk(e)}function pV(e,t){l1.registerCallbackConstructor(e,t)}var cs=class extends ce.Serializable{getConfig(){return{}}},Ek=class extends cs{apply(e,t=1){return zB(e,t)}};Ek.className="elu";ce.registerClass(Ek);var Rk=class extends cs{apply(e){return Sy(e)}};Rk.className="selu";ce.registerClass(Rk);var _k=class extends cs{apply(e){return Dr(e)}};_k.className="relu";ce.registerClass(_k);var $k=class extends cs{apply(e){return K(()=>Ud(6,Dr(e)))}};$k.className="relu6";ce.registerClass($k);var Dk=class extends cs{apply(e){return e}};Dk.className="linear";ce.registerClass(Dk);var Fk=class extends cs{apply(e){return as(e)}};Fk.className="sigmoid";ce.registerClass(Fk);var Pk=class extends cs{apply(e){return BB(e)}};Pk.className="hardSigmoid";ce.registerClass(Pk);var Ok=class extends cs{apply(e){return Hu(e)}};Ok.className="softplus";ce.registerClass(Ok);var Mk=class extends cs{apply(e){return LB(e)}};Mk.className="softsign";ce.registerClass(Mk);var zk=class extends cs{apply(e){return Bu(e)}};zk.className="tanh";ce.registerClass(zk);var k1=class extends cs{apply(e,t=-1){return Ku(e,t)}};k1.className="softmax";ce.registerClass(k1);var Lk=class extends cs{apply(e,t=-1){return hy(e,t)}};Lk.className="logSoftmax";ce.registerClass(Lk);var Bk=class extends cs{apply(e,t=1){return K(()=>L(as(L(e,t)),e))}};Bk.className="swish";ce.registerClass(Bk);var Wk=class extends cs{apply(e){return K(()=>L(e,Bu(Hu(e))))}};Wk.className="mish";ce.registerClass(Wk);function Vo(e){return e.getClassName()}function S1(e,t={}){return Zd(e,ce.SerializationMap.getMap().classNameMap,t,"activation")}function Uo(e){if(e==null){let t={};return t.className="linear",t.config={},S1(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},S1(t)}else return e instanceof cs?e:S1(e)}function I1(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 Vk=class extends ce.Serializable{},op=class extends Vk{constructor(e){super();I1(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=Gt([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 hV(e){return I1(e),new op({l1:e!=null?e.l1:null,l2:0})}function fV(e){return I1(e),new op({l2:e!=null?e.l2:null,l1:0})}var Uk={l1l2:"L1L2"};function bt(e){return By(e)}function Gk(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 Uk?Uk[e]:e,config:{}};return Gk(n)}else return e instanceof Vk?e:Gk(e)}var C1=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=Dr(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}};C1.className="ReLU";ce.registerClass(C1);var T1=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 wf(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};T1.className="LeakyReLU";ce.registerClass(T1);var N1=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Dt(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 ${e.sharedAxes}`)}build(e){e=ft(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Jt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Ve(e),Ef(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Lt(this.alphaInitializer),alphaRegularizer:bt(this.alphaRegularizer),alphaConstraint:ln(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};N1.className="PReLU";ce.registerClass(N1);var E1=class extends st{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Le(`Non-default alpha value (${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}};E1.className="ELU";ce.registerClass(E1);var R1=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,me(xs(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};R1.className="ThresholdedReLU";ce.registerClass(R1);var _1=class extends st{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new k1().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}};_1.className="Softmax";ce.registerClass(_1);function nc(e,t,n){if(typeof e=="number")return ml(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(!FB(r))throw new q(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function vr(e,t,n,s,r=1){if(e==null)return e;let a=t+(t-1)*(r-1),o;return n==="same"?o=e:o=e-a+1,Math.floor((o+s-1)/s)}function zr(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Bo([n-t,0]);else if(s==="same")e=e*t;else throw new q(`Unsupport padding mode: ${s}.`);return e}function $1(e,t){return K(()=>(Ht(t),t==="channelsFirst"?tt(e,[0,2,3,1]):e))}function Hk(e,t){return K(()=>(Ht(t),t==="channelsFirst"?tt(e,[0,2,3,4,1]):e))}function mV(e,t,n,s=1,r="valid",a,o=1){return K(()=>{if(a==null&&(a=mr()),Ht(a),e.shape.length!==3)throw new q(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new q(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new q(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=tt(e,[0,2,1])),r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=ay(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=Ar(i,n)),i})}function jk(e,t,n,s=[1,1],r="valid",a,o,i=null){return K(()=>{if(a==null&&(a=mr()),Ht(a),e.rank!==3&&e.rank!==4)throw new q(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new q(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=$1(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Oo.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 gV(e,t,n,s=[1,1,1],r="valid",a,o){return K(()=>{if(a==null&&(a=mr()),Ht(a),e.rank!==4&&e.rank!==5)throw new q(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new q(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=Hk(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=ly(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=Ar(i,n)),a==="channelsFirst"&&(i=tt(i,[0,4,1,2,3])),i})}var D1=class extends st{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",D1.verifyArgs(t),this.rank=e,xn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Le(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=nc(t.kernelSize,e,"kernelSize"),this.strides=nc(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Bs(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ht(this.dataFormat),this.activation=Uo(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Dt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=un(t.biasConstraint),this.biasRegularizer=Ft(t.biasRegularizer),this.activityRegularizer=Ft(t.activityRegularizer),this.dilationRate=nc(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new q(`dilationRate must be a number or an array of a single number for 1D convolution, but received ${JSON.stringify(this.dilationRate)}`);if(this.rank===2){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==2)throw new q(`dilationRate must be a number or array of two numbers for 2D convolution, but received ${JSON.stringify(this.dilationRate)}`)}else if(this.rank===3){if(typeof this.dilationRate=="number")this.dilationRate=[this.dilationRate,this.dilationRate,this.dilationRate];else if(this.dilationRate.length!==3)throw new q(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Fr("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!Vy(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:Vo(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 D1{constructor(e,t){super(e,t);this.kernel=null,ip.verifyArgs(t),this.filters=t.filters,xn(this.filters,"filters"),this.kernelInitializer=Dt(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=Ow(this.activation.getClassName());if(r!=null&&this.rank===2)n=jk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=mV(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=jk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=gV(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Le("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=ft(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let a=vr(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(a)}let s=[e[0]];return this.dataFormat==="channelsLast"?(s=s.concat(t),s.push(this.filters)):(s.push(this.filters),s=s.concat(t)),s}getConfig(){let e={filters:this.filters,kernelInitializer:Lt(this.kernelInitializer),kernelRegularizer:bt(this.kernelRegularizer),kernelConstraint:ln(this.kernelConstraint)},t=super.getConfig();return Object.assign(e,t),e}static verifyArgs(e){if(!("filters"in e)||typeof e.filters!="number"||e.filters<1)throw new q(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},qk=class extends ip{constructor(e){super(2,e);qk.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!Vy(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)}.`)}},gm=qk;gm.className="Conv2D";ce.registerClass(gm);var Xk=class extends ip{constructor(e){super(3,e);Xk.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)}.`)}},ym=Xk;ym.className="Conv3D";ce.registerClass(ym);var F1=class extends gm{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=iy(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=tt(g,[0,3,1,2])),this.bias!=null&&(g=Ar(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=ft(e);let t=e.slice(),n,s,r;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3):(n=3,s=1,r=2);let a=this.kernelSize[0],o=this.kernelSize[1],i=this.strides[0],l=this.strides[1];return t[n]=this.filters,t[s]=zr(t[s],i,a,this.padding),t[r]=zr(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};F1.className="Conv2DTranspose";ce.registerClass(F1);var P1=class extends ym{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=Tv(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=tt(w,[0,4,1,2,3])),this.bias!==null&&(w=Ar(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=ft(e);let t=e.slice(),n,s,r,a;this.dataFormat==="channelsFirst"?(n=1,s=2,r=3,a=4):(n=4,s=1,r=2,a=3);let o=this.kernelSize[0],i=this.kernelSize[1],l=this.kernelSize[2],c=this.strides[0],u=this.strides[1],d=this.strides[2];return t[n]=this.filters,t[s]=zr(t[s],c,o,this.padding),t[r]=zr(t[r],u,i,this.padding),t[a]=zr(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};P1.className="Conv3DTranspose";ce.registerClass(P1);var Kk=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 ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Dt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Ft(t.depthwiseRegularizer),this.depthwiseConstraint=un(t.depthwiseConstraint),this.pointwiseInitializer=Dt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Ft(t.pointwiseRegularizer),this.pointwiseConstraint=un(t.pointwiseConstraint)}build(e){if(e=ft(e),e.length<this.rank+2)throw new q(`Inputs to SeparableConv${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=Xv(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ar(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=tt(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Lt(this.depthwiseInitializer),e.pointwiseInitializer=Lt(this.pointwiseInitializer),e.depthwiseRegularizer=bt(this.depthwiseRegularizer),e.pointwiseRegularizer=bt(this.pointwiseRegularizer),e.depthwiseConstraint=ln(this.depthwiseConstraint),e.pointwiseConstraint=ln(this.pointwiseConstraint),e}};Kk.className="SeparableConv";var O1=class extends Kk{constructor(e){super(2,e)}};O1.className="SeparableConv2D";ce.registerClass(O1);var Zk=class extends ip{constructor(e){super(1,e);Zk.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"&&!Vy(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)}.`)}},M1=Zk;M1.className="Conv1D";ce.registerClass(M1);var z1=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=qf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return qf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=qf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return qf(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}};z1.className="Cropping2D";ce.registerClass(z1);var L1=class extends st{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,_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}};L1.className="UpSampling2D";ce.registerClass(L1);function yV(e,t,n=[1,1],s="valid",r,a){return K(()=>{r==null&&(r=mr()),Ht(r);let o=$1(e,r);if(e.rank!==4)throw new q(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new q(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Bd(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=tt(o,[0,3,1,2])),o})}var B1=class extends D1{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Dt(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=yV(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ar(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=vr(t,this.kernelSize[0],this.padding,this.strides[0]),a=vr(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],s,r,a]:[e[0],r,a,s]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Lt(this.depthwiseInitializer),e.depthwiseRegularizer=bt(this.depthwiseRegularizer),e.depthwiseConstraint=ln(this.depthwiseRegularizer),e}};B1.className="DepthwiseConv2D";ce.registerClass(B1);function Yk(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 Jk(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(yr(2,l));if(t=tt(t,c),a!=null)throw new Le("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=me(me(r,"bool"),"float32"),r.rank===l-1&&(r=Zt(r,-1)),r=tt(r,c)),s&&(t=Ls(t,0),r!=null&&(r=Ls(r,0)));let u=[],d,p=n,h=t.shape[0],f=os(t),m;r!=null&&(m=os(r));for(let y=0;y<h;++y){let x=f[y],A=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 Qk=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 bm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new q("The RNN cell should have an attribute `stateSize` (tuple of integers, one integer per RNN state).");this.cell=t,this.returnSequences=e.returnSequences==null?!1:e.returnSequences,this.returnState=e.returnState==null?!1:e.returnState,this.goBackwards=e.goBackwards==null?!1:e.goBackwards,this._stateful=e.stateful==null?!1:e.stateful,this.unroll=e.unroll==null?!1:e.unroll,this.supportsMasking=!0,this.inputSpec=[new Jt({ndim:3})],this.stateSpec=null,this.states_=null,this.numConstants=null,this.keptStates=[]}getStates(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;return yr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){a1(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.");a1(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=>Gt([n,s])):this.states_=[Gt([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=>Gt([n,s])):this.states_[0]=Gt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):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=Yk(e,n,s,this.numConstants);e=r.inputs,n=r.initialState,s=r.constants;let a=[],o=[];if(n!=null){t.initialState=n,a=a.concat(n),this.stateSpec=[];for(let l of n)this.stateSpec.push(new Jt({shape:l.shape}));o=o.concat(this.stateSpec)}if(s!=null&&(t.constants=s,a=a.concat(s),this.numConstants=s.length),a[0]instanceof xr){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return 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=Jk((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=Gt(e.shape);return t=we(t,[1,2]),t=Qd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?Zy(t,[1,n]):t):this.cell.stateSize>1?[Zy(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()===Qk.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=br(s,n);return new e(Object.assign(t,{cell:r}))}},ra=Qk;ra.className="RNN";ce.registerClass(ra);var lp=class extends st{},Am=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=Uo(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=un(e.kernelConstraint),this.recurrentConstraint=un(e.recurrentConstraint),this.biasConstraint=un(e.biasConstraint),this.dropout=Ju([1,Bo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ju([1,Bo([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=Go({ones:()=>zs(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Go({ones:()=>zs(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=Pr(L(e,a),this.kernel.read()):r=Pr(e,this.kernel.read()),this.bias!=null&&(r=Ar(r,this.bias.read())),o!=null&&(n=L(n,o));let i=ue(r,Pr(n,this.recurrentKernel.read()));return this.activation!=null&&(i=this.activation.apply(i)),[i,i]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Vo(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}}};Am.className="SimpleRNNCell";ce.registerClass(Am);var W1=class extends ra{constructor(e){e.cell=new Am(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)}};W1.className="SimpleRNN";ce.registerClass(W1);var xm=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=Uo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Uo(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=un(e.kernelConstraint),this.recurrentConstraint=un(e.recurrentConstraint),this.biasConstraint=un(e.biasConstraint),this.dropout=Ju([1,Bo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ju([1,Bo([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=Go({ones:()=>zs(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Go({ones:()=>zs(s),rate:this.recurrentDropout,training:n,count:3,dropoutFunc:this.dropoutFunc}));let r=this.dropoutMask,a=this.recurrentDropoutMask,o,i,l;0<this.dropout&&this.dropout<1&&(e=L(e,r[0]));let c=Pr(e,this.kernel.read());this.useBias&&(c=Ar(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,a[0]));let u=this.recurrentKernel.read(),[d,p]=Yt(u,[2*this.units,this.units],u.rank-1),h=Pr(s,d),[f,m,g]=Yt(c,3,c.rank-1),[y,x]=Yt(h,2,h.rank-1);o=this.recurrentActivation.apply(ue(f,y)),i=this.recurrentActivation.apply(ue(m,x));let A=Pr(L(i,s),p);l=this.activation.apply(ue(g,A));let b=ue(L(o,s),L(ue(1,zt(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Vo(this.activation),recurrentActivation:Vo(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}}};xm.className="GRUCell";ce.registerClass(xm);var V1=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 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 t.implmentation===0&&(t.implementation=1),new e(t)}};V1.className="GRU";ce.registerClass(V1);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=Uo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Uo(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Dt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Dt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Ft(e.kernelRegularizer),this.recurrentRegularizer=Ft(e.recurrentRegularizer),this.biasRegularizer=Ft(e.biasRegularizer),this.kernelConstraint=un(e.kernelConstraint),this.recurrentConstraint=un(e.recurrentConstraint),this.biasConstraint=un(e.biasConstraint),this.dropout=Ju([1,Bo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ju([1,Bo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=ft(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let s;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,a=this.units;s=new(t=class extends nr{apply(o,i){let l=r.apply([a]),c=new Kf().apply([a]),u=r.apply([a*2]);return Hw(Hw(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=Go({ones:()=>zs(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Go({ones:()=>zs(s),rate:this.recurrentDropout,training:n,count:4,dropoutFunc:this.dropoutFunc}));let a=this.dropoutMask,o=this.recurrentDropoutMask,i,l,c,u;0<this.dropout&&this.dropout<1&&(e=L(e,a[0]));let d=Pr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,o[0])),d=ue(d,Pr(s,this.recurrentKernel.read())),this.useBias&&(d=Ar(d,this.bias.read()));let[p,h,f,m]=Yt(d,4,d.rank-1);i=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(h),c=ue(L(l,r),L(i,this.activation.apply(f))),u=this.recurrentActivation.apply(m);let g=L(u,this.activation.apply(c));return[g,g,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Vo(this.activation),recurrentActivation:Vo(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 U1=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)}};U1.className="LSTM";ce.registerClass(U1);var bm=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){a1(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Al(`RNNCell_${s}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=r=>({className:r.getClassName(),config:r.getConfig()}),s={cells:this.cells.map(t)};return{...e,...s}}static fromConfig(e,t,n={}){let s=[];for(let r of t.cells)s.push(br(r,n));return new e({cells:s})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return o1(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]])}i1(t)}};bm.className="StackedRNNCells";ce.registerClass(bm);function Go(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):qw(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 eS=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=Gt(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(()=>Gt(r)):this.states_=[Gt(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(()=>Gt(r)):this.states_[0]=Gt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new q(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):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=vr(l,s[0],r,a[0],o[0]),d=vr(c,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,u,d]:[u,d,n]]}};eS.className="ConvRNN2D";var vm=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=nc(n,2,"kernelSize"),this.kernelSize.forEach(i=>xn(i,"kernelSize")),this.strides=nc(s||1,2,"strides"),this.strides.forEach(i=>xn(i,"strides")),this.padding=r||"valid",Bs(this.padding),this.dataFormat=a||"channelsLast",Ht(this.dataFormat),this.dilationRate=nc(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>xn(i,"dilationRate"))}build(e){var t;e=ft(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new q(`The channel dimension of the input should be defined. Found ${e[n]}`);let s=e[n],r=4,a=this.kernelSize.concat([s,this.filters*r]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let o=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",o,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let i;if(this.unitForgetBias){let l=this.biasInitializer,c=this.filters;i=new(t=class extends nr{apply(u,d){let p=l.apply([c]),h=bs([c]),f=l.apply([c*2]);return Ky([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 ${e.length}.`);let n=t.training||!1,s=e[0],r=e[1],a=e[2],o=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Go({ones:()=>zs(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(W,Q,ne)=>!Q||!Q[ne]?W:L(Q[ne],W),c=l(s,i,0),u=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Go({ones:()=>zs(r),rate:this.recurrentDropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let h=this.recurrentDropoutMask,f=l(r,h,0),m=l(r,h,1),g=l(r,h,2),y=l(r,h,3),x=3,[A,b,w,k]=Yt(this.kernel.read(),o,x),[I,E,R,P]=this.useBias?Yt(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,A,I,this.padding),u=this.inputConv(u,b,E,this.padding),d=this.inputConv(d,w,R,this.padding),p=this.inputConv(p,k,P,this.padding);let[$,_,T,O]=Yt(this.recurrentKernel.read(),o,x);f=this.recurrentConv(f,$),m=this.recurrentConv(m,_),g=this.recurrentConv(g,T),y=this.recurrentConv(y,O);let V=this.recurrentActivation.apply(ue(c,f)),X=this.recurrentActivation.apply(ue(u,m)),z=ue(L(X,a),L(V,this.activation.apply(ue(d,g)))),j=L(this.recurrentActivation.apply(ue(p,y)),this.activation.apply(z));return[j,j,z]})}getConfig(){let{units:e,...t}=super.getConfig(),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return{...t,...n}}inputConv(e,t,n,s){let r=Do(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ar(r,n,this.dataFormat):r}recurrentConv(e,t){return Do(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};vm.className="ConvLSTM2DCell";ce.registerClass(vm);var G1=class extends eS{constructor(e){let t=new vm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};G1.className="ConvLSTM2D";ce.registerClass(G1);var wm=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(()=>qw(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()}};wm.className="Dropout";ce.registerClass(wm);var H1=class extends wm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};H1.className="SpatialDropout1D";ce.registerClass(H1);var j1=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=Uo(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Dt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Dt(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=Ow(this.activation.getClassName()),r;return s!=null?r=Pr(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Pr(n,this.kernel.read()),this.bias!=null&&(r=Ar(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:Vo(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}};j1.className="Dense";ce.registerClass(j1);var q1=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],Lo(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 MB(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};q1.className="Flatten";ce.registerClass(q1);var X1=class extends st{constructor(e){super(e);this.supportsMasking=!0,this.activation=Uo(e.activation)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=Ve(e);return this.activation.apply(n)})}getConfig(){let e={activation:Vo(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};X1.className="Activation";ce.registerClass(X1);var K1=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),PB(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};K1.className="RepeatVector";ce.registerClass(K1);var Z1=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=Lo(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}};Z1.className="Reshape";ce.registerClass(Z1);var Y1=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=yr(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}};Y1.className="Permute";ce.registerClass(Y1);var J1=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 gf(ju(n,this.maskValue),s)}call(e,t){return K(()=>{this.invokeCallHook(e,t);let n=Ve(e),s=-1,r=!0,a=gf(ju(n,this.maskValue),s,r);return L(n,me(a,n.dtype))})}};J1.className="Masking";ce.registerClass(J1);var Q1=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=Dt(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),ju(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=jf(n,"int32"));let s=jw(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}};Q1.className="Embedding";ce.registerClass(Q1);var kl=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=zo(t),t.length>1)throw new q(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&zo(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return K(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=Bo(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=Qd(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let c=i.shape,u=c[0],d=c.slice(1).concat([u]),p=H(i,[u].concat(Lo(c.slice(1))));p=tt(p,[1,0]),p=H(p,d),n.push(p),r=!0}else if(l>1){let c=yr(1,l).concat([0]);n.push(tt(i,c)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,c=i[l-1],u=[c].concat(i.slice(0,i.length-1));a=H(tt(H(a,[-1,c]),[1,0]),u)}else if(o>1){let i=[o-1].concat(yr(0,o-1));a=tt(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=zo(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return K(()=>{if(t==null)return null;if(!Array.isArray(t))throw new q("`mask` should be an Array");if(!Array.isArray(e))throw new q("`inputs` should be an Array");if(t.length!==e.length)throw new q(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${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=hr(n,t[s]);return n})}},eA=class extends kl{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})}};eA.className="Add";ce.registerClass(eA);var tA=class extends kl{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})}};tA.className="Multiply";ce.registerClass(tA);var nA=class extends kl{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)})}};nA.className="Average";ce.registerClass(nA);var sA=class extends kl{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})}};sA.className="Maximum";ce.registerClass(sA);var rA=class extends kl{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})}};rA.className="Minimum";ce.registerClass(rA);var aA=class extends kl{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(()=>Ky(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(me(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 ey(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};aA.className="Concatenate";ce.registerClass(aA);function cp(e,t){for(;e<0;)e+=t;return e}function AV(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 oA=class extends kl{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=im(t,s[0]),n=im(n,s[1])),AV(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}};oA.className="Dot";ce.registerClass(oA);var iA=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(Xf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};iA.className="GaussianNoise";ce.registerClass(iA);var lA=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,Xf(n.shape,1,r))},()=>n,t.training||!1):n})}};lA.className="GaussianDropout";ce.registerClass(lA);var uA=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=ul(qu(n),this.rate);l=jf(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})}};uA.className="AlphaDropout";ce.registerClass(uA);function dp(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=Av(e,t,n,s,r,a);else if(e.rank===3)o=xv(e,t,n,s,r,a);else if(e.rank===4)o=bv(e,t,n,s,r,a);else throw new Le(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function xV(e,t,n,s,r=.001){return K(()=>{let a=Tf(e,s),o=a.mean,i=a.variance;return[dp(e,o,i,n,t,r),o,i]})}function bV(e,t,n,s,r=.001){return K(()=>{let a=Tf(e,s),o=a.mean,i=a.variance,l=[];for(let f of yr(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 vV(e,t,n,s,r=.001){return v.arraysEqual(s.slice().sort(),yr(0,e.rank-1))?xV(e,t,n,s,r):bV(e,t,n,s,r)}var cA=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=Dt(e.betaInitializer||"zeros"),this.gammaInitializer=Dt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Dt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Dt(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=yr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=ml(1,a);l[i]=r[i];let c=o.slice();c.sort();let u=!v.arraysEqual(c,yr(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]=vV(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}};cA.className="BatchNormalization";ce.registerClass(cA);var dA=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=Dt(e.betaInitializer||"zeros"),this.gammaInitializer=Dt(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!==zo(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}=Tf(n,this.axis,a),l=ml(1,r);for(let f of this.axis)l[f]=s[f];let c=f=>f!=null&&f.shape.length!==r?H(f,l):f,u=c(this.gamma.read()),d=c(this.beta.read()),p=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(p.push(s[f]),h.push(1)):(p.push(1),h.push(s[f]));return o=Js(o,p),i=Js(i,p),u=Js(u,h),d=Js(d,h),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}};dA.className="LayerNormalization";ce.registerClass(dA);function wV(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=mr()),n!=="channelsLast"&&n!=="channelsFirst")throw new q(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Qs(e,s)})}var pA=class extends st{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?mr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new q(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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(()=>wV(Ve(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};pA.className="ZeroPadding2D";ce.registerClass(pA);function km(e,t,n,s,r,a){return K(()=>{Ht(r),Bw(a),Bs(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=mr()),a==null&&(a="max"),e=$1(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=Cf(e,t,n,i):o=Af(e,t,n,i),r==="channelsFirst"&&(o=tt(o,[0,3,1,2])),o})}function tS(e,t,n,s,r,a){return K(()=>{Ht(r),Bw(a),Bs(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=mr()),a==null&&(a="max"),e=Hk(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=yy(e,t,n,i):o=sy(e,t,n,i),r==="channelsFirst"&&(o=tt(o,[0,4,1,2,3])),o})}var nS=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=2);super(e);if(typeof e.poolSize=="number")this.poolSize=[e.poolSize];else if(Array.isArray(e.poolSize)&&e.poolSize.length===1&&typeof e.poolSize[0]=="number")this.poolSize=e.poolSize;else throw new q(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(xn(this.poolSize,"poolSize"),e.strides==null)this.strides=this.poolSize;else if(typeof e.strides=="number")this.strides=[e.strides];else if(Array.isArray(e.strides)&&e.strides.length===1&&typeof e.strides[0]=="number")this.strides=e.strides;else throw new q(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);xn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Bs(this.padding),this.inputSpec=[new Jt({ndim:3})]}computeOutputShape(e){e=ft(e);let t=vr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return 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}},hA=class extends nS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),Bs(s),km(e,t,n,s,r,"max")}};hA.className="MaxPooling1D";ce.registerClass(hA);var fA=class extends nS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),Bs(s),km(e,t,n,s,r,"avg")}};fA.className="AveragePooling1D";ce.registerClass(fA);var sS=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==2)throw new q(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];xn(this.poolSize,"poolSize"),xn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),Bs(this.padding),this.inputSpec=[new Jt({ndim:4})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=vr(t,this.poolSize[0],this.padding,this.strides[0]),n=vr(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return 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}},mA=class extends sS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),Bs(s),km(e,t,n,s,r,"max")}};mA.className="MaxPooling2D";ce.registerClass(mA);var gA=class extends sS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),Bs(s),km(e,t,n,s,r,"avg")}};gA.className="AveragePooling2D";ce.registerClass(gA);var rS=class extends st{constructor(e){e.poolSize==null&&(e.poolSize=[2,2,2]);super(e);if(this.poolSize=Array.isArray(e.poolSize)?e.poolSize:[e.poolSize,e.poolSize,e.poolSize],e.strides==null)this.strides=this.poolSize;else if(Array.isArray(e.strides)){if(e.strides.length!==3)throw new q(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];xn(this.poolSize,"poolSize"),xn(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),Bs(this.padding),this.inputSpec=[new Jt({ndim:5})]}computeOutputShape(e){e=ft(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],s=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=vr(t,this.poolSize[0],this.padding,this.strides[0]),n=vr(n,this.poolSize[1],this.padding,this.strides[1]),s=vr(s,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,s]:[e[0],t,n,s,e[4]]}call(e,t){return 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}},yA=class extends rS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),Bs(s),tS(e,t,n,s,r,"max")}};yA.className="MaxPooling3D";ce.registerClass(yA);var AA=class extends rS{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),Bs(s),tS(e,t,n,s,r,"avg")}};AA.className="AveragePooling3D";ce.registerClass(AA);var aS=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}},xA=class extends aS{constructor(e){super(e||{})}call(e,t){return K(()=>{let n=Ve(e);return Ut(n,1)})}};xA.className="GlobalAveragePooling1D";ce.registerClass(xA);var bA=class extends aS{constructor(e){super(e||{})}call(e,t){return K(()=>{let n=Ve(e);return An(n,1)})}};bA.className="GlobalMaxPooling1D";ce.registerClass(bA);var oS=class extends st{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),this.inputSpec=[new Jt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Le}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},vA=class extends oS{call(e,t){return K(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?Ut(n,[1,2]):Ut(n,[2,3])})}};vA.className="GlobalAveragePooling2D";ce.registerClass(vA);var wA=class extends oS{call(e,t){return K(()=>{let n=Ve(e);return this.dataFormat==="channelsLast"?An(n,[1,2]):An(n,[2,3])})}};wA.className="GlobalMaxPooling2D";ce.registerClass(wA);var iS=class extends st{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let s=t.layer,r=br(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},kA=class extends iS{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),Jk((a,o)=>[Ve(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};kA.className="TimeDistributed";ce.registerClass(kA);function kV(e){yl(RB,"BidirectionalMergeMode",e)}var SV="concat",SA=class extends iS{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=br(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=br(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?SV:e.mergeMode,kV(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=Yk(e,n,s,this.numConstants);if(e=r.inputs,n=r.initialState,s=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&s==null)return super.apply(e,t);let a=[],o=[];if(n!=null){let l=n.length;if(l%2>0)throw new q("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,a.push(...n);let c=n.map(u=>new Jt({shape:u.shape}));this.forwardLayer.stateSpec=c.slice(0,l/2),this.backwardLayer.stateSpec=c.slice(l/2),o.push(...c)}if(s!=null)throw new Le("Support for constants in Bidirectional layers is not implemented yet.");let i=a[0]instanceof xr;for(let l of a)if(l instanceof xr!==i)throw new q("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(i){let l=[e].concat(a),c=this.inputSpec.concat(o),u=this.inputSpec;this.inputSpec=c;let d=super.apply(l,t);return this.inputSpec=u,d}else return super.apply(e,t)}call(e,t){return 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=Ky([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){Al(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Al(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let r=this.forwardLayer.states.map(a=>null);return Array.isArray(n)?n.concat(r).concat(r):[n].concat(r).concat(r)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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TypeError(`Node type ${e.op} is not implemented`)}};function sr(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){v.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let s=0;s<e.length;s++){let r=e[s],a=t[s];v.assert(r<0||a<0||r===a,()=>n+` Shapes ${e} and ${t} must match`)}}}function US(e){return!(typeof e=="number"||e.some(t=>t<0))}function pp(e,t,n){let s=BA(e,n),r=!US(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=BA(a.shape,s)}),!US(s))throw new Error(`Non-fully-defined elementShape: ${s}`);return s}function BA(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let s=0;s<e.length;++s){let r=e[s],a=t[s];if(r>=0&&a>=0&&r!==a)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[s]=r>=0?r:a}return n}var 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e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
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because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),sr(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,yn(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,s)=>this.write(n,t[s]))}gather(e,t){if(!!t&&t!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but gather requested dtype ${t}`);if(e)e=e.slice(0,this.size());else{e=[];for(let s=0;s<this.size();s++)e.push(s)}if(e.length===0)return pt([],[0].concat(this.elementShape));let n=this.readMany(e);return sr(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),an(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return pt([],[0].concat(this.elementShape));let t=[];for(let s=0;s<this.size();s++)t.push(s);let n=this.readMany(t);return sr(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),St(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,os(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,s=e.map(i=>(n+=i,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
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tensor.shape[0], but sum of lengths is
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${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,a=[];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}`);sr(t,r.shape,"TensorList shape mismatch: "),yn(r)}),this.idTensor=Ce(0),this.maxNumElements=s,yn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new 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.`);sr(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 sr(s.shape,e,"TensorList shape mismatch: "),H(s,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(sr(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");yn(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);sr(this.tensors[e].shape,t,"TensorList shape mismatch: ");let s=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.`);sr(this.elementShape,t.shape,"TensorList shape mismatch: "),yn(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);sr(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let s=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}`);sr(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 EG(e,t,n){let s=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);sr(r,t,"TensorList shape mismatch: ");let a=os(e);return new hp(a,t,s)}function RG(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=os(e,0);return t.forEach((i,l)=>{a.setItem(i,o[l])}),a}function $G(e,t,n){let s=0,r=t.map(u=>(s+=u,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
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tensor.shape[0], but sum of lengths is
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${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),o=BA(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 DG=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`)}},KG=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=zf.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}=zf.stringSplit(S("input",e,t,n),S("delimiter",e,t,n),S("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[zf.stringToHashBucketFast(S("input",e,t,n),S("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},ZG=(e,t,n)=>{switch(e.op){case"Cast":return[me(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[Gv(S("x",e,t,n),S("padding",e,t,n),S("mode",e,t,n))];case"PadV2":case"Pad":return[Qs(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[Nf(S("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=S("blockShape",e,t,n),r=S("crops",e,t,n);return[xf(S("x",e,t,n),s,r)]}case"DepthToSpace":{let s=S("blockSize",e,t,n),r=S("dataFormat",e,t,n).toUpperCase();return[Ev(S("x",e,t,n),s,r)]}case"BroadcastTo":return[Ld(S("x",e,t,n),S("shape",e,t,n))];case"BroadcastArgs":return[vv(S("s0",e,t,n),S("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function HS(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return K(()=>CG(a,o,i));case"basic_math":return K(()=>TG(a,o,i));case"control":return DG(a,o,i);case"convolution":return K(()=>FG(a,o,i));case"creation":return K(()=>PG(a,o,i));case"dynamic":return OG(a,o,i);case"evaluation":return K(()=>MG(a,o,i));case"image":return K(()=>WG(a,o,i));case"graph":return K(()=>zG(a,o,i));case"logical":return K(()=>VG(a,o,i));case"matrices":return K(()=>UG(a,o,i));case"normalization":return K(()=>GG(a,o,i));case"reduction":return K(()=>HG(a,o,i));case"slice_join":return K(()=>jG(a,o,i));case"sparse":return K(()=>qG(a,o,i));case"spectral":return K(()=>XG(a,o,i));case"string":return K(()=>KG(a,o,i));case"transformation":return K(()=>ZG(a,o,i));case"hash_table":return BG(a,o,i,s);case"custom":let l=xS(a.op);if(l&&l.customExecutor)return l.customExecutor(new IG(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 jS=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 qS(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((XS(p)||tH(p)||nH(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 YG(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 JG=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],QG=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],eH=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function XS(e){return JG.indexOf(e.op)>=0}function tH(e){return QG.indexOf(e.op)>=0}function nH(e){return eH.indexOf(e.op)>=0}var VA=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 VA(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=qS(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 YG(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 jS(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=HS(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=>Vn(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=rG(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 jS(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(c=>Vn(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}=qS(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=>!XS(x)&&!Vn(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}]. ${x}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let d="";if(u.node.op==="Enter"&&S("isConstant",u.node,s,n)&&([d]=Lr(u.node.name,n)),s[u.node.name]==null){let p=HS(u.node,s,n,this._resourceManager);d||([d]=Lr(u.node.name,n));let h=n.currentContext;v.isPromise(p)?c.push(p.then(f=>(s[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l),f))):(s[d]=p,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l))}else this.processChildNodes(u.node,t,n,s,r,l)}return c}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Lr(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Vn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Vn(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=vs(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${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 '${t}' is not found in the graph`)})}},sH=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},rH="?tfjs-format=file",aH="model.json",KS=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new sH}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=ss.browserHTTPRequest(e,this.loadOptions);else{let t=ss.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(ss.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let s=ss.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new VA(LS.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=LS.Instance.transformGraph(e.modelInitializer);this.initializer=new VA(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=ss.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof et)&&!Array.isArray(e))return 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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 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this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new IH(this,e)}filter(e){return new kH(this,e)}map(e){return new SH(this,e)}mapAsync(e){return new r7(this,e)}serialMapAsync(e){return new r7(this,e).serial()}flatmap(e){return new CH(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 wH(this,e,t)}columnMajorBatch(e,t=!0,n=JS){return this.rowMajorBatch(e,t).map(r=>cH(r,n))}concatenate(e,t){return new a7(s7([this,e]),t)}take(e){return e<0||e==null?this:new vH(this,e)}skip(e){return e<0||e==null?this:new bH(this,e)}prefetch(e){return new o7(this,e)}shuffle(e,t){return new NH(this,e,t)}serial(){return new xH(this)}},yH=class extends bn{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let 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this.upstream.next()}},vH=class extends bn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},wH=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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},kH=class extends bn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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)}}},SH=class extends bn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=this.transform(e.value),s=dr.getTensorsInContainer(n);for(let r of t)dr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},IH=class extends bn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},r7=class extends bn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=dr.getTensorsInContainer(e.value),n=await this.transform(e.value),s=dr.getTensorsInContainer(n);for(let r of t)dr.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},GA=class extends bn{constructor(){super();this.outputQueue=new n7,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},CH=class extends GA{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=dr.getTensorsInContainer(e.value),n=this.transform(e.value),s=dr.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)dr.isTensorInList(r,s)||r.dispose();return!0}},a7=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}},HA=(e=>(e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST",e))(HA||{}),TH=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 QS(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 ${this.count}.`);case 1:return{value:null,done:!0};case 2:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},o7=class extends bn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new e7(t)}summary(){return`${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()}},NH=class extends o7{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=lH.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}}},rc=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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${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),ws(async()=>(await n.iterator()).columnMajorBatch(e,t,_H),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,ws(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,ws(async()=>(await t.iterator()).filter(s=>K(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return ws(async()=>(await t.iterator()).map(n=>K(()=>e(n))),this.size)}mapAsync(e){let t=this;return ws(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return ws(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,ws(async()=>{let s=UA(async()=>({value:await t.iterator(),done:!1}));return mH(s.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,ws(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let s=this,r=iH.alea(t||v.now().toString());return ws(async()=>{let a=r.int32();return n&&(a+=r.int32()),(await s.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,ws(async()=>(await t.iterator()).take(e),n)}async toArray(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===1/0)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};rc.MAX_BUFFER_SIZE=1e4;function ws(e,t=null){return new class extends rc{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function EH(e){return ws(async()=>s7(e),e.length)}function RH(e){if(!sc(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return ws(async()=>{let n=await QS(e,s=>{if(s instanceof rc)return{value:s.iterator(),recurse:!1};if(sc(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return gH(n,HA.SHORTEST)},t)}function _H(e){if(e===null)return null;let t=e[0];return dH(t)?{value:$H(e),recurse:!1}:{value:null,recurse:!0}}function $H(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 i7=class extends rc{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
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`).map(s=>(s.endsWith("\r")&&(s=s.slice(0,-1)),s))}},Nm='"',fp=Symbol("out"),l7=Symbol("field"),Em=Symbol("quote"),jA=Symbol("quoteafterquote"),u7=Symbol("quoteinquote"),c7=class extends rc{constructor(e,t){super();this.input=e,this.hasHeader=!0,this.fullColumnNames=null,this.columnNamesValidated=!1,this.columnConfigs=null,this.configuredColumnsOnly=!1,this.delimiter=",",this.delimWhitespace=!1,this.base=new i7(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 Nm:s=o+1,a=Em;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=fp;break;default:a=l7,s=o;break}break;case l7:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=fp,s=o+1;break;default:}break;case Em:switch(e.charAt(o)){case Nm: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 Nm:a=Em;break;default:a=u7;break}break;case u7:switch(e.charAt(o)){case Nm:a=Em;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}},d7=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 d7(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)}},p7=class extends bn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Ct([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,s=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,a=(1-s)/2,o=r+n,i=s+a;this.cropBox=fr([a,r,i,o],[1,4])}else this.cropBox=fr([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(Y().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new p7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Ys.fromPixels(this.webcamVideoElement)}catch(t){throw new Error(`Error thrown converting video to pixels: ${JSON.stringify(t)}`)}if(this.resize)try{return{value:this.cropAndResizeFrame(e),done:!1}}catch(t){throw new Error(`Error thrown cropping the video: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return K(()=>{let t=Zt(me(e,"float32"),0),n;n=Se.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return H(n,s.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(t=>t.stop());try{this.webcamVideoElement.srcObject=null}catch(t){console.log(t),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},h7=class{},f7=class extends bn{split(e){return new DH(this,e)}},DH=class extends f7{constructor(e,t){super();this.upstream=e,this.impl=new FH(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},FH=class extends GA{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},PH=class extends bn{decodeUTF8(){return new OH(this)}},OH=class extends f7{constructor(e){super();this.upstream=e,this.impl=new MH(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},MH=class extends GA{constructor(e){super();if(this.upstream=e,Y().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=L5();this.decoder=new t("utf8")}}summary(){return`${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}},m7=class extends PH{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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function zH(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=LH(e));let a=await(n||v.fetch)(s,r);if(a.ok){let o=new Uint8Array(await a.arrayBuffer());return new m7(o,t)}else throw new Error(a.statusText)}var LH=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function g7(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var y7=class extends h7{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(g7(this.input)&&Y().get("IS_NODE")){let e=Ih();this.input=e.readFileSync(this.input.substr(7))}return new m7(this.input,this.options)}},A7=class extends h7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return g7(this.url)?new y7(this.url,this.fileOptions).iterator():zH(this.url,this.fileOptions)}};function BH(e,t={}){return new c7(new A7(e),t)}function WH(e){let t=UA(e);return ws(async()=>t)}function VH(e){return ws(async()=>{let t=await e();return UA(()=>t.next())})}async function UH(e,t){return p7.create(e,t)}async function GH(e){return d7.create(e)}var HH="0.0.0";function Ee(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var jH=er.whereImpl,x7=class extends su{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new ad(this,rs())}nextDataId(){return x7.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 rs().makeTensorFromDataId(s,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=v.now();return e(),{kernelMs:v.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){Ee([e],"where");let t=this.readSync(e.dataId);return jH(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},qA=x7;qA.nextDataId=0;var Rm={};Me(Rm,{addImpl:()=>v7,bincountImpl:()=>KA,bincountReduceImpl:()=>w7,ceilImpl:()=>k7,concatImpl:()=>ZA,equalImpl:()=>S7,expImpl:()=>C7,expm1Impl:()=>N7,floorImpl:()=>E7,gatherNdImpl:()=>R7,gatherV2Impl:()=>_7,greaterEqualImpl:()=>D7,greaterImpl:()=>$7,lessEqualImpl:()=>P7,lessImpl:()=>F7,linSpaceImpl:()=>O7,logImpl:()=>M7,maxImpl:()=>z7,maximumImpl:()=>L7,minimumImpl:()=>B7,multiplyImpl:()=>YA,negImpl:()=>W7,notEqualImpl:()=>V7,prodImpl:()=>U7,rangeImpl:()=>QA,rsqrtImpl:()=>G7,sigmoidImpl:()=>Fj,simpleAbsImpl:()=>b7,sliceImpl:()=>Dm,sparseFillEmptyRowsImpl:()=>j7,sparseReshapeImpl:()=>q7,sparseSegmentReductionImpl:()=>ex,sqrtImpl:()=>Mj,squaredDifferenceImpl:()=>X7,stridedSliceImpl:()=>K7,stringNGramsImpl:()=>Z7,stringSplitImpl:()=>Y7,stringToHashBucketFastImpl:()=>J7,subImpl:()=>Q7,tileImpl:()=>eI,topKImpl:()=>nI,transposeImpl:()=>JA,uniqueImpl:()=>sI});function b7(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var qH=e=>{let{x:t}=e.inputs,n=e.backend;Ee(t,"abs");let s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return s=b7(r),n.makeOutput(s,t.shape,t.dtype)},XH={kernelName:hi,backendName:"cpu",kernelFunc:qH};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 KH={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 ZH={kernelName:Xa,backendName:"cpu",kernelFunc:Br};function Sl(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 YH={kernelName:Ad,backendName:"cpu",kernelFunc:Sl};function Ho(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=Ho({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=Sl({inputs:{input:r},backend:n}),i=Ho({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 JH={kernelName:Fa,backendName:"cpu",kernelFunc:Ho};function vn(e,t,n,s){return n==null?({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;Ee([o,i],e);let 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u=t.slice();return u[u.length-1]=s,[ze(u,n,l),ze(u,"int32",c)]}function sI(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 jj="0.0.0";il("cpu",()=>new qA,1);var rI=mt(Va,e=>e>=0?e:Math.exp(e)-1),qj={kernelName:Va,backendName:"cpu",kernelFunc:rI};function aI(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s;Ee([r],"leakyRelu");let 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eq={kernelName:Da,backendName:"cpu",kernelFunc:uI};function tq(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=uI({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=nx(n,p,u,i,d),m.push(p),p=f);for(let y of m)n.disposeIntermediateTensorInfo(y);return p}var nq={kernelName:ko,backendName:"cpu",kernelFunc:tq},sq=mt(iu,e=>Math.acos(e)),rq={kernelName:iu,backendName:"cpu",kernelFunc:sq},aq=mt(lu,e=>Math.acosh(e)),oq={kernelName:lu,backendName:"cpu",kernelFunc:aq};function iq(e){let{inputs:t,backend:n}=e,s=t;Ee(t,"addN");let r=s.map(i=>n.data.get(i.dataId).values),a=ze(s[0].shape,s[0].dtype),o=a.values;for(let i=0;i<s.length;i++){let l=r[i];for(let c=0;c<o.length;c++)o[c]+=l[c]}return n.makeTensorInfo(a.shape,a.dtype,a.values)}var lq={kernelName:Ra,backendName:"cpu",kernelFunc:iq};function 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gq={kernelName:du,backendName:"cpu",kernelFunc:mq},yq=mt(pu,e=>Math.asin(e)),Aq={kernelName:pu,backendName:"cpu",kernelFunc:yq},xq=mt(hu,e=>Math.asinh(e)),bq={kernelName:hu,backendName:"cpu",kernelFunc:xq},vq=mt(fu,e=>Math.atan(e)),wq={kernelName:fu,backendName:"cpu",kernelFunc:vq},kq=Qt((e,t)=>Math.atan2(e,t)),Sq=vn(gu,kq),Iq={kernelName:gu,backendName:"cpu",kernelFunc:Sq},Cq=mt(mu,e=>Math.atanh(e)),Tq={kernelName:mu,backendName:"cpu",kernelFunc:Cq};function sx(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 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oe=Z*l-y,re=oe;for(;re<0;)re+=d;let ge=Math.min(r.inWidth,f+oe),ve=ae+Z*E,Re=x,Te=0,Fe=0;for(let ot=V;ot<X;ot+=c){let Je=$+ot*s[1];for(let Ze=Q;Ze<ne;Ze+=u){let gt=Je+Ze*s[2];for(let it=re;it<ge;it+=d){let yt=gt+it*s[3],_t=e[yt+_];if(a==="max"&&_t>Re?Re=_t:a==="avg"&&(Te+=_t,Fe++),isNaN(Re))break}if(isNaN(Re))break}if(isNaN(Re))break}let Ue=ve+_;b[Ue]=a==="avg"?Te/Fe:Re}}}}return A}function Nq(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,$=P;for(;$<0;)$+=l;let 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u=N.computePool3DInfo(a.shape,o,i,1,l,c),d=u.strideDepth,p=u.strideHeight,h=u.strideWidth,f=u.filterDepth,m=u.filterHeight,g=u.filterWidth,y=u.dilationDepth,x=u.dilationHeight,A=u.dilationWidth,b=u.effectiveFilterDepth,w=u.effectiveFilterHeight,k=u.effectiveFilterWidth,I=b-1-u.padInfo.front,E=k-1-u.padInfo.left,R=w-1-u.padInfo.top,P=ze(a.shape,"float32"),$=1/(f*m*g),_=n.bufferSync(r);for(let T=0;T<u.batchSize;++T)for(let O=0;O<u.inChannels;++O)for(let V=0;V<u.inDepth;++V)for(let X=0;X<u.inHeight;++X)for(let z=0;z<u.inWidth;++z){let j=V-I,W=X-R,Q=z-E,ne=0;for(let ae=0;ae<b;ae+=y){let Z=(j+ae)/d;if(!(Z<0||Z>=u.outDepth||Math.floor(Z)!==Z))for(let oe=0;oe<w;oe+=x){let re=(W+oe)/p;if(!(re<0||re>=u.outHeight||Math.floor(re)!==re))for(let ge=0;ge<k;ge+=A){let ve=(Q+ge)/h;if(ve<0||ve>=u.outWidth||Math.floor(ve)!==ve)continue;ne+=_.get(T,Z,re,ve,O)}}}P.set(ne*$,T,V,X,z,O)}return n.makeTensorInfo(P.shape,P.dtype,P.values)}var Fq={kernelName:_h,backendName:"cpu",kernelFunc:Dq};function 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n.makeTensorInfo(r.shape,r.dtype,m)}var zq={kernelName:ja,backendName:"cpu",kernelFunc:Mq};function Lq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;Ee([r],"batchToSpaceND");let i=a.reduce((y,x)=>y*x),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=Pt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Ws({inputs:{x:h},backend:n,attrs:{perm:c}}),m=Pt({inputs:{x:f},backend:n,attrs:{shape:u}}),g=Il({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var Bq={kernelName:fi,backendName:"cpu",kernelFunc:Lq};function Wq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o}=s,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,c=KA(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var 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Jq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,dataFormat:l,dimRoundingMode:c,filterShape:u}=s;Ee([r,a],"conv2dBackpropFilter");let d=N.convertConv2DDataFormat(l),p=N.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),{strideHeight:h,strideWidth:f,filterHeight:m,filterWidth:g}=p,y=p.dataFormat==="channelsLast",x=new sn(p.filterShape,"float32"),A=p.padInfo.left,b=p.padInfo.top,w=n.data.get(r.dataId).values,k=n.data.get(a.dataId).values,I=new sn(r.shape,r.dtype,w),E=new sn(a.shape,a.dtype,k);for(let R=0;R<m;++R){let P=Math.max(0,Math.ceil((b-R)/h)),$=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 V=0;V<p.inChannels;++V)for(let X=0;X<p.outChannels;++X){let z=0;for(let j=0;j<p.batchSize;++j)for(let W=P;W<$;++W){let Q=R+W*h-b;for(let ne=T;ne<O;++ne){let ae=_+ne*f-A;y?z+=I.get(j,Q,ae,V)*E.get(j,W,ne,X):z+=I.get(j,V,Q,ae)*E.get(j,X,W,ne)}}x.set(z,R,_,V,X)}}}return 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pX(e){let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,[u,d,p,h]=r.shape,f=a.shape[0],[m,g]=i,y=ze([f,m,g,h],"float32"),x=n.data.get(a.dataId).values,A=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,w=v.computeStrides(r.shape),k=v.computeStrides(y.shape);for(let I=0;I<f;I++){let E=I*4,R=x[E],P=x[E+1],$=x[E+2],_=x[E+3],T=A[I];if(T>=u)continue;let O=m>1?($-R)*(d-1)/(m-1):0,V=g>1?(_-P)*(p-1)/(g-1):0;for(let X=0;X<m;X++){let z=m>1?R*(d-1)+X*O:.5*(R+$)*(d-1);if(z<0||z>d-1){for(let j=0;j<g;j++)for(let W=0;W<h;W++){let Q=W+j*k[2]+X*k[1]+I*k[0];y.values[Q]=c}continue}if(l==="bilinear"){let j=Math.floor(z),W=Math.ceil(z),Q=z-j;for(let ne=0;ne<g;ne++){let ae=g>1?P*(p-1)+ne*V:.5*(P+_)*(p-1);if(ae<0||ae>p-1){for(let ge=0;ge<h;ge++){let ve=ge+ne*k[2]+X*k[1]+I*k[0];y.values[ve]=c}continue}let Z=Math.floor(ae),oe=Math.ceil(ae),re=ae-Z;for(let ge=0;ge<h;ge++){let ve=ge+Z*w[2]+j*w[1]+T*w[0],Re=b[ve];ve=ge+oe*w[2]+j*w[1]+T*w[0];let Te=b[ve];ve=ge+Z*w[2]+W*w[1]+T*w[0];let Fe=b[ve];ve=ge+oe*w[2]+W*w[1]+T*w[0];let Ue=b[ve],ot=Re+(Te-Re)*re,Je=Fe+(Ue-Fe)*re;ve=ge+ne*k[2]+X*k[1]+I*k[0],y.values[ve]=ot+(Je-ot)*Q}}}else for(let j=0;j<g;++j){let W=g>1?P*(p-1)+j*V:.5*(P+_)*(p-1);if(W<0||W>p-1){for(let ae=0;ae<h;ae++){let Z=ae+j*k[2]+X*k[1]+I*k[0];y.values[Z]=c}continue}let Q=Math.round(W),ne=Math.round(z);for(let ae=0;ae<h;ae++){let Z=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[Z]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var hX={kernelName:yi,backendName:"cpu",kernelFunc:pX};function fX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;Ee(r,"cumsum");let l=N.getAxesPermutation([a],r.shape.length),c=r;l!=null&&(c=Ws({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=N.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let d=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 mX={kernelName:gi,backendName:"cpu",kernelFunc:fX};function gX(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=KA(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=w7(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be 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vX(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s;Ee([r,a],"depthwiseConv2dNativeBackpropFilter");let d=N.computeConv2DInfo(r.shape,u,o,i,l,c,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new sn(d.filterShape,"float32"),y=d.padInfo.left,x=d.padInfo.top,A=d.outChannels/d.inChannels,b=n.data.get(r.dataId).values,w=new sn(r.shape,r.dtype,b),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 $=0;$<m;++$){let _=Math.max(0,Math.ceil((y-$)/h)),T=Math.min(d.outWidth,(d.inWidth+y-$)/h);for(let O=0;O<d.outChannels;++O){let V=Math.trunc(O/A),X=O%A,z=0;for(let j=0;j<d.batchSize;++j)for(let W=R;W<P;++W){let Q=E+W*p-x;for(let ne=_;ne<T;++ne){let ae=$+ne*h-y;z+=w.get(j,Q,ae,V)*I.get(j,W,ne,O)}}g.set(z,E,$,V,X)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var wX={kernelName:zh,backendName:"cpu",kernelFunc:vX};function kX(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s;Ee([r,a],"depthwiseConv2DNativeBackpropInput");let d=v.computeStrides(r.shape),p=v.computeStrides(a.shape),h=N.computeConv2DInfo(u,a.shape,o,i,l,c,!0),f=new sn(h.inShape,"float32"),m=f.values,[g,y,x]=f.strides,A=n.data.get(r.dataId).values,[b,w,k]=d,I=n.data.get(a.dataId).values,[E,R,P]=p,{batchSize:$,filterHeight:_,filterWidth:T,inChannels:O,inHeight:V,inWidth:X,outChannels:z,outHeight:j,outWidth:W,strideHeight:Q,strideWidth:ne}=h,ae=_-1-h.padInfo.top,Z=T-1-h.padInfo.left,oe=z/O;for(let re=0;re<$;++re)for(let ge=0;ge<O;++ge)for(let ve=0;ve<V;++ve){let Re=ve-ae,Te=Math.max(0,Math.ceil(Re/Q)),Fe=Math.min(j,(_+Re)/Q);for(let Ue=0;Ue<X;++Ue){let ot=Ue-Z,Je=Math.max(0,Math.ceil(ot/ne)),Ze=Math.min(W,(T+ot)/ne),gt=0;for(let it=Te;it<Fe;++it){let yt=it*Q-Re;for(let _t=Je;_t<Ze;++_t){let Jn=_t*ne-ot,lt=b*re+w*it+k*_t,ms=E*(_-1-yt)+R*(T-1-Jn)+P*ge;for(let kn=0;kn<oe;++kn){let js=ge*oe+kn,Qn=A[lt+js],gs=I[ms+kn];gt+=Qn*gs}}}m[g*re+y*ve+x*Ue+ge]=gt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var SX={kernelName:Lh,backendName:"cpu",kernelFunc:kX};function IX(e){let{inputs:t,backend:n}=e,{x:s}=t,r=v.sizeFromShape(s.shape),a=n.data.get(s.dataId).values,o=ze([r,r],s.dtype),i=o.values;for(let c=0;c<a.length;c++)i[c*r+c]=a[c];let l=[...s.shape,...s.shape];return n.makeTensorInfo(l,o.dtype,o.values)}var CX={kernelName:Bh,backendName:"cpu",kernelFunc:IX},TX={kernelName:pd,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:s,filter:r}=e,{strides:a,pad:o,dilations:i}=n,l=t,c=l.data.get(s.dataId).values,u=s.shape.length,d=l.data.get(r.dataId).values,p=r.shape.length,{batchSize:h,inHeight:f,inWidth:m,inChannels:g,outHeight:y,outWidth:x,padInfo:A,strideHeight:b,strideWidth:w,filterHeight:k,filterWidth:I,dilationHeight:E,dilationWidth:R,outShape:P}=N.computeDilation2DInfo(s.shape,r.shape,a,o,"NHWC",i),$=v.sizeFromShape(P),_=P.length,T=v.getArrayFromDType(s.dtype,$);for(let V=0;V<h;++V)for(let X=0;X<y;++X){let z=X*b-A.top;for(let j=0;j<x;++j){let W=j*w-A.left;for(let Q=0;Q<g;++Q){let ne=Number.MIN_SAFE_INTEGER;for(let Z=0;Z<k;++Z){let oe=z+Z*E;if(oe>=0&&oe<f)for(let re=0;re<I;++re){let ge=W+re*R;if(ge>=0&&ge<m){let ve=v.locToIndex([V,oe,ge,Q],u,v.computeStrides(s.shape)),Re=v.locToIndex([Z,re,Q],p,v.computeStrides(r.shape)),Te=c[ve]+d[Re];Te>ne&&(ne=Te)}}}let 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n?r=s:t?r=e:r=Math.log(1+s),r}),tY={kernelName:_u,backendName:"cpu",kernelFunc:eY};function nY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;Ee([r],"spaceToBatchND");let i=v.sizeFromShape(a),l=[[0,0]];l.push(...o);for(let k=1+a.length;k<r.shape.length;++k)l.push([0,0]);let c=xI.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),u=N.getReshaped(c.shape,a,i,!1),d=N.getPermuted(u.length,a.length,!1),p=N.getReshapedPermuted(c.shape,a,i,!1),m=Pt({inputs:{x:c},backend:n,attrs:{shape:u}}),x=Ws({inputs:{x:m},backend:n,attrs:{perm:d}}),w=Pt({inputs:{x},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(x),w}var sY={kernelName:Gi,backendName:"cpu",kernelFunc:nY};function rY(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:
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|
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
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|
${o.shape}`);let i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=n.data.get(o.dataId).values[0],[d,p,h,f,m]=j7(i,s.shape,s.dtype,l,r.dtype,c,u);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var aY={kernelName:xd,backendName:"cpu",kernelFunc:rY};function oY(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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${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 ${a.shape}`);let o=Array.from(n.data.get(r.dataId).values),i=n.data.get(s.dataId).values,l=Array.from(n.data.get(a.dataId).values),[c,u,d]=q7(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var iY={kernelName:$u,backendName:"cpu",kernelFunc:oY};function lY(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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|
${a.shape}`);if(r.shape[0]!==a.shape[0])throw new Error("segmentIds and indices should have same size.");let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[c,u]=ex(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var uY={kernelName:bd,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]=ex(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var dY={kernelName:vd,backendName:"cpu",kernelFunc:cY};function pY(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=bI(f,m,i,p,u,c,l,d,g,h);return n.makeTensorInfo(i,y.dtype,y.values)}var hY={kernelName:wd,backendName:"cpu",kernelFunc:pY};function fY(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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$Y={kernelName:Kr,backendName:"cpu",kernelFunc:_Y};function DY(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s;Ee(r,"topk");let i=n.data.get(r.dataId).values,[l,c]=nI(i,r.shape,r.dtype,a,o);return[n.makeTensorInfo(l.shape,l.dtype,l.values),n.makeTensorInfo(c.shape,c.dtype,c.values)]}var FY={kernelName:Xi,backendName:"cpu",kernelFunc:DY};function PY(e){let{inputs:t,attrs:n,backend:s}=e,{image:r,transforms:a}=t,{interpolation:o,fillMode:i,fillValue:l,outputShape:c}=n,[u,d,p,h]=r.shape,[f,m]=c!=null?c:[d,p],g=[u,f,m,h],y=v.computeStrides(r.shape),x=y[0],A=y[1],b=y[2],w=v.getTypedArrayFromDType(r.dtype,v.sizeFromShape(g));w.fill(l);let k=s.data.get(r.dataId).values,I=s.data.get(a.dataId).values;for(let R=0;R<u;++R){let P=a.shape[0]===1?I:I.subarray(R*8,R*8+8);for(let $=0;$<f;++$)for(let _=0;_<m;++_)for(let T=0;T<h;++T){let O,V=P[6]*_+P[7]*$+1;if(V===0)continue;let 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De=Y();De.registerFlag("HAS_WEBGL",()=>De.getNumber("WEBGL_VERSION")>0);De.registerFlag("WEBGL_VERSION",()=>px(2)?2:px(1)?1:0);De.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);De.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>De.get("WEBGL_VERSION")===2);De.registerFlag("WEBGL_CPU_FORWARD",()=>!0);De.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);De.registerFlag("WEBGL_PACK",()=>De.getBool("HAS_WEBGL"));De.registerFlag("WEBGL_PACK_NORMALIZATION",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_CLIP",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_PACK_REDUCE",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_LAZILY_UNPACK",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_CONV_IM2COL",()=>De.getBool("WEBGL_PACK"));De.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>VI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>UI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=De.getNumber("WEBGL_VERSION");return e===0?0:GI(e)});De.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>De.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Ou.isMobile());De.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>HI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>De.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:De.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));De.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>jI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_FENCE_API_ENABLED",()=>qI(De.getNumber("WEBGL_VERSION")));De.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>De.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);De.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}.`)});De.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>Ou.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});De.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);De.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);De.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);De.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function Un(){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 El(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 Hm(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}; ${i};`}).join("")}function dJ(e,t){let n=e.length,s=e.map(a=>`${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 pJ(e,t,n="index"){let s=e.map((a,o)=>o),r=dJ(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 fx(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 mx(){return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
|
|
}
|
|
`}var XI=`
|
|
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:KI}=N;function hJ(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}=gx(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?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
|
|
`),a=e.map(h=>fJ(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
|
|
`),o=t.texShape,i=Un(),l=yJ(i),c,u,d=bJ(i);return t.isPacked?(c=mJ(t.logicalShape,o,n.enableShapeUniforms),u=xJ(i)):(c=gJ(t.logicalShape,o,n.enableShapeUniforms),u=AJ(i)),n.packedInputs&&(d+=SJ),[d,l,u,r,c,a,n.userCode].join(`
|
|
`)}function cc(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return OJ(e,t);case 1:return zJ(e,t);case 2:return BJ(e,t);case 3:return VJ(e,t);case 4:return GJ(e,t);case 5:return HJ(e);case 6:return jJ(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function ZI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return PJ(e);case 1:return MJ(e,t);case 2:return LJ(e,t);case 3:return WJ(e,t);default:return UJ(e,t)}}function fJ(e,t,n=!1,s){let r="";n?r+=ZI(e,s):r+=cc(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=qJ(e,t):r+=XJ(e,t)),r}function mJ(e,t,n){switch(e.length){case 0:return YI();case 1:return IJ(e,t,n);case 2:return DJ(e,t,n);case 3:return TJ(e,t,n);default:return EJ(e,t,n)}}function gJ(e,t,n){switch(e.length){case 0:return YI();case 1:return CJ(e,t,n);case 2:return FJ(e,t,n);case 3:return NJ(e,t,n);case 4:return RJ(e,t,n);case 5:return _J(e,t);case 6:return $J(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function yJ(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function AJ(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function xJ(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function bJ(e){return`${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);
|
|
}
|
|
|
|
${vJ}
|
|
${wJ}
|
|
${kJ}
|
|
`}var vJ=`
|
|
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);
|
|
}
|
|
`,wJ=`
|
|
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);
|
|
}
|
|
`,kJ=`
|
|
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);
|
|
}
|
|
`,SJ=`
|
|
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 YI(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function IJ(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 CJ(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 * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function TJ(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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 NJ(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;
|
|
${Hm(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let s=El(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function EJ(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let c=2;c<e.length-1;c++)o*=e[e.length-c-1],i=`
|
|
int b${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 RJ(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;
|
|
${Hm(["r","c","d","d2"],e)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let s=El(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function _J(e,t){let n=El(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function $J(e,t){let n=El(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function DJ(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${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, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function FJ(e,t,n){return v.arraysEqual(e,t)?n?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${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 * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Rl(e){return`offset${e}`}function PJ(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Un();return`
|
|
vec4 ${n}() {
|
|
return ${s.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function OJ(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=Rl(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 MJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=Un();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 zJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int index) {
|
|
${dc(e)}
|
|
}
|
|
`;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
|
|
float ${s}(int index) {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let i=Rl(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 LJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=Un();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 BJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape;if(a!=null&&v.arraysEqual(n,a)){if(t)return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let p=a[0],h=a[1];return`
|
|
float ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}let{newShape:o,keptDims:i}=v.squeezeShape(n),l=o;if(l.length<n.length){let p=pc(e,l),h=["row","col"];return`
|
|
${cc(p,t)}
|
|
float ${r}(int row, int col) {
|
|
return ${r}(${hc(h,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
|
|
${dc(e)}
|
|
}
|
|
`;let c=a[0],u=a[1],d=Rl(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 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=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)];if(n[0]===1){let p=n.slice(1),h=[1,2],f=pc(e,p),m=["b","row","col"];return`
|
|
${ZI(f,t)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${hc(m,h)});
|
|
}
|
|
`}let i=Un();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 VJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[1]*n[2],o=n[2],{newShape:i,keptDims:l}=v.squeezeShape(n),c=i;if(c.length<n.length){let m=pc(e,c),g=["row","col","depth"];return`
|
|
${cc(m,t)}
|
|
float ${r}(int row, int col, int depth) {
|
|
return ${r}(${hc(g,l)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${a}, ${o}, 1)));
|
|
${dc(e)}
|
|
}
|
|
`;let u=e.shapeInfo.texShape,d=u[0],p=u[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
int stride1 = ${s}Shape[2];
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(stride1, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${o}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;if(p===o&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let f=Rl(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 UJ(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=Un();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 GJ(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=n[3],o=n[2]*a,i=n[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(n);if(l.length<n.length){let x=pc(e,l),A=["row","col","depth","depth2"];return`
|
|
${cc(x,t)}
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
return ${r}(${hc(A,c)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${o}, ${a}, 1)));
|
|
${dc(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1],f=`int stride2 = ${s}Shape[3];`,m=`int stride1 = ${s}Shape[2] * stride2;`,g=`int stride0 = ${s}Shape[1] * stride1;`;if(h===i&&u==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
${f}
|
|
${m}
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(stride1, stride2, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${o}, ${a}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;if(h===a&&u==null)return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${s}Shape[1] * ${s}Shape[2], ${s}Shape[2], 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${n[1]*n[2]}, ${n[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let y=Rl(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 HJ(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],a=t[3]*r,o=t[2]*a,i=t[1]*o,{newShape:l,keptDims:c}=v.squeezeShape(t);if(l.length<t.length){let m=pc(e,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${cc(m)}
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${s}(${hc(g,c)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${o}, ${a}, ${r})) +
|
|
depth3;
|
|
${dc(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&u==null)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${o}, ${a}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===r&&u==null)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]},
|
|
${t[2]*t[3]}, ${t[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=Rl(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 jJ(e){let t=e.shapeInfo.logicalShape,n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:a}=v.squeezeShape(t);if(r.length<t.length){let g=pc(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${cc(g)}
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${s}(${hc(y,a)});
|
|
}
|
|
`}let o=t[5],i=t[4]*o,l=t[3]*i,c=t[2]*l,u=t[1]*c;if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${u}, ${c}, ${l}, ${i})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${o}, 1)));
|
|
${dc(e)}
|
|
}
|
|
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===u&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${c}, ${l}, ${i}, ${o})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(f===o&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=Rl(n);return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${u} + col * ${c} + depth * ${l} +
|
|
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
|
|
vec2 uv = uvFromFlat(${h}, ${f}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function dc(e){let t=e.name,n=v.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function qJ(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=KI(e.shapeInfo.logicalShape,t.logicalShape),l=vt(o),c=o-a,u,d=["x","y","z","w","u","v"];a===0?u="":o<2&&i.length>=1?u="coords = 0;":u=i.map(x=>`coords.${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 XJ(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=KI(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 ${e} is not yet supported`)}function gx(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function pc(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function hc(e,t){return t.map(n=>e[n]).join(", ")}function KJ(e,t,n,s){let r=n.map((b,w)=>{let k={logicalShape:b.shape,texShape:b.isUniform?null:b.texData.texShape,isUniform:b.isUniform,isPacked:b.isUniform?!1:b.texData.isPacked,flatOffset:null};return b.texData!=null&&b.texData.slice!=null&&b.texData.slice.flatOffset>0&&(k.flatOffset=b.texData.slice.flatOffset),{name:t.variableNames[w],shapeInfo:k}}),a=r.map(b=>b.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=hJ(r,o,t),l=TI(e.gl,i),c=e.createProgram(l),u=null,d=e.getUniformLocation(c,"NAN",!1);Y().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let p=!1,h={},f={},m={};for(let b=0;b<t.variableNames.length;b++){let w=t.variableNames[b];h[w]=e.getUniformLocation(c,w,p),h[`offset${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 JI(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 ${l} must match`)})}function ZJ(e,t,n,s,r){t.program.enableShapeUniforms||(JI(t.inShapeInfos,n),JI([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),Y().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,c)=>{let u=t.program.variableNames[c],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`],h=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(h){let{uniformShape:m}=gx(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 ${l.type} is not supported yet.`)}),e.executeProgram()}function YJ(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:c,uniformShape:u,keptDims:d}=gx(e.packedInputs,o.shape,l),p="",h="",f="";if(u.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${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 JJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Mm.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Un();this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Hm(["r","c","d"],e):El(["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;
|
|
}
|
|
`}},QJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Mm.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Un();this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Hm(["r","c","d"],e):El(["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;
|
|
}
|
|
`}},eQ=class{constructor(e){this.variableNames=["A"],this.outTexUsage=rr.DOWNLOAD;let t=Un();this.outputShape=e,this.userCode=`
|
|
${XI}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},tQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=rr.DOWNLOAD;let t=Un();this.outputShape=e,this.userCode=`
|
|
${XI}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},nQ=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Un();this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${this.enableShapeUniforms?mx():fx(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.);
|
|
}
|
|
`}},sQ=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Un();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?mx():fx(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${s}
|
|
|
|
${n.output} = ${r};
|
|
}
|
|
`}},QI={};Me(QI,{bindVertexProgramAttributeStreams:()=>lC,createBufferFromOutputTexture:()=>dC,createFloat16MatrixTexture:()=>rC,createFloat16PackedMatrixTexture:()=>iC,createFloat32MatrixTexture:()=>sC,createIndexBuffer:()=>nC,createPackedMatrixTexture:()=>oC,createUnsignedBytesMatrixTexture:()=>aC,createVertexBuffer:()=>tC,createVertexShader:()=>eC,downloadByteEncodedFloatMatrixFromOutputTexture:()=>hC,downloadFloat32MatrixFromBuffer:()=>pC,downloadMatrixFromPackedOutputTexture:()=>mC,downloadPackedMatrixFromBuffer:()=>fC,getInternalFormatForFloat16MatrixTexture:()=>Ax,getInternalFormatForFloat16PackedMatrixTexture:()=>vx,getInternalFormatForFloat32MatrixTexture:()=>yx,getInternalFormatForPackedMatrixTexture:()=>bx,getInternalFormatForUnsignedBytesMatrixTexture:()=>xx,uploadDenseMatrixToTexture:()=>uC,uploadPixelDataToTexture:()=>cC});function eC(e){let t=Un(),n=`${t.version}
|
|
precision highp float;
|
|
${t.attribute} vec3 clipSpacePos;
|
|
${t.attribute} vec2 uv;
|
|
${t.varyingVs} vec2 resultUV;
|
|
|
|
void main() {
|
|
gl_Position = vec4(clipSpacePos, 1);
|
|
resultUV = uv;
|
|
}`;return CI(e,n)}function tC(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return RI(e,t)}function nC(e){let t=new Uint16Array([0,1,2,2,1,3]);return _I(e,t)}function kp(e,t,n,s,r,a){DI(t,n);let o=$I(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 yx(e){return e.internalFormatFloat}function sC(e,t,n,s){let[r,a]=xp(t,n);return kp(e,r,a,yx(s),s.textureFormatFloat,e.FLOAT)}function Ax(e){return e.internalFormatHalfFloat}function rC(e,t,n,s){let[r,a]=xp(t,n);return kp(e,r,a,Ax(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function xx(e){return e.downloadTextureFormat}function aC(e,t,n,s){let[r,a]=xp(t,n);return kp(e,r,a,xx(s),e.RGBA,e.UNSIGNED_BYTE)}function bx(e){return e.internalFormatPackedFloat}function oC(e,t,n,s){let[r,a]=lc(t,n);return kp(e,r,a,bx(s),e.RGBA,e.FLOAT)}function vx(e){return e.internalFormatPackedHalfFloat}function iC(e,t,n,s){let[r,a]=lc(t,n);return kp(e,r,a,vx(s),e.RGBA,s.textureTypeHalfFloat)}function lC(e,t,n){let s=0,r=3*4,a=3*4+2*4;return ke(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),cx(e,t,"clipSpacePos",n,3,a,s)&&cx(e,t,"uv",n,2,a,r)}function uC(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 cC(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 dC(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 pC(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 hC(e,t,n,s){let[r,a]=xp(t,n),o=4,i=new Uint8Array(JY(t*n,o));return ke(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function fC(e,t,n,s,r,a,o,i){let l=e,c=new Float32Array(QY(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 mC(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 jm=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,Om(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=bp(this.gl,r),Vs(this.gl,a))this.textureHalfFloatExtension=bp(this.gl,a);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),Vs(this.gl,s))this.colorBufferHalfFloatExtension=bp(this.gl,s);else if(Y().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",Vs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Vs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=tC(this.gl),this.indexBuffer=nC(this.gl),this.framebuffer=FI(this.gl),this.textureConfig=ux(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(),sC(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),rC(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),aC(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),cC(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),uC(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),iC(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),oC(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(dx(this.gl,this.framebuffer),this.outputTexture=null),ke(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>hC(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return fC(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return pC(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=dC(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,()=>mC(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=eC(t));let n=NI(t);return ke(t,()=>t.attachShader(n,this.vertexShader)),ke(t,()=>t.attachShader(n,e)),EI(t,n),this.debug&&Lm(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=lC(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&&Lm(this.gl,this.program),ke(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?OI(this.gl,e,t):MI(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(),zI(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=lc(t,n);this.setOutputMatrixTextureDriver(e,s,r)}setOutputMatrixWriteRegion(e,t,n,s){this.setOutputMatrixWriteRegionDriver(n,e,s,t)}setOutputPackedMatrixWriteRegion(e,t,n,s){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&Lm(this.gl,this.program),vp(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=bp(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=rQ(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(),Bm(this.gl,e,this.framebuffer),this.debug&&vp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Bm(this.gl,this.outputTexture,this.framebuffer),this.debug&&vp(this.gl)):dx(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;Bm(s,e,this.framebuffer),this.debug&&vp(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 rQ(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:aQ,bincountImpl:gC,bincountReduceImpl:oQ,ceilImpl:iQ,concatImpl:lQ,equalImpl:uQ,expImpl:cQ,expm1Impl:dQ,floorImpl:pQ,gatherNdImpl:hQ,gatherV2Impl:fQ,greaterImpl:mQ,greaterEqualImpl:gQ,lessImpl:yQ,lessEqualImpl:AQ,linSpaceImpl:xQ,logImpl:bQ,maxImpl:vQ,maximumImpl:wQ,minimumImpl:kQ,multiplyImpl:SQ,negImpl:IQ,notEqualImpl:CQ,prodImpl:TQ,rangeImpl:NQ,rsqrtImpl:EQ,sigmoidImpl:RQ,simpleAbsImpl:yC,sliceImpl:_Q,sparseFillEmptyRowsImpl:$Q,sparseReshapeImpl:DQ,sparseSegmentReductionImpl:AC,sqrtImpl:FQ,stridedSliceImpl:PQ,stringNGramsImpl:OQ,stringSplitImpl:MQ,stringToHashBucketFastImpl:zQ,subImpl:LQ,tileImpl:BQ,topKImpl:WQ,transposeImpl:wx,uniqueImpl:VQ}=Rm;function xC(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Gn(e,t){return t===1?[e]:xC(e,t)}function UQ(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 GQ=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=Gn("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]})`}},bC=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=`
|
|
${HQ(t,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?mx():fx(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 HQ(e,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?pJ(["r","c","d"],"inputShape"):El(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var jQ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=wC(t,n),r=kC(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=vC(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===$n.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===$n.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===$n.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===$n.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===$n.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=wC(n,s),a=kC(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=vC(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 qQ(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 vC(e,t,n,s,r){let a=XQ(t,s),o;if(r){let[l,c]=lc(e[0],e[1]);o=l*c}else{let[l,c]=xp(e[0],e[1]);o=l*c}let i=qQ(n,a);return o*i}function XQ(e,t){switch(e){case $n.PACKED_2X2_FLOAT32:return bx(t);case $n.PACKED_2X2_FLOAT16:return vx(t);case $n.UNPACKED_FLOAT32:return yx(t);case $n.UNPACKED_FLOAT16:return Ax(t);case $n.PACKED_4X1_UNSIGNED_BYTE:return xx(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function KQ(e){return Y().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?$n.PACKED_2X2_FLOAT32:$n.UNPACKED_FLOAT32:e?$n.PACKED_2X2_FLOAT16:$n.UNPACKED_FLOAT16}function wC(e,t){if(e===rr.UPLOAD)return $n.PACKED_2X2_FLOAT32;if(e===rr.RENDER||e==null)return KQ(t);if(e===rr.DOWNLOAD||e===rr.PIXELS)return $n.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function kC(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var qo=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;",ZQ="return x;",SC="return abs(x);",YQ="return (x >= 0.0) ? x : (exp(x) - 1.0);",JQ=kr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,QQ=kr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,qm="return x;",eee="return 1.0 / (1.0 + exp(-1.0 * x));",tee="return x;",nee=`
|
|
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;
|
|
`,see=`
|
|
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;
|
|
`,ree=`
|
|
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;
|
|
`,aee="return 1.0 / (1.0 + exp(-1.0 * x));",fc=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},oee=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e,this.enableShapeUniforms=ds(this.outputShape.length);let t=e.length,n=Gn("rc",t),s=vt(t),r=UQ(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${o}));
|
|
}
|
|
`}},iee=er.whereImpl,lee=1e-7,uee=1e-4,Xm={};function cee(e){return e in Xm||(Xm[e]={}),Xm[e]}var dee=Y().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),pee=600;function hee(){return Y().global.screen==null?1024:Y().global.screen.height*Y().global.screen.width*window.devicePixelRatio*pee/1024/1024}var IC=class extends su{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!Y().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Wr(Y().getNumber("WEBGL_VERSION"));this.binaryCache=cee(Y().getNumber("WEBGL_VERSION")),this.gpgpu=new jm(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 jQ(this.gpgpu),this.numMBBeforeWarning=hee(),this.texData=new ad(this,rs())}nextDataId(){return IC.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((Y().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||Y().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()};return this.texData.set(s,{shape:t,dtype:n,values:e,usage:rr.UPLOAD,refCount:1}),s}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,s,r){if(Y().getBool("DEBUG")&&this.checkNumericalProblems(t),s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:s,values:t,usage:rr.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:s,complexTensorInfos:r,slice:a,shape:o,isPacked:i}=t;if(a!=null){let d;i?d=new fc(o,qm):d=new qo(o,qm);let p=this.runWebGLProgram(d,[{dataId:e,shape:o,dtype:s}],s),h=this.readSync(p.dataId);return this.disposeIntermediateTensorInfo(p),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(s==="string")return n;let l=this.activeTimers!=null,c;l&&(c=v.now());let u;if(s==="complex64"){let d=this.readSync(r.real.dataId),p=this.readSync(r.imag.dataId);u=N.mergeRealAndImagArrays(d,p)}else u=this.getValuesFromTexture(e);return l&&(this.downloadWaitMs+=v.now()-c),this.convertAndCacheOnCPU(e,u)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(f=>h.push(f))}let t=this.texData.get(e),{values:n,shape:s,slice:r,dtype:a,complexTensorInfos:o,isPacked:i}=t;if(r!=null){let h;i?h=new fc(s,qm):h=new qo(s,qm);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,...zm(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)&&rs().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(!SI(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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(Y().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...zm(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=Y().getBool("WEBGL_PACK")&&s===!0,o=a?Wm(t):t,i=a?new tQ(o):new eQ(o),l=this.runWebGLProgram(i,[{shape:o,dtype:n,dataId:e}],"float32"),c=this.texData.get(l.dataId),u=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(l),u}timerAvailable(){return Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(i=>i.query)).filter(i=>i!=null),a=v.flatten(this.activeTimers.map(i=>i.name)).filter(i=>i!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(Y().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let i=await Promise.all(r);o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${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=dee){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 iee(e.shape,t)}packedUnaryOp(e,t,n){let s=new fc(e.shape,t),r=this.compileAndRun(s,[e],n);return rs().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=yC(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,SC,e.dtype);let t=new qo(e.shape,SC),n=this.compileAndRun(t,[e]);return rs().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 rs().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new oee(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new GQ(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Tl(e.shape),...Nl(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Tl(t),...Nl(t)],a=new bC(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=Wm(s),o,i=zm(a);n?o=new QJ(a):o=new JJ(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===Mm.DENSE){let m=zm(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&&!wp(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=YJ(e,l,c),d=this.getAndSaveBinary(u,()=>KJ(this.gpgpu,e,l,c)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),ZJ(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?lee:uee}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=WI(n,i),t.texShape=u),r!=null){let d=Wm(n),p,h=u[1],f=u[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;(i||!m)&&([h,f]=lc(u[0],u[1])),i?p=new sQ(d,m):p=new nQ(d,m);let g=m?[f,h]:u,y=this.makeTensorInfo(g,s),x=this.texData.get(y.dataId);m?x.usage=rr.PIXELS:x.usage=rr.UPLOAD,x.texShape=g,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),h,f,r);let A=[[f,h]],b=!0,w=this.runWebGLProgram(p,[y],s,A,b),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=fee(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)}},Sp=IC;Sp.nextDataId=0;function fee(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 mee="0.0.0";function CC(){Y().set("WEBGL_FORCE_F16_TEXTURES",!0)}Ou.isBrowser()&&il("webgl",()=>new Sp,2);var gee={forceHalfFloat:CC},TC=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,mc=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=ds(this.outputShape.length),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},Km=`
|
|
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;
|
|
`,Ip=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) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=Gn("coords",r);this.enableShapeUniforms?a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${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);
|
|
${a}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function Ss(e){let{inputs:t,backend:n}=e,{x:s}=t;return n.incRef(s.dataId),{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}var yee={kernelName:Xa,backendName:"webgl",kernelFunc:Ss};function Xo(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.texData.get(a.dataId),i=Ss({inputs:{x:s},backend:n}),l=Ss({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var Aee={kernelName:ud,backendName:"webgl",kernelFunc:Xo},NC="return (a < 0.) ? b * a : a;",EC=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function xee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ip(EC,r.shape,o.shape):new mc(NC,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),l}var bee={kernelName:Ka,backendName:"webgl",kernelFunc:xee},RC="return (a < 0.) ? b * a : a;",_C=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function vee(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ip(_C,s.shape,r.shape):new mc(RC,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var wee={kernelName:io,backendName:"webgl",kernelFunc:vee},$C="if (isnan(x)) return x;",kee=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,See=`
|
|
result.r = isNaN.r > 0. ? NAN : result.r;
|
|
result.g = isNaN.g > 0. ? NAN : result.g;
|
|
result.b = isNaN.b > 0. ? NAN : result.b;
|
|
result.a = isNaN.a > 0. ? NAN : result.a;
|
|
`;function at({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:s}){return({inputs:r,backend:a})=>{let{x:o}=r,i=a,l=s||o.dtype;if(i.shouldExecuteOnCPU([o])&&n!=null){let d=i.texData.get(o.dataId),p=n(d.values,l);return i.makeTensorInfo(o.shape,l,p)}let c=Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new fc(o.shape,t):u=new qo(o.shape,e),i.runWebGLProgram(u,[o],l)}}function In({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:s=!1,cpuKernelImpl:r,dtype:a}){return({inputs:o,backend:i})=>{let{a:l,b:c}=o,u=i;if(s&&l.dtype==="complex64"){let f=u.texData.get(l.dataId),m=u.texData.get(c.dataId),[g,y]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(A=>{let[b,w]=A,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},I={dataId:w.dataId,dtype:w.dtype,shape:c.shape},E=new mc(e,l.shape,c.shape);return u.runWebGLProgram(E,[k,I],zn(b.dtype,w.dtype))}),x=Xo({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 Ip(t,l.shape,c.shape,n):h=new mc(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],d)}}function Zm(e,t=!1){if(e==="linear")return t?tee:ZQ;if(e==="relu")return t?see:JQ;if(e==="elu")return t?nee:YQ;if(e==="relu6")return t?ree:QQ;if(e==="prelu")return t?_C:RC;if(e==="leakyrelu")return t?EC:NC;if(e==="sigmoid")return t?aee:eee;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var DC=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);
|
|
}
|
|
`}},FC={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},PC=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) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},OC="return a * b;";function kx(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=N.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),c=new PC(FC.REAL,s.shape,r.shape),u=new PC(FC.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=Xo({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[c,u]=SQ(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(u,a),p=n.texData.get(d.dataId);return p.values=c,d}let o;return Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new Ip(OC,s.shape,r.shape):o=new mc(OC,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var Iee={kernelName:ro,backendName:"webgl",kernelFunc:kx};function Cee(e,t,n){let s=[Tl(e.shape),...Nl(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[Tl(t),...Nl(t)],o=new bC(a,s),i=!0,l=[s],c=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function be(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),c=v.sizeFromShape(l);v.assert(i===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let u=o.texData.get(r.dataId);return u.isPacked&&!wp(r.shape,l)&&!(u.texture!==null&&wp(u.shape,l))?Cee(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var Tee={kernelName:Mi,backendName:"webgl",kernelFunc:be},MC=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);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},Nee=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${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 Eee(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],s=N.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function _l(e,t,n,s){let r=Eee(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 MC({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},i):new MC({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c}):u=new Nee({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 Ree=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 $ee=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 ${this.rank} is not yet supported.`);let s=vt(this.rank),r=xC("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=r[c];let o=`vec2(${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 Ym(e,t,n){let s=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $ee(e.shape,t):new Ree(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function Dee(e,t,n,s){let r=t,a=e.shape.length,o=v.parseAxisParam(r,e.shape),i=o,l=N.getAxesPermutation(i,a),c=l!=null,u=e;c&&(u=Ym(e,l,s),i=N.getInnerMostAxes(i.length,a)),N.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=N.computeOutAndReduceShapes(u.shape,i),h=d;n&&(h=N.expandShapeToKeepDim(d,o));let f=v.sizeFromShape(p),g=v.sizeFromShape(e.shape)/f,y=be({inputs:{x:u},attrs:{shape:[g,f]},backend:s}),x=Fd(e.dtype),A=_l(y,x,"sum",s),b=be({inputs:{x:A},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(y),s.disposeIntermediateTensorInfo(A),c&&s.disposeIntermediateTensorInfo(u),b}function Jm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return Dee(r,a,o,n)}var Fee={kernelName:go,backendName:"webgl",kernelFunc:Jm};function Hn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];let c;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,p=wx(d,r.shape,r.dtype,a,l);c=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(c.dataId);h.values=p}else c=Ym(r,a,o);return c}var Pee={kernelName:vo,backendName:"webgl",kernelFunc:Hn},zC=1e3;function Qm({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),b=rl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[y,d,h]:[y,h,d],k=s?[x,f,p]:[x,p,f],I=be({inputs:{x:e},backend:r,attrs:{shape:w}}),E=be({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[I,E],P=Math.max(y,x),$=n?I.shape[1]:I.shape[2],_=a!=null,T=o!=null,O=l==="leakyrelu",V=l!=null?Zm(l,!0):null,X=_||T||O||V!=null,z;if((h===1||f===1)&&$>zC&&X===!1){let W=I,Q=E;n&&(W=Hn({inputs:{x:I},backend:r,attrs:{perm:[0,2,1]}}),R.push(W)),s&&(Q=Hn({inputs:{x:E},backend:r,attrs:{perm:[0,2,1]}}),R.push(Q));let ne=f!==1,ae=f===1,Z=W;ne&&(Z=be({inputs:{x:W},backend:r,attrs:{shape:[P,$,1]}}),R.push(Z));let oe=f===1?2:1,re=Q;ae&&(re=be({inputs:{x:Q},backend:r,attrs:{shape:[P,1,$]}}),R.push(re));let ge=kx({inputs:{a:Z,b:re},backend:r});z=Jm({inputs:{x:ge},backend:r,attrs:{axis:oe,keepDims:!0}}),R.push(ge)}else{let W=zn(e.dtype,t.dtype),Q=new DC(w,k,[P,h,f],n,s,_,V,T,O),ne=[I,E];if(a!=null&&ne.push(a),T&&ne.push(o),O){let ae=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));ne.push(ae),R.push(ae)}z=r.runWebGLProgram(Q,ne,W)}let j=be({inputs:{x:z},backend:r,attrs:{shape:b}});R.push(z);for(let W of R)r.disposeIntermediateTensorInfo(W);return j}function Oee(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return Qm({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var Mee={kernelName:ko,backendName:"webgl",kernelFunc:Oee},LC="return abs(x);";function zee(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])&&s.dtype!=="complex64"){let a=n.texData.get(s.dataId),o=yC(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new fc(s.shape,LC):r=new qo(s.shape,LC),n.runWebGLProgram(r,[s],s.dtype)}var Lee={kernelName:hi,backendName:"webgl",kernelFunc:zee},Bee=kr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,Wee=at({opSnippet:Bee}),Vee={kernelName:iu,backendName:"webgl",kernelFunc:Wee},Uee=kr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,Gee=at({opSnippet:Uee}),Hee={kernelName:lu,backendName:"webgl",kernelFunc:Gee},BC="return a + b;",jee=In({opSnippet:BC,packedOpSnippet:BC,supportsComplex:!0,cpuKernelImpl:aQ}),qee={kernelName:qr,backendName:"webgl",kernelFunc:jee},Xee=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,a)=>`T${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 = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}},Kee=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${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 e0(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=e0({inputs:s.slice(0,l),backend:n}),u=e0({inputs:s.slice(l),backend:n});return e0({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 Kee(s[0].shape,a):new Xee(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var Zee={kernelName:Ra,backendName:"webgl",kernelFunc:e0};function Yee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s,i=r.shape.length,l=v.parseAxisParam(a,r.shape),c=l,u=N.getAxesPermutation(c,i),d=r;u!=null&&(d=Hn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,i)),N.assertAxesAreInnerMostDims("all",c,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=_l(m,m.dtype,"all",n),y;if(o){let x=N.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var Jee={kernelName:uu,backendName:"webgl",kernelFunc:Yee};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=Hn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,i)),N.assertAxesAreInnerMostDims("any",c,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=_l(m,m.dtype,"any",n),y;if(o){let x=N.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var ete={kernelName:cu,backendName:"webgl",kernelFunc:Qee},tte=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 ${o} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},nte=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${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=Gn("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=Gn("sourceLocR",d-1).concat("inIdx.r"),g=Gn("sourceLocG",d-1).concat("inIdx.g"),y=Gn("sourceLocB",d-1).concat("inIdx.b"),x=Gn("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 WC(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 tte(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=WC(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}function VC(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=N.computeOptimalWindowSize(a),i=new nte(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=VC(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function UC(e,t,n,s){let r=[n];if(N.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!Y().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let a=[],o=e.texData.get(t.dataId),i=o!==null&&o.isPacked,l=t;i&&(l=e.unpackTensor(t),a.push(l));let[c,u]=N.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(u),p=be({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=WC(e,p,s);a.push(h);let f=be({inputs:{x:h},backend:e,attrs:{shape:c}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return VC(e,t,s)}function ste(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Hn({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=UC(n,l,o[0],"max");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var rte={kernelName:_a,backendName:"webgl",kernelFunc:ste};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=Hn({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=UC(n,l,o[0],"min");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var ote={kernelName:du,backendName:"webgl",kernelFunc:ate},ite=kr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,lte=at({opSnippet:ite}),ute={kernelName:pu,backendName:"webgl",kernelFunc:lte},cte=kr+"return log(x + sqrt(x * x + 1.0));",dte=at({opSnippet:cte}),pte={kernelName:hu,backendName:"webgl",kernelFunc:dte},hte=kr+`
|
|
return atan(x);
|
|
`,fte=at({opSnippet:hte}),mte={kernelName:fu,backendName:"webgl",kernelFunc:fte},gte=kee+`
|
|
return atan(a, b);
|
|
`,yte=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+See+`
|
|
return result;
|
|
`,Ate=In({opSnippet:gte,packedOpSnippet:yte}),xte={kernelName:gu,backendName:"webgl",kernelFunc:Ate},bte=kr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vte=at({opSnippet:bte}),wte={kernelName:mu,backendName:"webgl",kernelFunc:vte},Cp=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});
|
|
}
|
|
`}},Sx=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 kte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;uc(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(N.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=N.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Ss({inputs:{x:r},backend:n});let d=new Cp(u,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var Ste={kernelName:$a,backendName:"webgl",kernelFunc:kte};function Ite(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l,dataFormat:c}=s,u=[1,1,1],d=N.computePool3DInfo(r.shape,a,o,u,i,l,c),p=new Sx(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var Cte={kernelName:ld,backendName:"webgl",kernelFunc:Ite},Tte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=i-1-e.padInfo.top,u=l-1-e.padInfo.left,d=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${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 >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Nte=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
|
|
const ivec3 pads = ivec3(${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 Ete(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 Nte(p);return n.runWebGLProgram(h,[r],o.dtype)}var Rte={kernelName:_h,backendName:"webgl",kernelFunc:Ete};function _te(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;uc([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=N.computePool2DInfo(o.shape,i,l,1,c),d=new Tte(u);return n.runWebGLProgram(d,[r],o.dtype)}var $te={kernelName:Rh,backendName:"webgl",kernelFunc:_te};function Dte(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Qm({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var Fte={kernelName:Da,backendName:"webgl",kernelFunc:Dte},Pte=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)));
|
|
}
|
|
`}},Ote=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);
|
|
}
|
|
`}},Mte=({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 Ote(s.shape,r.shape,a.shape,u,d,l):new Pte(s.shape,r.shape,a.shape,u,d,l);return t.runWebGLProgram(p,c,c[0].dtype)},zte={kernelName:ja,backendName:"webgl",kernelFunc:Mte},Lte=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=Bte(this.rank),s,r=e.map((a,o)=>`sourceLoc.${Ix[o]} = start[${o}] + coords.${Ix[o]};`);s=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${r.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${s}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}},Ix=["x","y","z","w","u","v"];function Bte(e){if(e===1)return"sourceLoc";if(e<=6)return Ix.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Wte=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=vt(this.rank),n=Gn("coords",this.rank),s=Gn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${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 Vte(e,t,n,s){let r=s.texData.get(e.dataId),a=s.makeTensorInfo(n,e.dtype),o=s.texData.get(a.dataId);Object.assign(o,r),o.refCount=1,o.shape=n,o.dtype=e.dtype;let i=Mt.computeFlatOffset(t,v.computeStrides(e.shape));r.slice&&(i+=r.slice.flatOffset),o.slice={flatOffset:i,origDataId:r.slice&&r.slice.origDataId||e.dataId};let l=s.dataRefCount.get(o.slice.origDataId)||1;return s.dataRefCount.set(o.slice.origDataId,l+1),a}function gc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Mt.parseSliceParams(r,a,o);if(Mt.assertParamsValid(r,i,l),v.sizeFromShape(l)===0)return n.makeTensorInfo(l,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.texData.get(r.dataId),p=_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 Wte(l):new Lte(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),Vte(r,i,l,n)}var Ute={kernelName:Vi,backendName:"webgl",kernelFunc:gc},Gte=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;v.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=a.reduce((x,A)=>x*A),l=N.getReshaped(r.shape,a,i),c=N.getPermuted(l.length,a.length),u=N.getReshapedPermuted(r.shape,a,i),d=N.getSliceBeginCoords(o,a.length),p=N.getSliceSize(u,o,a.length),h=[],f=be({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Hn({inputs:{x:f},backend:n,attrs:{perm:c}}),g=be({inputs:{x:m},backend:n,attrs:{shape:u}}),y=gc({inputs:{x:g},backend:n,attrs:{begin:d,size:p}});return h.push(f),h.push(m),h.push(g),h.forEach(x=>n.disposeIntermediateTensorInfo(x)),y},Hte={kernelName:fi,backendName:"webgl",kernelFunc:Gte};function jte(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=gC(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var qte={kernelName:$h,backendName:"webgl",kernelFunc:jte};function Xte(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 Kte={kernelName:Dh,backendName:"webgl",kernelFunc:Xte},Zte="return float(a != b);",GC=In({opSnippet:Zte,cpuKernelImpl:CQ,dtype:"bool"}),Yte={kernelName:Ri,backendName:"webgl",kernelFunc:GC};function Tp(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 Jte={kernelName:Ad,backendName:"webgl",kernelFunc:Tp},Qte="return float(int(x));";function ene(e,t){let n=new qo(e.shape,Qte),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Cx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Ss({inputs:{x:r},backend:n});let o=Gt(r.shape),i=Cx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Xo({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=Tp({inputs:{input:r},backend:n}),i=Cx({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 ene(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=GC({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 tne={kernelName:Fa,backendName:"webgl",kernelFunc:Cx},HC="return ceil(x);",nne=at({opSnippet:HC,packedOpSnippet:HC,cpuKernelImpl:iQ}),sne={kernelName:Pa,backendName:"webgl",kernelFunc:nne},rne=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));
|
|
}
|
|
`}},ane=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 one(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 ane(r.shape):i=new rne(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var ine={kernelName:Xr,backendName:"webgl",kernelFunc:one},lne=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 jC(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function une(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new lne(s.shape),o=[jC(s,r.complexTensorInfos.real),jC(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var cne={kernelName:cd,backendName:"webgl",kernelFunc:une},dne=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;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},pne=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=vt(s),a=Gn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${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}(${t0(o,l,m)}),
|
|
vec2(${t0(c,l,m)}));
|
|
}`}let p=i.length,h=i[i.length-1];d+=`
|
|
return getChannel(
|
|
getT${p}(${t0(o,l,h)}),
|
|
vec2(${t0(c,l,h)}));`,this.userCode=`
|
|
float getValue(${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 t0(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function n0(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 hne={kernelName:fd,backendName:"webgl",kernelFunc:n0};function yc(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>Tp({inputs:{input:m},backend:n})),d=e.map(m=>n0({inputs:{input:m},backend:n})),p=yc(u,t,n),h=yc(d,t,n),f=Xo({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),d.forEach(m=>n.disposeIntermediateTensorInfo(m)),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(y=>{let x=v.sizeFromShape(y.shape.slice(t));return be({inputs:{x:y},backend:n,attrs:{shape:[-1,x]}})}),d=u.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),p=N.computeOutShape(u.map(y=>y.shape),1),h=u[0].shape[0]===1,f=lQ(d,p,s,h),m=N.computeOutShape(e.map(y=>y.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(y=>n.disposeIntermediateTensorInfo(y)),g}if(e.length>Y().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),d=yc(e.slice(0,u),t,n),p=yc(e.slice(u),t,n),h=yc([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new pne(e.map(d=>d.shape),t);return n.runWebGLProgram(u,e,s)}let{tensors2D:a,outShape:o}=fne(e,t,n),i=new dne(a.map(u=>u.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let c=be({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),c}function fne(e,t,n){let s=N.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>be({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function qC(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=N.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return Ss({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return N.assertParamsConsistent(l,a),yc(i,a,n)}var mne={kernelName:mi,backendName:"webgl",kernelFunc:qC},XC=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}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}},gne=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${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);
|
|
}
|
|
`}},yne=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=Un(),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 KC({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>zC)&&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(wp(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let I=be({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(I);let E=Qm({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=be({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=be({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),I=Qm({a:w,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=be({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 ZC({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,outWidth:d,outHeight:p,dataFormat:h}=n,f=h==="channelsLast",m=l*c*u,g=p*d,y=[m,g],x=!0,A=!1,b=[],w=be({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=be({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(k);let I=new yne(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=be({inputs:{x:R},backend:s,attrs:{shape:[1,y[0],y[1]]}});b.push(R),b.push(P);let $=r!=null,_=a!=null,T=i==="leakyrelu",O=i?Zm(i,!0):null,V=new DC(P.shape,k.shape,[1,g,n.outChannels],x,A,$,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(V,X,"float32"),j=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],W=be({inputs:{x:z},backend:s,attrs:{shape:j}});b.push(z);for(let Q of b)s.disposeIntermediateTensorInfo(Q);return W}function Ane(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=KC({x:r,filter:a,convInfo:p,backend:n});else if(Y().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=ZC({x:r,filter:a,convInfo:p,backend:n});else{let m=new XC(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=be({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var xne={kernelName:Oa,backendName:"webgl",kernelFunc:Ane},bne=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);
|
|
}
|
|
`}},vne=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);
|
|
}
|
|
`}},wne=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},kne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 Sne(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 bne(p);return n.runWebGLProgram(h,[r,a],"float32")}var Ine={kernelName:Fh,backendName:"webgl",kernelFunc:Sne};function Cne(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 vne(p);return n.runWebGLProgram(h,[r,a],"float32")}var Tne={kernelName:Ma,backendName:"webgl",kernelFunc:Cne};function Nne(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 gne(c);return n.runWebGLProgram(u,[r,a],"float32")}var Ene={kernelName:dd,backendName:"webgl",kernelFunc:Nne};function Rne(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 wne(c);return n.runWebGLProgram(u,[r,a],"float32")}var _ne={kernelName:Ph,backendName:"webgl",kernelFunc:Rne};function $ne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,c=N.computeConv3DInfo(l,a.shape,i,1,o),u=new kne(c);return n.runWebGLProgram(u,[r,a],"float32")}var Dne={kernelName:Oh,backendName:"webgl",kernelFunc:$ne},Fne=$C+`
|
|
return cos(x);
|
|
`,Pne=at({opSnippet:Fne}),One={kernelName:za,backendName:"webgl",kernelFunc:Pne},Mne=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,zne=at({opSnippet:Mne}),Lne={kernelName:La,backendName:"webgl",kernelFunc:zne},Bne=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[c]=t,[u,d]=n;this.outputShape=[c,u,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${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(${p} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},Wne=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new Bne(r.shape,a.shape,i,l,c);return n.runWebGLProgram(u,[r,a,o],"float32")},Vne={kernelName:yi,backendName:"webgl",kernelFunc:Wne},YC=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${JC(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 = ${QC(s,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${o}) {
|
|
int idx = ${i};
|
|
${QC(s,"coords")} = idx;
|
|
val += getX(${JC(s,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}};function JC(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 ${e} is not yet supported`)}function QC(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function Une(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,c=N.getAxesPermutation([a],l),u=r;c!=null&&(u=Hn({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=N.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=Ss({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new YC(u.shape,!1,i),g=[[f]],y=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(y)}if(o){let f=new YC(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=N.getUndoAxesPermutation(c),m=Hn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var Gne={kernelName:gi,backendName:"webgl",kernelFunc:Une};function Hne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=gC(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=oQ(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var jne={kernelName:Mh,backendName:"webgl",kernelFunc:Hne},qne=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 Xne(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 qne(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var Kne={kernelName:Ai,backendName:"webgl",kernelFunc:Xne},e4=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}
|
|
${c}
|
|
setOutput(result);
|
|
}
|
|
`}},t4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=ds(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,u=e.filterWidth,d=u,p=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<u;g++)p+=`
|
|
vec4 xTexelC${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 Zne(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 t4(d):p=new e4(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 Yne={kernelName:Ba,backendName:"webgl",kernelFunc:Zne},Jne=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Qne=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${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 * ${i} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function ese(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s,d=N.computeConv2DInfo(r.shape,u,o,i,l,c,!0),p=new Jne(d);return n.runWebGLProgram(p,[r,a],"float32")}var tse={kernelName:zh,backendName:"webgl",kernelFunc:ese};function nse(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s,d=N.computeConv2DInfo(u,a.shape,o,i,l,c,!0),p=new Qne(d);return n.runWebGLProgram(p,[r,a],"float32")}var sse={kernelName:Lh,backendName:"webgl",kernelFunc:nse},rse=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function ase(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=be({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new rse(a),l=n.runWebGLProgram(i,[o],o.dtype),c=be({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var ose={kernelName:Bh,backendName:"webgl",kernelFunc:ase},ise=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:c}=e,{top:u,left:d}=s;this.userCode=`
|
|
const ivec2 strides = ivec2(${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 lse(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 ise(c);u=n.runWebGLProgram(d,[r,a],"float32");let p=be({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var use={kernelName:pd,backendName:"webgl",kernelFunc:lse};function cse(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=Hn({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(A.shape,b)||(A=be({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),p===null?p=A:(p=kx({inputs:{a:A,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Jm({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 dse={kernelName:hd,backendName:"webgl",kernelFunc:cse},pse="return (x >= 0.0) ? x : (exp(x) - 1.0);",hse=`
|
|
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;
|
|
`,fse=at({opSnippet:pse,packedOpSnippet:hse}),mse={kernelName:Va,backendName:"webgl",kernelFunc:fse},gse="return (b >= 1.0) ? a : a * (b + 1.0);",yse=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,Ase=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=Y().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Ip(yse,s.shape,r.shape):new mc(gse,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},xse={kernelName:Uh,backendName:"webgl",kernelFunc:Ase},bse=`
|
|
return vec4(equal(a, b));
|
|
`,vse="return float(a == b);",wse=In({opSnippet:vse,packedOpSnippet:bse,dtype:"bool",cpuKernelImpl:uQ}),kse={kernelName:xi,backendName:"webgl",kernelFunc:wse},Sse=`
|
|
// 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 = ${N.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,Ise=at({opSnippet:Sse}),Cse={kernelName:yu,backendName:"webgl",kernelFunc:Ise},n4="return exp(x);",s4=at({opSnippet:n4,packedOpSnippet:n4,cpuKernelImpl:cQ,dtype:"float32"}),Tse={kernelName:Ua,backendName:"webgl",kernelFunc:s4};function Tx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),be({inputs:{x:a},backend:s,attrs:{shape:i}})}var Nse={kernelName:bi,backendName:"webgl",kernelFunc:Tx},r4="return exp(x) - 1.0;",Ese=at({opSnippet:r4,packedOpSnippet:r4,cpuKernelImpl:dQ}),Rse={kernelName:vi,backendName:"webgl",kernelFunc:Ese},a4=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 o4(e,t,n){let s=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=be({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,c=new a4("real",l,t),u=new a4("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=Xo({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=be({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function _se(e){let{inputs:t,backend:n}=e,{input:s}=t;return o4(s,!1,n)}var $se={kernelName:Gh,backendName:"webgl",kernelFunc:_se},Dse=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 Np(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 Dse(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var Fse={kernelName:Au,backendName:"webgl",kernelFunc:Np},Pse=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},Ose={kernelName:wi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Pse(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},i4="return floor(x);",Mse=at({opSnippet:i4,packedOpSnippet:i4,cpuKernelImpl:pQ}),zse={kernelName:Ga,backendName:"webgl",kernelFunc:Mse},Lse=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,Bse=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,Wse=In({opSnippet:Lse,packedOpSnippet:Bse,dtype:"int32"}),Vse={kernelName:Ha,backendName:"webgl",kernelFunc:Wse},Use=class{constructor(e){this.variableNames=["A"];let t=Un(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},Gse=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Un(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${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;
|
|
}
|
|
`}},Hse={kernelName:Id,backendName:"webgl",kernelFunc:jse},Ac;function jse(e){let{inputs:t,backend:n,attrs:s}=e,{pixels:r}=t,{numChannels:a}=s,o=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[l,c]=o?[r.videoWidth,r.videoHeight]:[r.width,r.height],u=[c,l],d=[c,l,a];(i||o)&&(Ac==null&&(Ac=document.createElement("canvas").getContext("2d")),Ac.canvas.width=l,Ac.canvas.height=c,Ac.drawImage(r,0,0,l,c),r=Ac.canvas);let p=n.makeTensorInfo(u,"int32");n.texData.get(p.dataId).usage=rr.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=Y().getBool("WEBGL_PACK")?new Gse(d):new Use(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function qse(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=KC({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=ZC({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?Zm(h,!1):null,E=new XC(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=be({inputs:{x:y},backend:n,attrs:{shape:g.outShape}});return x.push(y),x.forEach(b=>n.disposeIntermediateTensorInfo(b)),A}var Xse={kernelName:So,backendName:"webgl",kernelFunc:qse};function Kse(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?Zm(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 t4(g,b,x,w,k):I=new e4(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 Zse={kernelName:Io,backendName:"webgl",kernelFunc:Kse},Yse=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 Jse(e){let{inputs:t,backend:n}=e,{params:s,indices:r}=t,a=r.shape,o=a[a.length-1],i=v.sizeFromShape(s.shape),[l,c,u,d]=N.prepareAndValidate(s,r),p=be({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=be({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let y=n.readSync(r.dataId),x=n.bufferSync(s),A=hQ(y,x,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,A.values)}let f=new Yse(o,d,[c,u]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=be({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var Qse={kernelName:Si,backendName:"webgl",kernelFunc:Jse},ere=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=vt(this.rank),s=tre(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function tre(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 l4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,indices:a}=t,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=n.readSync(a.dataId),u=r.shape[l];for(let b=0;b<c.length;++b){let w=c[b];v.assert(w<=u-1&&w>=0,()=>`GatherV2: the index value ${w} is not in [0, ${u-1}]`)}let d=N.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=v.sizeFromShape(a.shape),h=[],f=be({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=be({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,p/d.batchSize]}});h.push(f),h.push(m);let g=[d.batchSize,d.outerSize,p/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let b=n.bufferSync(m),w=n.bufferSync(f),k=fQ(w,b,g);return h.forEach(I=>n.disposeIntermediateTensorInfo(I)),n.makeTensorInfo(d.outputShape,k.dtype,k.values)}let y=new ere(f.shape,g),x=n.runWebGLProgram(y,[f,m],f.dtype);h.push(x);let A=be({inputs:{x},backend:n,attrs:{shape:d.outputShape}});return h.forEach(b=>n.disposeIntermediateTensorInfo(b)),A}var nre={kernelName:ki,backendName:"webgl",kernelFunc:l4},sre="return float(a > b);",rre=`
|
|
return vec4(greaterThan(a, b));
|
|
`,are=In({opSnippet:sre,packedOpSnippet:rre,cpuKernelImpl:mQ,dtype:"bool"}),ore={kernelName:Ii,backendName:"webgl",kernelFunc:are},ire="return float(a >= b);",lre=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,ure=In({opSnippet:ire,packedOpSnippet:lre,dtype:"bool",cpuKernelImpl:gQ}),cre={kernelName:qa,backendName:"webgl",kernelFunc:ure};function dre(e){let{inputs:t,backend:n}=e,{input:s}=t;return o4(s,!0,n)}var pre={kernelName:Hh,backendName:"webgl",kernelFunc:dre},hre="return float(!isnan(x) && !isinf(x));",fre=at({opSnippet:hre,dtype:"bool"}),mre={kernelName:xu,backendName:"webgl",kernelFunc:fre},gre="return float(isinf(x));",yre=at({opSnippet:gre,dtype:"bool"}),Are={kernelName:bu,backendName:"webgl",kernelFunc:yre},xre="return float(isnan(x));",bre=at({opSnippet:xre,dtype:"bool"}),vre={kernelName:vu,backendName:"webgl",kernelFunc:bre},wre="return float(a < b);",kre=`
|
|
return vec4(lessThan(a, b));
|
|
`,Sre=In({opSnippet:wre,packedOpSnippet:kre,cpuKernelImpl:yQ,dtype:"bool"}),Ire={kernelName:Ci,backendName:"webgl",kernelFunc:Sre},Cre="return float(a <= b);",Tre=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,Nre=In({opSnippet:Cre,packedOpSnippet:Tre,cpuKernelImpl:AQ,dtype:"bool"}),Ere={kernelName:Ti,backendName:"webgl",kernelFunc:Nre};function Rre(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=xQ(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var _re={kernelName:jh,backendName:"webgl",kernelFunc:Rre},$re=`if (x < 0.0) return NAN;
|
|
return log(x);`,Dre=`
|
|
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;
|
|
`,Fre=at({opSnippet:$re,packedOpSnippet:Dre,cpuKernelImpl:bQ}),Pre={kernelName:Za,backendName:"webgl",kernelFunc:Fre},Ore="return log(1.0 + x);",Mre=at({opSnippet:Ore}),zre={kernelName:wu,backendName:"webgl",kernelFunc:Mre},Lre="return float(a >= 1.0 && b >= 1.0);",Bre=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,Wre=In({opSnippet:Lre,packedOpSnippet:Bre,dtype:"bool"}),Vre={kernelName:Ni,backendName:"webgl",kernelFunc:Wre},Ure="return float(!(x >= 1.0));",Gre=at({opSnippet:Ure}),Hre={kernelName:ku,backendName:"webgl",kernelFunc:Gre},jre="return float(a >= 1.0 || b >= 1.0);",qre=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,Xre=In({opSnippet:jre,packedOpSnippet:qre,dtype:"bool"}),Kre={kernelName:md,backendName:"webgl",kernelFunc:Xre},Zre=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${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 * ${i};
|
|
setOutput(val);
|
|
}
|
|
`}},Yre=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${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);
|
|
}
|
|
`}},Jre=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 Yre(r.shape,a,o,i,l):new Zre(r.shape,a,o,i,l);return n.runWebGLProgram(c,[r],r.dtype)},Qre={kernelName:gd,backendName:"webgl",kernelFunc:Jre},eae=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);
|
|
}
|
|
`}},tae=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 eae(r.shape,i,l,c,u);return n.runWebGLProgram(d,[r,a,o],r.dtype)},nae={kernelName:qh,backendName:"webgl",kernelFunc:tae};function sae(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=_l(i,e.dtype,"max",s),c=be({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}function u4(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=wx(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=Ym(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=vQ(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=sae(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),y}var rae={kernelName:Ya,backendName:"webgl",kernelFunc:u4},aae=TC+`
|
|
return max(a, b);
|
|
`,oae=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Km+`
|
|
return result;
|
|
`,iae=In({opSnippet:aae,packedOpSnippet:oae,cpuKernelImpl:wQ}),lae={kernelName:Ja,backendName:"webgl",kernelFunc:iae};function uae(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;uc(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(N.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=N.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Ss({inputs:{x:r},backend:n});let d=new Cp(u,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var cae={kernelName:Qa,backendName:"webgl",kernelFunc:uae};function dae(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 Sx(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var pae={kernelName:yd,backendName:"webgl",kernelFunc:dae},hae=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 * ${a} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},fae=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,h=i*l*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 mae(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 Sx(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new fae(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var gae={kernelName:Kh,backendName:"webgl",kernelFunc:mae};function yae(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;uc([a,o],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:d}=s,p=N.computePool2DInfo(i.shape,l,c,1,u,d),h=!0,f=new Cp(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new hae(p),y=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),y}var Aae={kernelName:Xh,backendName:"webgl",kernelFunc:yae};function xae(e,t,n,s){let r=new Cp(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new Cp(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var bae={kernelName:Zh,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]=xae(s,i,u,l);return[d,p]}};function vae(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=be({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=_l(i,"float32","mean",s),c=be({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}var wae={kernelName:eo,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=wx(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=Ym(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=vae(f,g,y,o);for(let A of h)o.disposeIntermediateTensorInfo(A);return x}};function kae(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=Hn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=N.getInnerMostAxes(c.length,r.shape.length)),N.assertAxesAreInnerMostDims("min",c,i);let[p,h]=N.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=be({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=_l(m,m.dtype,"min",n),y;if(o){let x=N.expandShapeToKeepDim(p,l);y=be({inputs:{x:g},backend:n,attrs:{shape:x}})}else y=be({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),y}var Sae={kernelName:to,backendName:"webgl",kernelFunc:kae},Iae=TC+`
|
|
return min(a, b);
|
|
`,Cae=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Km+`
|
|
return result;
|
|
`,Tae=In({opSnippet:Iae,packedOpSnippet:Cae,cpuKernelImpl:kQ}),Nae={kernelName:no,backendName:"webgl",kernelFunc:Tae},Eae=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}));
|
|
}
|
|
`}},Rae=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=Gn("rc",s),l=Gn("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.);
|
|
${p}
|
|
setOutput(result);
|
|
}
|
|
`}},_ae=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Rae(s.shape,r,a):new Eae(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},$ae={kernelName:so,backendName:"webgl",kernelFunc:_ae},Dae=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,Fae=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+Km+`
|
|
return result;
|
|
`,Pae=In({opSnippet:Dae,packedOpSnippet:Fae}),Oae={kernelName:Su,backendName:"webgl",kernelFunc:Pae},Mae=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}));
|
|
}
|
|
`}},zae=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,Lae=`
|
|
// 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;
|
|
`,c4=In({opSnippet:zae,packedOpSnippet:Lae,checkOutOfBounds:!0}),Bae={kernelName:Wa,backendName:"webgl",kernelFunc:c4},d4="return a - b;",p4=In({opSnippet:d4,packedOpSnippet:d4,supportsComplex:!0,cpuKernelImpl:LQ}),Wae={kernelName:xo,backendName:"webgl",kernelFunc:p4};function h4(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=u4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,o),c=be({inputs:{x:i},backend:n,attrs:{shape:l}}),u=p4({inputs:{a:r,b:c},backend:n}),d=s4({inputs:{x:u},backend:n}),p=Jm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=be({inputs:{x:p},backend:n,attrs:{shape:l}}),f=c4({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 Vae={kernelName:yo,backendName:"webgl",kernelFunc:h4};function Uae(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:h4({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],d=new Mae(c,u,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var Gae={kernelName:Yh,backendName:"webgl",kernelFunc:Uae},f4="return -x;";function Hae(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=IQ(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return Y().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new fc(s.shape,f4):r=new qo(s.shape,f4),n.runWebGLProgram(r,[s],s.dtype)}var jae={kernelName:Ei,backendName:"webgl",kernelFunc:Hae},qae=er.nonMaxSuppressionV3Impl;function Xae(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}=qae(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Kae={kernelName:_i,backendName:"webgl",kernelFunc:Xae},Zae=er.nonMaxSuppressionV4Impl;function Yae(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}=Zae(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Jae={kernelName:Iu,backendName:"webgl",kernelFunc:Yae},Qae=er.nonMaxSuppressionV5Impl;function eoe(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}=Qae(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var toe={kernelName:$i,backendName:"webgl",kernelFunc:eoe},noe=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${s}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},soe=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 noe(l,a,o,i),u=be({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let p=[...r.shape,a],h=be({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},roe={kernelName:Fi,backendName:"webgl",kernelFunc:soe};function s0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=Tp({inputs:{input:s},backend:n}),a=s0({inputs:{x:r},backend:n}),o=n0({inputs:{input:s},backend:n}),i=s0({inputs:{x:o},backend:n}),l=Xo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Np({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var aoe={kernelName:Yi,backendName:"webgl",kernelFunc:s0};function m4(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=Tp({inputs:{input:s},backend:n}),a=m4({inputs:{x:r},backend:n}),o=n0({inputs:{input:s},backend:n}),i=s0({inputs:{x:o},backend:n}),l=Xo({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Np({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var ooe={kernelName:Di,backendName:"webgl",kernelFunc:m4};function ioe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Tx({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=Tx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=qC({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var loe={kernelName:Pi,backendName:"webgl",kernelFunc:ioe},uoe=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}));
|
|
}
|
|
}
|
|
`}},coe=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=Gn("rc",s),l=Gn("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);
|
|
}
|
|
`}},g4=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 Np({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=Y().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new coe(r.shape,a,o):new uoe(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},doe={kernelName:ao,backendName:"webgl",kernelFunc:g4},poe=`
|
|
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);
|
|
`,hoe=`
|
|
// 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));
|
|
`+Km+`
|
|
return result;
|
|
`,foe=In({opSnippet:poe,packedOpSnippet:hoe}),moe={kernelName:oo,backendName:"webgl",kernelFunc:foe};function goe(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=Hn({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}=TQ(p.shape,p.dtype,f,u);h=n.makeTensorInfo(g,y,m)}else{let[f,m]=N.computeOutAndReduceShapes(p.shape,u),g=v.sizeFromShape(m),y=be({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),x=Fd(r.dtype),A=_l(y,x,"prod",n);h=be({inputs:{x:A},backend:n,attrs:{shape:f}}),l.push(y),l.push(A)}if(o){l.push(h);let f=N.expandShapeToKeepDim(h.shape,c);h=be({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var yoe={kernelName:Oi,backendName:"webgl",kernelFunc:goe},y4=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=NQ(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},Aoe={kernelName:Cu,backendName:"webgl",kernelFunc:y4},xoe="return 1.0 / x;",boe=at({opSnippet:xoe}),voe={kernelName:Tu,backendName:"webgl",kernelFunc:boe},woe=kr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,koe=`
|
|
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;
|
|
`,Soe=at({opSnippet:woe,packedOpSnippet:koe}),Ioe={kernelName:lo,backendName:"webgl",kernelFunc:Soe},Coe=kr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Toe=`
|
|
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;
|
|
`,Noe=at({opSnippet:Coe,packedOpSnippet:Toe}),Eoe={kernelName:co,backendName:"webgl",kernelFunc:Noe},Roe=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 $oe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new _oe(r.shape,l,c,a,o):new Roe(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],"float32")}var Doe={kernelName:uo,backendName:"webgl",kernelFunc:$oe},Foe=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 Poe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Foe(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Ooe={kernelName:Qh,backendName:"webgl",kernelFunc:Poe},Moe=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);
|
|
}
|
|
`}},zoe=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 < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function Loe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=Y().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new zoe(r.shape,l,c,a,o):new Moe(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],r.dtype)}var Boe={kernelName:Nu,backendName:"webgl",kernelFunc:Loe},Woe=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${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),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function Voe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Woe(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Uoe={kernelName:Jh,backendName:"webgl",kernelFunc:Voe},Goe=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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}));
|
|
}
|
|
`}},Hoe=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-${n} tensor is not yet supported`);this.outputShape=e;let s=Gn("rc",n),r=`${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 joe(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 Hoe(r.shape,i):new Goe(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var qoe={kernelName:zi,backendName:"webgl",kernelFunc:joe},Xoe=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 < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},Koe={kernelName:Ji,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Xoe(s.shape,a),[c,u]=N.getImageCenter(o,s.shape[1],s.shape[2]),d=[[c,u,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},Zoe=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,Yoe=at({opSnippet:Zoe}),Joe={kernelName:Li,backendName:"webgl",kernelFunc:Yoe},Qoe="return inversesqrt(x);",eie=at({opSnippet:Qoe,cpuKernelImpl:EQ}),tie={kernelName:po,backendName:"webgl",kernelFunc:eie},A4=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=vt(r.length),l=vt(a.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${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 nie(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r,updates:a}=t,{shape:o}=s,{sliceRank:i,numUpdates:l,sliceSize:c,strides:u,outputSize:d}=N.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=be({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=be({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new A4(l,i,h.shape.length,f.shape.length,u,p),y=n.runWebGLProgram(g,[f,h,m],f.dtype),x=be({inputs:{x:y},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(m),x}var sie={kernelName:Bi,backendName:"webgl",kernelFunc:nie},rie=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 aie(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new rie(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],zn(r.dtype,a.dtype))}var oie={kernelName:Wi,backendName:"webgl",kernelFunc:aie},iie=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${N.SELU_SCALEALPHA};
|
|
float scale = ${N.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,lie=at({opSnippet:iie}),uie={kernelName:Eu,backendName:"webgl",kernelFunc:lie},x4="return 1.0 / (1.0 + exp(-1.0 * x));",cie=at({opSnippet:x4,packedOpSnippet:x4,cpuKernelImpl:RQ}),die={kernelName:fo,backendName:"webgl",kernelFunc:cie},pie=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,hie=at({opSnippet:pie}),fie={kernelName:Ru,backendName:"webgl",kernelFunc:hie},mie=$C+`
|
|
return sin(x);
|
|
`,gie=at({opSnippet:mie}),yie={kernelName:ho,backendName:"webgl",kernelFunc:gie},Aie=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,xie=at({opSnippet:Aie}),bie={kernelName:Ui,backendName:"webgl",kernelFunc:xie},vie=`
|
|
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;
|
|
`,wie=at({opSnippet:vie}),kie={kernelName:_u,backendName:"webgl",kernelFunc:wie},Sie=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=g4({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=N.getReshaped(u.shape,a,i,!1),p=N.getPermuted(d.length,a.length,!1),h=N.getReshapedPermuted(u.shape,a,i,!1),f=be({inputs:{x:u},backend:n,attrs:{shape:d}}),m=Hn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=be({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(y=>n.disposeIntermediateTensorInfo(y)),g},Iie={kernelName:Gi,backendName:"webgl",kernelFunc:Sie};function Cie(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:
|
|
${o.shape}`);let i=n.readSync(s.dataId),l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=n.readSync(o.dataId)[0],[d,p,h,f,m]=$Q(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 Tie={kernelName:xd,backendName:"webgl",kernelFunc:Cie};function Nie(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 ${a.shape}`);let o=Array.from(n.readSync(r.dataId)),i=n.readSync(s.dataId),l=Array.from(n.readSync(a.dataId)),[c,u,d]=DQ(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Eie={kernelName:$u,backendName:"webgl",kernelFunc:Nie};function Rie(e){let{inputs:t,backend:n}=e,{data:s,indices:r,segmentIds:a}=t;if(s.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=AC(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var _ie={kernelName:bd,backendName:"webgl",kernelFunc:Rie};function $ie(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
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=AC(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var Die={kernelName:vd,backendName:"webgl",kernelFunc:$ie};function Fie(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 A4(c,l,r.shape.length,a.shape.length,u,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=be({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var Pie={kernelName:wd,backendName:"webgl",kernelFunc:Fie};function Oie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=N.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=gc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var Mie={kernelName:Hi,backendName:"webgl",kernelFunc:Oie},b4="return sqrt(x);",zie=at({opSnippet:b4,packedOpSnippet:b4,cpuKernelImpl:FQ}),Lie={kernelName:mo,backendName:"webgl",kernelFunc:zie},Bie="return x * x;",Wie=at({opSnippet:Bie}),Vie={kernelName:Du,backendName:"webgl",kernelFunc:Wie},v4="return (a - b) * (a - b);",Uie=In({opSnippet:v4,packedOpSnippet:v4}),Gie={kernelName:Ao,backendName:"webgl",kernelFunc:Uie};function Hie({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=kr+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,a=new qo(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var jie={kernelName:wo,backendName:"webgl",kernelFunc:Hie},qie=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=vt(n.length),a=vt(n.length),o="";if(s===1)o="coords * strides + begin";else{let i=0;o=n.map((l,c)=>(i++,n.length===1?`coords * strides[${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 Xie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,end:o,strides:i,beginMask:l,endMask:c,ellipsisMask:u,newAxisMask:d,shrinkAxisMask:p}=s,{finalShapeSparse:h,finalShape:f,isIdentity:m,sliceDim0:g,isSimpleSlice:y,begin:x,end:A,strides:b}=Mt.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=be({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||y){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let I=Mt.computeOutShape(x,A,b),E=gc({inputs:{x:r},backend:n,attrs:{begin:x,size:I}});w=be({inputs:{x:E},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(E)}else if(n.shouldExecuteOnCPU([r])){let E=n.readSync(r.dataId),R=ze(r.shape,r.dtype,E),P=PQ(h,R,b,x);w=n.makeTensorInfo(f,r.dtype,P.values)}else{let E=new qie(x,b,h);w=n.runWebGLProgram(E,[r],r.dtype)}let k=be({inputs:{x:w},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(w),k}var Kie={kernelName:ji,backendName:"webgl",kernelFunc:Xie};function Zie(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]=OQ(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Yie={kernelName:kd,backendName:"webgl",kernelFunc:Zie};function Jie(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]=MQ(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 Qie={kernelName:ef,backendName:"webgl",kernelFunc:Jie};function ele(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=zQ(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var tle={kernelName:tf,backendName:"webgl",kernelFunc:ele},nle="return tan(x);",sle=at({opSnippet:nle}),rle={kernelName:qi,backendName:"webgl",kernelFunc:sle},ale=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,ole=at({opSnippet:ale}),ile={kernelName:bo,backendName:"webgl",kernelFunc:ole},lle=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=ule(e);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function ule(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 w4(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=BQ(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new lle(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var cle={kernelName:Kr,backendName:"webgl",kernelFunc:w4},dle=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));
|
|
}
|
|
}
|
|
`}},ple=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 $l(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function k4(e){let t=1;for(;t<e;)t*=2;return t}function hle(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=Y().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=Y().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),c=r.shape,u=c[c.length-1];if(n.shouldExecuteOnCPU([r])||u<i||a>l){let P=n.readSync(r.dataId),[$,_]=WQ(P,c,r.dtype,a,o);return[n.makeTensorInfo($.shape,$.dtype,$.values),n.makeTensorInfo(_.shape,_.dtype,_.values)]}if(a===0)return c[c.length-1]=0,[n.makeTensorInfo(c,r.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(u===1)return[r,Np({attrs:{shape:c,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,m=v.sizeFromShape(c)/u,g=be({inputs:{x:h},attrs:{shape:[m,u]},backend:n});p&&$l(n,h);let y=k4(a),x=k4(u),A=null,b=()=>A===null?[g,g]:[g,A],w=(P,$,_)=>{let T=b(),O=new dle(_),X=[[u],[A===null?1:0],[Number.NEGATIVE_INFINITY],[P],[$]],z=A;A=n.runWebGLProgram(O,T,"int32",X),$l(n,z)};for(let P=1;P<y;P*=2){let $=P*2;for(let _=P;_>=1;_/=2)w($,_,[m,x])}for(let P=x;P>y;P/=2){let $=b(),_=new ple([m,P/2]),O=[[u],[A===null?1:0],[y]],V=A;A=n.runWebGLProgram(_,$,"int32",O),$l(n,V);let X=y/2,z=X*2;for(let j=X;j>=1;j/=2)w(z,j,A.shape)}let k=A;A=gc({inputs:{x:A},backend:n,attrs:{begin:0,size:[m,a]}}),$l(n,k);let I=l4({inputs:{x:g,indices:A},backend:n,attrs:{axis:1,batchDims:1}});$l(n,g);let E=c.slice(0,-1);E.push(a),k=A,A=be({inputs:{x:A},attrs:{shape:E},backend:n}),$l(n,k);let R=I;return I=be({inputs:{x:I},attrs:{shape:E},backend:n}),$l(n,R),[I,A]}var fle={kernelName:Xi,backendName:"webgl",kernelFunc:hle},mle=class{constructor(e,t,n,s,r,a){this.variableNames=["Image","Transforms"],this.outputShape=a;let o=n==="nearest"?1:2,i;switch(s){case"constant":i=1;break;case"reflect":i=2;break;case"wrap":i=3;break;case"nearest":i=4;break;default:i=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${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 gle(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 mle(d,p,o,i,l,g);return n.runWebGLProgram(y,[r,a],"float32")}var yle={kernelName:Ki,backendName:"webgl",kernelFunc:gle};function Ale(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;uc(a,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let o=s.readSync(a.dataId),{outputValues:i,outputShape:l,indices:c}=VQ(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var xle={kernelName:nf,backendName:"webgl",kernelFunc:Ale};function ble(e){let{inputs:t,backend:n,attrs:s}=e,{value:r}=t,{axis:a}=s;a<0&&(a+=r.shape.length);let o=r,i=o.shape.length,l=r.shape[a],c=new Array(i-1),u=0;for(let m=0;m<i;m++)m!==a&&(c[u++]=o.shape[m]);let d=[],p=new Array(i).fill(0),h=o.shape.slice();h[a]=1;let f=new Array(l);for(let m=0;m<f.length;m++){p[a]=m;let g=gc({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),y=be({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=y,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var vle={kernelName:Zi,backendName:"webgl",kernelFunc:ble},wle=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 kle(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=Hn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(d),c=N.getInnerMostAxes(1,i)[0]);let p=N.segment_util.computeOutShape(d.shape,c,o),h=v.sizeFromShape([d.shape[c]]),f=be({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=Fd(r.dtype),g=(b,w,k,I,E)=>{let R=b.shape[0],P=b.shape[1],$=N.segment_util.segOpComputeOptimalWindowSize(P,E),_={windowSize:$,inSize:P,batchSize:R,numSegments:E},T=new wle(_,w),O=n.compileAndRun(T,[b,k],I);if(l.push(O),O.shape[1]===E)return O;let V=y4({backend:n,attrs:{start:0,stop:E,step:1,dtype:"float32"}}),X=w4({inputs:{x:V},backend:n,attrs:{reps:[P/$]}});return l.push(V),l.push(X),g(O,w,X,I,E)},y=g(f,"unsortedSegmentSum",a,m,o),x=be({inputs:{x:y},backend:n,attrs:{shape:p}}),A=x;if(u!=null){l.push(x);let b=N.getUndoAxesPermutation(u);A=Hn({inputs:{x:A},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),A}var Sle={kernelName:Sd,backendName:"webgl",kernelFunc:kle},Ile=[Qre,nae,Mee,Lee,Vee,Hee,qee,Zee,Jee,ete,rte,ote,ute,pte,xte,mte,wte,Cte,Ste,Rte,$te,Fte,zte,Hte,qte,Kte,tne,sne,ine,cne,Aee,mne,Ine,Tne,xne,_ne,Dne,Ene,One,Lne,Vne,Gne,jne,Kne,tse,sse,Yne,ose,use,dse,mse,xse,kse,Cse,Tse,Nse,Rse,$se,Fse,Ose,zse,Vse,Hse,Xse,Zse,Qse,nre,ore,cre,yee,pre,hne,mre,Are,vre,bee,Ire,Ere,_re,zre,Pre,Vre,Hre,Kre,rae,pae,cae,gae,Aae,bae,lae,wae,Sae,Nae,$ae,Oae,Gae,Iee,jae,Kae,Jae,toe,Yte,roe,ooe,loe,doe,moe,wee,yoe,Aoe,Jte,Bae,voe,Eoe,Ioe,Tee,Doe,Ooe,Boe,Uoe,qoe,Koe,Joe,tie,sie,oie,uie,die,fie,yie,bie,Ute,Vae,kie,Iie,Tie,Eie,_ie,Die,Pie,Mie,Lie,Vie,Gie,jie,Kie,Yie,Qie,tle,Wae,Fee,rle,ile,cle,fle,yle,Pee,xle,vle,Sle,aoe];for(let e of Ile)cr(e);var Vr=Y();Vr.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Vr.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Vr.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Vr.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Vr.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Vr.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Vr.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Vr.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Vr.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Vr.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function Cle(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 r0(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Nx(){return`
|
|
[[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]]
|
|
`}function ia(){return`
|
|
${Nx()}
|
|
fn main([[builtin(local_invocation_id)]] LocalId : vec3<u32>,
|
|
[[builtin(global_invocation_id)]] GlobalId : vec3<u32>,
|
|
[[builtin(num_workgroups)]] NumWorkgroups: vec3<u32>) {
|
|
localId = LocalId;
|
|
globalId = GlobalId;
|
|
numWorkgroups = NumWorkgroups;
|
|
`}function Ke(){return`
|
|
${ia()}
|
|
let index = getGlobalIndex();
|
|
`}function Tle(e,t,n,s=!1){let r=`
|
|
let workGroupSizeX = ${n.workGroupSize[0]}u;
|
|
let workGroupSizeY = ${n.workGroupSize[1]}u;
|
|
let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(s===!0){let h=C4(t.shape),f=`
|
|
struct Matrix0 {
|
|
numbers: array<${r0(t.dtype,n.isVec4)}>;
|
|
};
|
|
struct Uniform {
|
|
size : i32;
|
|
numChannels : i32;
|
|
outShapeStrides : vec2<i32>;
|
|
dispatchSize : vec3<u32>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
|
|
[[group(0), binding(2)]] var<uniform> uniforms: Uniform;
|
|
`;return[S4,f,r,I4,h,n.getUserCode()].join(`
|
|
`)}let a=[],o="struct Uniforms { NAN : f32; ";n.variableNames.forEach((h,f)=>{o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${wn(e[f].shape.length)}; `}),o+=`outShape : ${wn(t.shape.length)} ; `;let i=t.shape.length-1;o+=`
|
|
outShapeStrides: ${wn(i)}; `,n.size&&(o+="size : i32; "),n.uniforms&&(o+=n.uniforms),o+="};",a.push(o),n.atomic?a.push(`
|
|
struct Matrix0 {
|
|
numbers: array<atomic<i32>>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, read_write> result : Matrix0;
|
|
`):a.push(`
|
|
struct Matrix0 {
|
|
numbers: array<${r0(t.dtype,n.isVec4)}>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
|
|
`),n.variableNames.forEach((h,f)=>{a.push(`
|
|
struct Matrix${1+f} {
|
|
numbers: array<${r0(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]=Dle(t.shape,n.dispatchLayout),u=C4(t.shape),d=[S4,a.join(`
|
|
`),I4,u,l,Nle(t.shape.length)];if(n.atomic||d.push(Ele(t.shape,t.dtype,n.isVec4)),c===t.shape.length){let h=e.map(f=>Rle(f,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
|
|
`);d.push(h)}return d.push(n.getUserCode()),d.join(`
|
|
`)}var S4=`
|
|
var<private> localId: vec3<u32>;
|
|
var<private> globalId: vec3<u32>;
|
|
var<private> numWorkgroups: vec3<u32>;
|
|
|
|
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
|
|
var res: i32 = a / b;
|
|
let mod: i32 = a % b;
|
|
if (sign < 0. && mod != 0) {
|
|
res = res - 1;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
fn isNanCustom(val : f32) -> bool {
|
|
if (val > 0.0) {
|
|
return false;
|
|
}
|
|
if (val < 0.0) {
|
|
return false;
|
|
}
|
|
if (val == 0.0) {
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
fn isNanCustomVec4(val : vec4<f32>) -> vec4<bool> {
|
|
return vec4<bool>(isNanCustom(val[0]), isNanCustom(val[1]), isNanCustom(val[2]), isNanCustom(val[3]));
|
|
}
|
|
|
|
// Checks whether coordinates lie within the bounds of the shape.
|
|
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
|
|
return all(coord >= vec4<i32>(0)) &&
|
|
all(coord < shape);
|
|
}
|
|
|
|
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
|
|
return all(coord >= vec3<i32>(0)) &&
|
|
all(coord < shape);
|
|
}
|
|
|
|
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
|
|
return all(coord >= vec2<i32>(0)) &&
|
|
all(coord < shape);
|
|
}
|
|
`,I4=`
|
|
fn getFlatIndex1D(coord : i32, shape : i32) -> i32 {
|
|
return coord;
|
|
}
|
|
|
|
fn getFlatIndex2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
|
|
return dot(coords, vec2<i32>(shape.y, 1));
|
|
}
|
|
|
|
fn getFlatIndex3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
|
|
return dot(coords, vec3<i32>(shape.y * shape.z, shape.z, 1));
|
|
}
|
|
|
|
fn getFlatIndex4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
|
|
return dot(coords, vec4<i32>(
|
|
shape.y * shape.z * shape.w, shape.z * shape.w, shape.w, 1));
|
|
}
|
|
|
|
// Only used when the y/z dimension of workgroup size is 1.
|
|
fn getGlobalIndex() -> i32 {
|
|
if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
|
|
return i32(globalId.x);
|
|
}
|
|
|
|
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
|
|
localId.y * workGroupSizeX + localId.x;
|
|
let workGroupID = (globalId - localId)/vec3<u32>(
|
|
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
|
|
|
|
return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
|
|
workGroupID.y * numWorkgroups.x + workGroupID.x) *
|
|
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
|
|
localInvocationIndex);
|
|
}
|
|
`;function Nle(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 Ele(e,t,n){let s=e.length,r=r0(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}(${o.join(", ")}));
|
|
setOutputFlatI32(flatIndex, value);
|
|
}
|
|
`}return a}function Rle(e,t,n,s){let r=_le(e,n);return e.shape.length<=t.length&&(r+=$le(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 $le(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 Dle(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoords() -> ${wn(a)}{
|
|
let globalIndex = getGlobalIndex();
|
|
return getCoordsFromFlatIndex(globalIndex);
|
|
}
|
|
`,a];let o="",i=[n,s,r],l=0;for(let p=0;p<i.length;p++){let h=i[p];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${p}]);`;else{let f=Cle(h,"uniforms.outShape");o+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${p} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${p} - d${h[m]} * ${f[m]};`:o+=`index${p} = index${p} - d${h[m]} * ${f[m]};`}}let c=[];for(let p=0;p<l;p++)c.push(`d${p}`);let u=wn(l),d=`fn getOutputCoords() -> ${u} {
|
|
${o}
|
|
`;return c.length===0?d+=`return ${u}(0); }`:d+=`return ${u}(${c.join(",")}); }`,[d,l]}function C4(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 T4={};Me(T4,{ArrayBufferToTypedArray:()=>N4,GPUBytesPerElement:()=>$x,computeDispatch:()=>Oe,computeWorkGroupSizeForConv2d:()=>Ex,computeWorkGroupSizeForMatMul:()=>Rx,computeWorkPerThreadForConv2d:()=>_x,flatDispatchLayout:()=>He,isWebGPUSupported:()=>Dx,tilesFitEvenlyIntoShape:()=>la});var xc=65535,Dl=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function la(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 Ex(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 Rx(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function _x(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 $x(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function N4(e,t){if(t==="float32")return new Float32Array(e);if(t==="int32")return new Int32Array(e);if(t==="bool"||t==="string"){let n=new Int32Array(e),s=new ArrayBuffer(n.length),r=new Uint8Array(s);for(let a=0;a<n.length;a++)r[a]=n[a];return r}else throw new Error(`Unknown dtype ${t}`)}function Dx(){return!!navigator.gpu}var jt=(e=>(e[e.MUL=0]="MUL",e[e.ADD=1]="ADD",e[e.SUB=2]="SUB",e[e.DIV=3]="DIV",e[e.EQUAL=4]="EQUAL",e[e.GREATER=5]="GREATER",e[e.GREATER_EQUAL=6]="GREATER_EQUAL",e[e.LESS=7]="LESS",e[e.LESS_EQUAL=8]="LESS_EQUAL",e[e.LOGICAL_AND=9]="LOGICAL_AND",e[e.NOT_EQUAL=10]="NOT_EQUAL",e[e.SQUARED_DIFFERENCE=11]="SQUARED_DIFFERENCE",e[e.INT_DIV=12]="INT_DIV",e[e.POW=13]="POW",e[e.PRELU=14]="PRELU",e[e.MAX=15]="MAX",e[e.MIN=16]="MIN",e[e.COMPLEX_MULTIPLY_REAL=17]="COMPLEX_MULTIPLY_REAL",e[e.COMPLEX_MULTIPLY_IMAG=18]="COMPLEX_MULTIPLY_IMAG",e))(jt||{}),Fle="return a + b;",Ple="return areal * breal - aimag * bimag;",Ole="return areal * bimag + aimag * breal;",Mle="return a / b;",zle="return a * b;",Lle="return (a - b) * (a - b);",Ble="return a - b;",Wle="return f32(a == b);",Vle="return vec4<f32>(a == b);",Ule="return f32(a > b);",Gle="return vec4<f32>(a > b);",Hle="return f32(a >= b);",jle="return vec4<f32>(a >= b);",qle="return f32(a < b);",Xle="return vec4<f32>(a < b);",Kle="return f32(a <= b);",Zle="return vec4<f32>(a <= b);",Yle="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Jle=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
|
|
vec4<f32>(b >= vec4<f32>(1.0)));`,Qle=`
|
|
if (isNanCustom(a)) { return a; }
|
|
if (isNanCustom(b)) { return b; }
|
|
`,E4=`
|
|
if (isNaN.r) {
|
|
resultTemp.r = uniforms.NAN;
|
|
}
|
|
if (isNaN.g) {
|
|
resultTemp.g = uniforms.NAN;
|
|
}
|
|
if (isNaN.b) {
|
|
resultTemp.b = uniforms.NAN;
|
|
}
|
|
if (isNaN.a) {
|
|
resultTemp.a = uniforms.NAN;
|
|
}
|
|
`,eue=`
|
|
let s = sign(a) * sign(b);
|
|
let ia = i32(round(a));
|
|
let ib = i32(round(b));
|
|
return f32(idiv(ia, ib, s));
|
|
`,tue=`
|
|
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);
|
|
`,nue="return f32(a != b);",sue="return vec4<f32>(a != b);",rue=`
|
|
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);
|
|
`,aue=`
|
|
let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
|
|
let isModRound1 = vec4<f32>(isModRound1Bool);
|
|
let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
|
|
var resultTemp = multiplier * pow(abs(a), b);
|
|
|
|
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
|
|
let isExpZero = b == vec4<f32>(0.0);
|
|
if (isExpZero.r) {
|
|
resultTemp.r = 1.0;
|
|
}
|
|
if (isExpZero.g) {
|
|
resultTemp.g = 1.0;
|
|
}
|
|
if (isExpZero.b) {
|
|
resultTemp.b = 1.0;
|
|
}
|
|
if (isExpZero.a) {
|
|
resultTemp.a = 1.0;
|
|
}
|
|
let isNaN = a < vec4<f32>(0.0) & floor(b) < b;
|
|
${E4}
|
|
return resultTemp;
|
|
`,oue="if (a < 0.0) { return b * a; } return a;",iue=`
|
|
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
|
|
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
|
|
`;function R4(e,t){let n=t?E4:Qle;return t?`
|
|
var resultTemp = vec4<f32>(${e}(a, b));
|
|
let isNaN = isNanCustomVec4(a) | isNanCustomVec4(b);
|
|
`+n+`
|
|
return resultTemp;
|
|
`:n+`
|
|
return ${e}(a, b);
|
|
`}function Ep(e,t){switch(e){case 0:return zle;case 1:return Fle;case 2:return Ble;case 3:return Mle;case 4:return t?Vle:Wle;case 5:return t?Gle:Ule;case 6:return t?jle:Hle;case 7:return t?Xle:qle;case 8:return t?Zle:Kle;case 9:return t?Jle:Yle;case 10:return t?sue:nue;case 11:return Lle;case 12:return t?tue:eue;case 14:return t?iue:oue;case 15:return R4("max",t);case 16:return R4("min",t);case 13:return t?aue:rue;case 17:return Ple;case 18:return Ole;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var wt=(e=>(e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.LINEAR=8]="LINEAR",e[e.LOG=9]="LOG",e[e.LOGICAL_NOT=10]="LOGICAL_NOT",e[e.NEG=11]="NEG",e[e.PRELU=12]="PRELU",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.LEAKYRELU=15]="LEAKYRELU",e[e.RSQRT=16]="RSQRT",e[e.SIN=17]="SIN",e[e.SINH=18]="SINH",e[e.SIGMOID=19]="SIGMOID",e[e.SQRT=20]="SQRT",e[e.SQUARE=21]="SQUARE",e[e.TANH=22]="TANH",e[e.TO_INT=23]="TO_INT",e))(wt||{}),lue="return abs(a);",uue="return ceil(a);",cue="return cos(a);",due=`
|
|
let e2x = exp(-a);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,pue="return exp(a) - 1.0;",hue="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",fue=`
|
|
var resFloat = exp(a) - vec4<f32>(1.0);
|
|
if (a.r >= 0.0) {
|
|
resFloat.r = a.r;
|
|
}
|
|
if (a.g >= 0.0) {
|
|
resFloat.g = a.g;
|
|
}
|
|
if (a.b >= 0.0) {
|
|
resFloat.b = a.b;
|
|
}
|
|
if (a.a >= 0.0) {
|
|
resFloat.a = a.a;
|
|
}
|
|
return resFloat;
|
|
`,mue="return exp(a);",gue="return floor(a);",yue="return a;",Aue=`if (a < 0.0) { return 1.0/0.0; }
|
|
return log(a);`,xue="return f32(!(a >= 1.0));",bue="return -a;",vue="return (a < 0.0) ? b * a : a;",wue="if (a < 0.0) { return uniforms.alpha * a; } return a;",kue="return max(a, 0.0);",Sue="return clamp(a, 0.0, 6.0);",Iue="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Cue=`
|
|
var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
|
|
let isNaN = isNanCustomVec4(a);
|
|
|
|
if (isNaN.r) {
|
|
resFloat.r = a.r;
|
|
}
|
|
if (isNaN.g) {
|
|
resFloat.g = a.g;
|
|
}
|
|
if (isNaN.b) {
|
|
resFloat.b = a.b;
|
|
}
|
|
if (isNaN.a) {
|
|
resFloat.a = a.a;
|
|
}
|
|
return resFloat;
|
|
`,Tue="return 1.0/sqrt(a);",Nue="return 1.0 / (1.0 + exp(-1.0 * a));",Eue="return sin(a);",Rue=`
|
|
let e2x = exp(a);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,_ue="return sqrt(a);",$ue="return a * a;",Due=`
|
|
let e2x = exp(-2.0 * abs(a));
|
|
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,Fue="return f32(i32((a)));";function bc(e,t){switch(e){case 0:return lue;case 2:return cue;case 3:return due;case 1:return uue;case 4:return t?fue:hue;case 5:return mue;case 6:return pue;case 7:return gue;case 8:return yue;case 9:return Aue;case 10:return xue;case 11:return bue;case 12:return vue;case 15:return wue;case 13:return t?Cue:kue;case 14:return t?Iue:Sue;case 16:return Tue;case 19:return Nue;case 17:return Eue;case 18:return Rue;case 20:return _ue;case 21:return $ue;case 22:return Due;case 23:return Fue;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function ua(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 Ep(jt.PRELU,t);if(e==="sigmoid")return bc(wt.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function _4(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};
|
|
|
|
${ia()}
|
|
|
|
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 Pue(e){return`
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
let tileSize = ${e[0]*4};
|
|
${ia()}
|
|
let tileCol = i32(localId.x);
|
|
let globalCol = i32(globalId.x);
|
|
let globalRow = i32(globalId.y);
|
|
|
|
let numTiles = (uniforms.dimInner - 1) / tileSize + 1;
|
|
|
|
// Without this initialization strange values show up in acc.
|
|
var acc = vec4<f32>(0.0);
|
|
|
|
// Loop over shared dimension.
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
// Load one tile of A into local memory.
|
|
let colA = t * tileSize / 4 + tileCol;
|
|
mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId);
|
|
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < tileSize / 4; k = k + 1) {
|
|
let rowB = t * tileSize + k * 4;
|
|
let BCached0 = mm_readB(rowB, globalCol, globalId);
|
|
let BCached1 = mm_readB(rowB + 1, globalCol, globalId);
|
|
let BCached2 = mm_readB(rowB + 2, globalCol, globalId);
|
|
let BCached3 = mm_readB(rowB + 3, globalCol, globalId);
|
|
|
|
let ACached = mm_Asub[k];
|
|
acc = acc + BCached0 * ACached.x;
|
|
acc = acc + BCached1 * ACached.y;
|
|
acc = acc + BCached2 * ACached.z;
|
|
acc = acc + BCached3 * ACached.w;
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
|
|
mm_write(globalRow, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var Oue=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=Rx(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${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[la(o,this.aShape.slice(1)),la(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=ua(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?_4([this.vecSize,this.workPerThread,1],this.workGroupSize):Pue(this.workGroupSize)}
|
|
|
|
`}};function Fx(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}>;
|
|
${ia()}
|
|
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 Mue(e){return`
|
|
let TileSize = ${e[0]*4};
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
|
|
${ia()}
|
|
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 $4=class{constructor(e,t,n,s=!1,r=!1,a=null,o=null,i=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.outputShape=t,this.dispatchLayout={x:[2],y:[1],z:[0]};let l=s?e[1]:e[2];this.workGroupSize=Rx(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]));let c=a!=null,u=i!=null;c&&this.variableNames.push("bias"),u&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.transposeA=s,this.transposeB=r,this.addBias=c,this.activation=o,this.hasPreluActivationWeights=u;let d=this.outputShape[2],p=this.transposeB?[this.outputShape[0],d,l]:[this.outputShape[0],l,d];[this.fitA,this.fitB]=this.getShapeFit(p),this.shaderKey=`matMulPacked_${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[la(r,this.aShape.slice(1)),la(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=ua(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?Fx([this.workPerThread,this.workPerThread,1],this.workGroupSize):Mue(this.workGroupSize)}
|
|
`}};function zue(){return`
|
|
var<workgroup> sumValues : array<f32, workGroupSizeX>;
|
|
${ia()}
|
|
let coords = getOutputCoords();
|
|
let batch = coords[0];
|
|
let row = coords[1];
|
|
let col = coords[2];
|
|
var sum = 0.0;
|
|
let Length = uniforms.dimInner;
|
|
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
|
|
let dataA = mm_readA(batch, row, k);
|
|
let dataB = mm_readB(batch, k, col);
|
|
sum = sum + dataA * dataB;
|
|
}
|
|
sumValues[localId.x] = sum;
|
|
workgroupBarrier();
|
|
|
|
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
|
|
currentSize = currentSize / 2u) {
|
|
if (localId.x < currentSize)
|
|
{
|
|
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
|
|
}
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (localId.x == 0u) {
|
|
sum = sumValues[0] + sumValues[1];
|
|
mm_write(batch, row, col, sum);
|
|
}
|
|
}
|
|
`}var Lue=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulReduce_${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=ua(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);
|
|
}
|
|
${zue()}
|
|
`}};function Bue(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.
|
|
${ia()}
|
|
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 >= ${t} && writeCol >= 0) {
|
|
mm_write(writeCol, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var Wue=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${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=ua(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);
|
|
}
|
|
}
|
|
${Bue(this.workGroupSize)}
|
|
`}};function qe(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Vue={kernelName:Mi,backendName:"webgpu",kernelFunc:qe};function Px({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),y=v.sizeFromShape(m),x=v.sizeFromShape(g),b=rl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${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%4==0&&f%4==0&&!n&&!s&&f>=32,_;h*f<=32?_=new Lue([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 Wue(w,k,[P,h,f],a,l,o):$?_=new Oue(w,[P,h,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):_=new $4(w,[P,h,f],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,l,o);let T=[I,E];a&&T.push(a),o&&T.push(o);let O=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],V=r.runWebGPUProgram(_,T,e.dtype,O),X=qe({inputs:{x:V},backend:r,attrs:{shape:b}});R.push(V);for(let z of R)r.disposeData(z.dataId);return X}function Uue(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return Px({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var Gue={kernelName:ko,backendName:"webgpu",kernelFunc:Uue},D4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
|
|
fn binaryOpComplex(
|
|
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
|
|
${Ep(this.op,!1)}
|
|
}
|
|
|
|
${Ke()}
|
|
if(index < uniforms.size) {
|
|
let areal = getARealAtOutCoordsByGlobalIndex(index);
|
|
let aimag = getAImagAtOutCoordsByGlobalIndex(index);
|
|
let breal = getBRealAtOutCoordsByGlobalIndex(index);
|
|
let bimag = getBImagAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
}
|
|
`}},Hue=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=He(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${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 {
|
|
${Ep(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);
|
|
|
|
${t}
|
|
setOutputFlat(flatIndex, binaryOperation(a, b));
|
|
}
|
|
}
|
|
}
|
|
`}},jue=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
|
|
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
|
|
${Ep(this.op,this.isVec4)}
|
|
}
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
let b = getBAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}},F4=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=N.assertAndGetBroadcastShape(t,n),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
|
|
fn binaryOperation(a : f32, b : f32) -> f32 {
|
|
${Ep(this.op,!1)}
|
|
}
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
let b = getBAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}};function P4(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4==0)return new jue(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Hue(e,t,n,a):new F4(e,t,n)}function ar(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var que={kernelName:Xa,backendName:"webgpu",kernelFunc:ar};function vc(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=ar({inputs:{x:s},backend:n}),l=ar({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var Xue={kernelName:ud,backendName:"webgpu",kernelFunc:vc},Rp=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
|
|
fn unaryOperation(a : f32) -> f32 {
|
|
${bc(this.op,!1)}
|
|
}
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, unaryOperation(a));
|
|
}
|
|
}
|
|
`}};function Cn({opType:e,cpuKernelImpl:t,dtype:n}){return({inputs:s,backend:r})=>{let{x:a}=s,o=r,i=n||a.dtype;if(o.shouldExecuteOnCPU([a])&&t!=null){let c=o.tensorMap.get(a.dataId),u=t(c.values,i);return o.makeTensorInfo(a.shape,i,u)}let l=new Rp(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function jn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(n&&o.dtype==="complex64"){let d=l.tensorMap.get(o.dataId),p=l.tensorMap.get(i.dataId),h,f;if(e!==jt.MUL)[h,f]=[[d.complexTensorInfos.real,p.complexTensorInfos.real],[d.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[y,x]=g,A={dataId:y.dataId,dtype:y.dtype,shape:o.shape},b={dataId:x.dataId,dtype:x.dtype,shape:i.shape},w=P4(e,o.shape,i.shape);return l.runWebGPUProgram(w,[A,b],zn(y.dtype,x.dtype))});else{let g=new D4(jt.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),y=new D4(jt.COMPLEX_MULTIPLY_IMAG,o.shape,i.shape),x=[{dataId:d.complexTensorInfos.real.dataId,dtype:d.complexTensorInfos.real.dtype,shape:o.shape},{dataId:d.complexTensorInfos.imag.dataId,dtype:d.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:p.complexTensorInfos.real.dataId,dtype:p.complexTensorInfos.real.dtype,shape:i.shape},{dataId:p.complexTensorInfos.imag.dataId,dtype:p.complexTensorInfos.imag.dtype,shape:i.shape}];h=l.runWebGPUProgram(g,x,"float32"),f=l.runWebGPUProgram(y,x,"float32")}let m=vc({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let c=s||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=P4(e,o.shape,i.shape);return l.runWebGPUProgram(u,[o,i],c)}}var{addImpl:Kue,ceilImpl:Zue,concatImpl:Yue,equalImpl:Jue,expImpl:Que,expm1Impl:ece,floorImpl:tce,gatherNdImpl:nce,gatherV2Impl:sce,greaterEqualImpl:rce,greaterImpl:ace,lessEqualImpl:oce,lessImpl:ice,logImpl:lce,maxImpl:uce,maximumImpl:cce,minimumImpl:dce,multiplyImpl:pce,negImpl:hce,notEqualImpl:fce,prodImpl:mce,rangeImpl:gce,rsqrtImpl:yce,simpleAbsImpl:Ace,sliceImpl:xce,stridedSliceImpl:bce,stringNGramsImpl:vce,subImpl:wce,tileImpl:kce,topKImpl:Sce,transposeImpl:Ice,uniqueImpl:Jye}=Rm,Cce=Cn({opType:wt.ABS,cpuKernelImpl:Ace}),Tce={kernelName:hi,backendName:"webgpu",kernelFunc:Cce},Nce=jn({opSnippet:jt.ADD,cpuKernelImpl:Kue,supportsComplex:!0}),Ece={kernelName:qr,backendName:"webgpu",kernelFunc:Nce},Rce=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 _ce(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return ar({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>zn(i,l)),a=s.map(i=>i.shape),o=new Rce(a);return n.runWebGPUProgram(o,s,r)}var $ce={kernelName:Ra,backendName:"webgpu",kernelFunc:_ce},O4=class{constructor(e,t,n){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="axis : i32; infinityValue : f32;",this.size=!0;let s=[t];N.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r]=N.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.inputShape=e,this.shaderKey=`argMinMax${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]);
|
|
}
|
|
}
|
|
`}},Dce=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]}>;
|
|
${Nx()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(workgroup_id)]] workgroupId : vec3<u32>) {
|
|
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
|
|
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
|
|
let width = uniforms.outShape[0];
|
|
let height = uniforms.outShape[1];
|
|
if (x < width && y < height) {
|
|
tile[localId.y][localId.x] =
|
|
A.numbers[y * width + x];
|
|
}
|
|
workgroupBarrier();
|
|
|
|
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
|
|
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
|
|
if (x < height && y < width) {
|
|
setOutputFlat((y * height + x), tile[localId.x]
|
|
[localId.y]);
|
|
}
|
|
}
|
|
`}},Fce=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=wn(this.outputShape.length),t=Pce(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 Pce(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 Fl(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=Ice(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 Dce(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}let c=new Fce(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}var Oce={kernelName:vo,backendName:"webgpu",kernelFunc:Fl};function Mce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=N.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Fl({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 O4(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 zce={kernelName:_a,backendName:"webgpu",kernelFunc:Mce};function Lce(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=Fl({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 O4(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 Bce={kernelName:du,backendName:"webgpu",kernelFunc:Lce},M4=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});
|
|
}
|
|
}
|
|
`}},z4=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`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let batch = coords[0];
|
|
let d = coords[3];
|
|
|
|
let xRCCorner = coords.yz * uniforms.stride;
|
|
let xRCorner = xRCCorner.x;
|
|
let xCCorner = xRCCorner.y;
|
|
|
|
let value = getX(batch, xRCorner, xCCorner, d);
|
|
setOutputFlat(index, value);
|
|
}
|
|
}
|
|
`}};function Wce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=N.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return ar({inputs:{x:r},backend:n});let d,p=[{type:"int32",data:[u.strideHeight,u.strideWidth]}];return u.filterHeight===1&&u.filterWidth===1?d=new z4(u):(d=new M4(u,"avg"),p.push({type:"int32",data:[u.padInfo.top,u.padInfo.left]},{type:"int32",data:[u.dilationHeight,u.dilationWidth]},{type:"int32",data:[u.inHeight,u.inWidth]},{type:"int32",data:[u.effectiveFilterHeight,u.effectiveFilterWidth]})),n.runWebGPUProgram(d,[r],r.dtype,p)}var Vce={kernelName:$a,backendName:"webgpu",kernelFunc:Wce};function Uce(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Px({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var Gce={kernelName:Da,backendName:"webgpu",kernelFunc:Uce},Hce=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 : ${wn(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=wn(this.rank),t=jce(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${Ox[a]} = uniforms.start[${a}] + coords.${Ox[a]};`),`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
var sourceLoc : ${e};
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
${n.join(`
|
|
`)}
|
|
setOutputFlat(index, getSource(${t}));
|
|
}
|
|
}
|
|
`}},Ox=["x","y","z","w","u","v"];function jce(e){if(e===1)return"sourceLoc";if(e<=6)return Ox.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=xce(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 Hce(i,l),u=[{type:"int32",data:i}];return n.runWebGPUProgram(c,[r],r.dtype,u)}var qce={kernelName:Vi,backendName:"webgpu",kernelFunc:wc},Xce=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=Fl({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},Kce={kernelName:fi,backendName:"webgpu",kernelFunc:Xce},L4=jn({opSnippet:jt.NOT_EQUAL,dtype:"bool",cpuKernelImpl:fce}),Zce={kernelName:Ri,backendName:"webgpu",kernelFunc:L4};function _p(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return ar({inputs:{x:r.complexTensorInfos.real},backend:n})}var Yce={kernelName:Ad,backendName:"webgpu",kernelFunc:_p};function Jce(e,t){let n=new Rp(e.shape,wt.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Mx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return ar({inputs:{x:r},backend:n});let o=Gt(r.shape),i=Mx({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=Mx({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=ar({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Jce(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=L4({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 Qce={kernelName:Fa,backendName:"webgpu",kernelFunc:Mx},ede=Cn({opType:wt.CEIL,cpuKernelImpl:Zue}),tde={kernelName:Pa,backendName:"webgpu",kernelFunc:ede},nde=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);
|
|
}
|
|
}
|
|
`}},sde=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`
|
|
${Ke()}
|
|
if(index < uniforms.size) {
|
|
let value = getAAtOutCoordsByGlobalIndex(index);
|
|
if (isNanCustom(value)) {
|
|
setOutputFlat(index, value);
|
|
return;
|
|
}
|
|
setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal));
|
|
}
|
|
}
|
|
`}};function rde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4==0?i=new nde(r.shape):i=new sde(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var ade={kernelName:Xr,backendName:"webgpu",kernelFunc:rde},ode=class{constructor(e){this.uniforms="",this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${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 a0(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return ar({inputs:{x:r.complexTensorInfos.imag},backend:n})}var ide={kernelName:fd,backendName:"webgpu",kernelFunc:a0};function zx(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=>a0({inputs:{input:x},backend:n})),m=zx(h,t,n),g=zx(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=Yue(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}=lde(e,t,n),i=a.map(h=>h.shape),l=new ode(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 lde(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 B4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=N.computeOutShape(t.map(c=>c.shape),a);if(v.sizeFromShape(o)===0)return n.makeTensorInfo(o,t[0].dtype,[]);let i=t.filter(c=>v.sizeFromShape(c.shape)>0);if(i.length===1)return ar({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return N.assertParamsConsistent(l,a),zx(i,a,n)}var ude={kernelName:mi,backendName:"webgpu",kernelFunc:B4},cde=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[${t}] && d1 >= 0) {
|
|
value = getA(d0, d1, ch);
|
|
}
|
|
}
|
|
setOutputFlat(flatIndex, value);
|
|
}
|
|
}
|
|
}
|
|
`}};function W4({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let l=e.shape,c=n.dataFormat==="channelsLast",u=!1,d=!1,p=c?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],h=qe({inputs:{x:e},backend:s,attrs:{shape:[1,p,n.inChannels]}}),f=qe({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),m=Px({a:h,b:f,transposeA:u,transposeB:d,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=qe({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function dde({x:e,filter:t,convInfo:n,backend:s,bias:r=null,preluActivationWeights:a=null,leakyreluAlpha:o=0,activation:i=null}){let{filterWidth:l,filterHeight:c,inChannels:u,strideWidth:d,strideHeight:p,padInfo:h,outWidth:f,outHeight:m,dilationWidth:g,dilationHeight:y,dataFormat:x}=n,A=x==="channelsLast",b=l*c*u,w=m*f,k=[w,b],I=!1,E=!1,R=[],P=qe({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),$=qe({inputs:{x:t},backend:s,attrs:{shape:[1,b,-1]}});R.push(P),R.push($);let _=new cde(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),V=qe({inputs:{x:O},backend:s,attrs:{shape:[1,k[0],k[1]]}});R.push(O),R.push(V);let X=[1,k[0],k[1]],z=new $4(X,[1,w,n.outChannels],Y().get("WEBGPU_MATMUL_WORK_PER_THREAD"),I,E),j=X[1],W=X[2],Q=n.outChannels,ne=[{type:"int32",data:[j]},{type:"int32",data:[Q]},{type:"int32",data:[W]}],ae=s.runWebGPUProgram(z,[V,$],V.dtype,ne),Z=A?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],oe=qe({inputs:{x:ae},backend:s,attrs:{shape:Z}});R.push(ae);for(let re of R)s.disposeData(re.dataId);return oe}var V4=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}_${this.fitB}`}getShapeFit(e){let t=this.workGroupSize[1]*e[1],n=this.workGroupSize[0]*e[0],s=n,r=[t,s],a=[s,n],o=this.outputShape[1]*this.outputShape[2],i=this.outputShape[3],l=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[la(r,[o,l]),la(a,[l,i])]}getSampleAWithRemainder(e){return`let flatIndex${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=_4([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=ua(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);
|
|
}
|
|
}
|
|
${t}
|
|
`}},U4=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=Ex(this.dispatchLayout,this.outputShape),this.elementsPerThread=_x(this.dispatchLayout,this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[la(s,[a,i]),la(r,[i,o])]}getUserCode(){let e=Fx(this.elementsPerThread,this.workGroupSize),t=`
|
|
let outRow = row / uniforms.outShape[2];
|
|
let outCol = row % uniforms.outShape[2];
|
|
|
|
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
|
|
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
|
|
let coord = vec4<i32>(
|
|
batch,
|
|
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
|
|
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
|
|
col % uniforms.xShape[3]);
|
|
// The bounds checking is always needed since we use it to pad zero for the
|
|
// 'same' padding type.
|
|
if(coordsInBounds4D(coord, uniforms.xShape)) {
|
|
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
|
|
}
|
|
return 0.0;`,n=this.fitA?`${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=ua(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;
|
|
}
|
|
${e}
|
|
`}},G4=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=ua(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);
|
|
}
|
|
}
|
|
|
|
${ia()}
|
|
let coords = getOutputCoords();
|
|
let batch = coords[0];
|
|
let outChannel = coords[3];
|
|
|
|
var acc = 0.0;
|
|
|
|
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
|
|
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
|
|
for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
|
|
let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
|
|
let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
|
|
let v = readInp(batch, coordRow, coordCol, xChannel);
|
|
let f = readFilt(row, col, xChannel, outChannel);
|
|
acc = acc + v * f;
|
|
}
|
|
}
|
|
}
|
|
|
|
writeResult(batch, coords[1], coords[2], outChannel, acc);
|
|
}
|
|
`}};function pde(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 W4({x:r,filter:a,convInfo:p,backend:s});if(Y().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&r.shape[0]===1)return dde({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 G4(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new V4(p):h=new U4(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 hde={kernelName:Oa,backendName:"webgpu",kernelFunc:pde},fde=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=Ex(this.dispatchLayout,this.outputShape),this.elementsPerThread=_x(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;
|
|
}
|
|
|
|
${Fx(this.elementsPerThread,this.workGroupSize)}
|
|
`}},mde=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${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 (${this.isChannelsLast}) {
|
|
let xValue = getDy(batch, idyR, idyC, d2);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
} else {
|
|
let xValue = getDy(batch, d2, idyR, idyC);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutputFlat(index, dotProd);
|
|
}
|
|
}
|
|
`}};function gde(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=N.convertConv2DDataFormat(c),p=N.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(Y().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new mde(p);else{f=new fde(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],y=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[y]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var yde={kernelName:Ma,backendName:"webgpu",kernelFunc:gde},Ade=Cn({opType:wt.COS}),xde={kernelName:za,backendName:"webgpu",kernelFunc:Ade},bde=Cn({opType:wt.COSH}),vde={kernelName:La,backendName:"webgpu",kernelFunc:bde},wde=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${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);
|
|
}
|
|
}
|
|
}
|
|
`}},kde=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 wde(r.shape[3],a.shape,i,l),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(u,[r,a,o],"float32",d)},Sde={kernelName:yi,backendName:"webgpu",kernelFunc:kde},Ide=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 = ${this.getInputSamplingString()};
|
|
setOutputFlat(index, rlt);
|
|
}
|
|
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Cde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new Ide(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Tde={kernelName:Ai,backendName:"webgpu",kernelFunc:Cde},H4=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=ua(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}
|
|
|
|
${Nx()}
|
|
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}
|
|
${t}
|
|
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
|
|
}
|
|
}
|
|
}
|
|
`}},j4=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms=`pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;
|
|
inDims : vec2<i32>; filterHeight : i32; filterWidth : i32;
|
|
channelMul : i32;`,this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=ua(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);
|
|
}
|
|
}
|
|
|
|
${ia()}
|
|
let coords = getOutputCoords();
|
|
let batch = coords[0];
|
|
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
|
|
let d2 = coords[3];
|
|
let d1 = d2 / uniforms.channelMul;
|
|
let q = d2 - d1 * uniforms.channelMul;
|
|
|
|
let inputRowStart = xRCCorner.x;
|
|
let inputColStart = xRCCorner.y;
|
|
let inputRowEnd = inputRowStart + uniforms.filterHeight *
|
|
uniforms.dilation[0];
|
|
let inputColEnd = inputColStart + uniforms.filterWidth *
|
|
uniforms.dilation[1];
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
var dotProd = 0.0;
|
|
|
|
// Extract if checking out of for loop for performance.
|
|
if (inputRowStart >= 0 && inputColStart >= 0 &&
|
|
inputRowEnd < uniforms.inDims[0] &&
|
|
inputColEnd < uniforms.inDims[1]) {
|
|
// Here using a constant value |this.convInfo.filterHeight| instead
|
|
// of uniform value is in order to loop unrolling.
|
|
for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
|
|
let xR = inputRowStart + wR * uniforms.dilation[0];
|
|
|
|
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
|
|
let xC = inputColStart + wC * uniforms.dilation[1];
|
|
|
|
let xVal = getX(batch, xR, xC, d1);
|
|
let wVal = getW(wR, wC, d1, q);
|
|
dotProd = dotProd + xVal * wVal;
|
|
}
|
|
}
|
|
} else {
|
|
for (var wR = 0; wR < uniforms.filterHeight; wR = wR + 1) {
|
|
let xR = inputRowStart + wR * uniforms.dilation[0];
|
|
|
|
if (xR < 0 || xR >= uniforms.inDims[0]) {
|
|
continue;
|
|
}
|
|
|
|
for (var wC = 0; wC < uniforms.filterWidth; wC = wC + 1) {
|
|
let xC = inputColStart + wC * uniforms.dilation[1];
|
|
|
|
if (xC < 0 || xC >= uniforms.inDims[1]) {
|
|
continue;
|
|
}
|
|
|
|
let xVal = getX(batch, xR, xC, d1);
|
|
let wVal = getW(wR, wC, d1, q);
|
|
dotProd = dotProd + xVal * wVal;
|
|
}
|
|
}
|
|
}
|
|
|
|
${n}
|
|
${t}
|
|
writeResult(batch, coords[1], coords[2], d2, dotProd);
|
|
}
|
|
`}};function Nde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]);let d=N.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}],h;return d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?h=new H4(d):(h=new j4(d),p.push({type:"int32",data:[d.filterHeight]},{type:"int32",data:[d.filterWidth]},{type:"int32",data:[d.outChannels/d.inChannels]})),n.runWebGPUProgram(h,[r,a],r.dtype,p)}var Ede={kernelName:Ba,backendName:"webgpu",kernelFunc:Nde},q4=jn({opSnippet:jt.MUL,cpuKernelImpl:pce,supportsComplex:!0}),Rde={kernelName:ro,backendName:"webgpu",kernelFunc:q4},_de=class{constructor(e,t){this.workGroupSize=[64,1,1],this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.size=!0,this.inputShape=[e.batchSize,e.inSize];let[n]=N.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=n.length===0?[1]:n,this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,[1,1,1]),this.reduceType=t,this.shaderKey=`reduce_${t}`}getUserCode(){let e="",t="0.0";this.reduceType==="min"||this.reduceType==="max"?(e=`
|
|
if (isNanCustom(candidate)) {
|
|
bestValue = uniforms.NAN;
|
|
} elseif (!isNanCustom(bestValue) && candidate ${this.reduceType==="min"?"<":">"} bestValue)
|
|
{ bestValue = candidate; }`,t="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?e=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(e=" bestValue = bestValue * candidate; ",t="1.0");let n=this.reduceType==="mean"?"setOutputFlat(outputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputFlat(outputIndex, bestValue);";return`
|
|
fn DIV_CEIL(a : u32, b : u32) -> u32 {
|
|
return ((a - 1u) / b + 1u);
|
|
}
|
|
|
|
${`
|
|
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
|
|
`}
|
|
fn getOffset(outputIndex : i32) -> i32 {
|
|
let outputCoords = getCoordsFromFlatIndex(outputIndex);
|
|
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
|
|
return offset;
|
|
}
|
|
${Ke()}
|
|
let outputIndex = index / i32(workGroupSizeX);
|
|
let offset = getOffset(outputIndex);
|
|
var bestValue = ${t};
|
|
let Length = uniforms.reduceSize;
|
|
let WorkPerThread = DIV_CEIL(u32(Length), workGroupSizeX);
|
|
for (var k = i32(localId.x); k < Length && outputIndex < uniforms.size;
|
|
k = k + i32(workGroupSizeX)) {
|
|
let candidate = f32(x.numbers[offset + k]);
|
|
${e}
|
|
}
|
|
xBestValues[localId.x] = bestValue;
|
|
workgroupBarrier();
|
|
|
|
var reduceSize = min(u32(Length), workGroupSizeX);
|
|
for (var currentSize = reduceSize / 2u; reduceSize > 1u;
|
|
currentSize = reduceSize / 2u) {
|
|
let interval = DIV_CEIL(reduceSize, 2u);
|
|
if (localId.x < currentSize) {
|
|
let candidate = xBestValues[localId.x + interval];
|
|
${e}
|
|
xBestValues[localId.x] = bestValue;
|
|
}
|
|
reduceSize = interval;
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (localId.x == 0u && outputIndex < uniforms.size) {
|
|
${n}
|
|
}
|
|
}
|
|
`}};function $p(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,c=N.getAxesPermutation(l,a),u=e;c!=null&&(u=Fl({inputs:{x:e},attrs:{perm:c},backend:r}),l=N.getInnerMostAxes(l.length,a),o.push(u)),N.assertAxesAreInnerMostDims(s,l,a);let[d,p]=N.computeOutAndReduceShapes(u.shape,l),h=d;n&&(h=N.expandShapeToKeepDim(d,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([u])){let m=r.tensorMap.get(u.dataId).values;switch(s){case"max":let g=uce(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:y,outShape:x,outDtype:A}=mce(u.shape,u.dtype,m,l);f=r.makeTensorInfo(x,A,y);break;default:throw new Error(`${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 _de(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 Lx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return $p(r,a,o,"sum",n)}var $de={kernelName:go,backendName:"webgpu",kernelFunc:Lx};function Dde(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=N.decodeEinsumEquation(r,a.length);N.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=N.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:y,expandDims:x}=N.getEinsumPermutation(h,l[g]),A;N.isIdentityPermutation(y)?A=a[g]:(A=Fl({inputs:{x:a[g]},backend:n,attrs:{perm:y}}),f.push(A));let b=A.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(A.shape,b)||(A=qe({inputs:{x:A},backend:n,attrs:{shape:b}}),f.push(A)),p===null?p=A:(p=q4({inputs:{a:A,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Lx({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeData(m.dataId);return p}var Fde={kernelName:hd,backendName:"webgpu",kernelFunc:Dde},Pde=Cn({opType:wt.ELU}),Ode={kernelName:Va,backendName:"webgpu",kernelFunc:Pde},Mde=jn({opSnippet:jt.EQUAL,dtype:"bool",cpuKernelImpl:Jue}),zde={kernelName:xi,backendName:"webgpu",kernelFunc:Mde},X4=Cn({opType:wt.EXP,cpuKernelImpl:Que,dtype:"float32"}),Lde={kernelName:Ua,backendName:"webgpu",kernelFunc:X4};function Bx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),qe({inputs:{x:a},backend:s,attrs:{shape:i}})}var Bde={kernelName:bi,backendName:"webgpu",kernelFunc:Bx},Wde=Cn({opType:wt.EXPM1,cpuKernelImpl:ece}),Vde={kernelName:vi,backendName:"webgpu",kernelFunc:Wde},Ude=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 Ude(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var Gde={kernelName:Au,backendName:"webgpu",kernelFunc:kc},Hde=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);
|
|
}
|
|
}
|
|
`}},jde={kernelName:wi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Hde(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},qde=Cn({opType:wt.FLOOR,cpuKernelImpl:tce}),Xde={kernelName:Ga,backendName:"webgpu",kernelFunc:qde},Kde=jn({opSnippet:jt.INT_DIV,dtype:"int32"}),Zde={kernelName:Ha,backendName:"webgpu",kernelFunc:Kde},Yde=(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}))})},K4=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=Tle(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 Z4(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 Y4(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=Z4(u,d,p),f=u.getLayout(n.device),m=n.getAndSavePipeline(h,()=>K4(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 Jde={kernelName:Id,backendName:"webgpu",kernelFunc:Qde},Sc;function Qde(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 Y4({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 Y4({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 epe=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 = ${t};
|
|
let inv = scaleValue * inverseSqrt(varianValue + f32(uniforms.varianceEpsilon));
|
|
setOutputFlat(index,dot(vec3<f32>(xValue, -meanValue, offsetValue), vec3<f32>(inv, inv, 1.0)));
|
|
}
|
|
}
|
|
`}},tpe={kernelName:ja,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:s,scale:r,offset:a,mean:o,variance:i}=e,{varianceEpsilon:l}=t,c=n,u=[s,o,i],d=null;a!=null&&(d=a.shape,u.push(a));let p=null;r!=null&&(p=r.shape,u.push(r));let h=new epe(s.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[l]}];return c.runWebGPUProgram(h,u,s.dtype,f)}};function npe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dataFormat:u,dilations:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=s,m=N.convertConv2DDataFormat(u),g=N.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),y=o!=null,x=i!=null,A;if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))return W4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});let b=Y().getBool("WEBGPU_USE_NAIVE_CONV2D"),w=g.inChannels%4==0&&g.outChannels%4==0,k=[g.padInfo.top,g.padInfo.left],I=[{type:"int32",data:[g.filterHeight,g.filterWidth]},{type:"int32",data:[...k]},{type:"int32",data:[g.strideHeight,g.strideWidth]},{type:"int32",data:[g.dilationHeight,g.dilationWidth]}];if(b)A=new G4(g,y,h,x);else{w?A=new V4(g,y,h,x):A=new U4(g,y,h,x);let R=g.outShape[1]*g.outShape[2],P=g.outShape[3],$=g.filterHeight*g.filterWidth*g.inShape[3];I.push({type:"int32",data:[R]},{type:"int32",data:[P]},{type:"int32",data:[$]})}let E=[r,a];return y&&E.push(o),x&&E.push(i),n.runWebGPUProgram(A,E,r.dtype,I)}var spe={kernelName:So,backendName:"webgpu",kernelFunc:npe};function rpe(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 H4(f,g,p,y):(A=new j4(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 ape={kernelName:Io,backendName:"webgpu",kernelFunc:rpe},ope=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 ipe(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=nce(x,A,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new ope(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 lpe={kernelName:Si,backendName:"webgpu",kernelFunc:ipe},upe=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=cpe(this.aShape,"i32");return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function cpe(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 J4(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=sce(I,b,f);return d.forEach(R=>n.disposeData(R.dataId)),n.makeTensorInfo(c.outputShape,E.dtype,E.values)}let m=new upe(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 dpe={kernelName:ki,backendName:"webgpu",kernelFunc:J4},ppe=jn({opSnippet:jt.GREATER,cpuKernelImpl:ace,dtype:"bool"}),hpe={kernelName:Ii,backendName:"webgpu",kernelFunc:ppe},fpe=jn({opSnippet:jt.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:rce}),mpe={kernelName:qa,backendName:"webgpu",kernelFunc:fpe},gpe=jn({opSnippet:jt.LESS,dtype:"bool",cpuKernelImpl:ice}),ype={kernelName:Ci,backendName:"webgpu",kernelFunc:gpe},Ape=jn({opSnippet:jt.LESS_EQUAL,dtype:"bool",cpuKernelImpl:oce}),xpe={kernelName:Ti,backendName:"webgpu",kernelFunc:Ape},bpe=Cn({opType:wt.LOG,cpuKernelImpl:lce}),vpe={kernelName:Za,backendName:"webgpu",kernelFunc:bpe},wpe=jn({opSnippet:jt.LOGICAL_AND,dtype:"bool"}),kpe={kernelName:Ni,backendName:"webgpu",kernelFunc:wpe},Spe=Cn({opType:wt.LOGICAL_NOT}),Ipe={kernelName:ku,backendName:"webgpu",kernelFunc:Spe};function Q4(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return $p(r,a,o,"max",n)}var Cpe={kernelName:Ya,backendName:"webgpu",kernelFunc:Q4},Tpe=jn({opSnippet:jt.MAX,cpuKernelImpl:cce}),Npe={kernelName:Ja,backendName:"webgpu",kernelFunc:Tpe};function Epe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=N.computePool2DInfo(r.shape,a,o,c,i,l),d,p=[];if(u.filterHeight===1&&u.filterWidth===1){if(v.arraysEqual(u.inShape,u.outShape))return ar({inputs:{x:r},backend:n});d=new z4(u),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else d=new M4(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 Rpe={kernelName:Qa,backendName:"webgpu",kernelFunc:Epe};function _pe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return $p(r,o,a,"mean",n)}var $pe={kernelName:eo,backendName:"webgpu",kernelFunc:_pe};function Dpe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return $p(r,a,o,"min",n)}var Fpe={kernelName:to,backendName:"webgpu",kernelFunc:Dpe},Ppe=jn({opSnippet:jt.MIN,cpuKernelImpl:dce}),Ope={kernelName:no,backendName:"webgpu",kernelFunc:Ppe},Mpe=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=He(this.outputShape),this.dispatch=Oe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${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}));
|
|
}
|
|
}
|
|
`}},zpe={kernelName:so,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 Mpe(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function Lpe(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=hce(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new Rp(s.shape,wt.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var Bpe={kernelName:Ei,backendName:"webgpu",kernelFunc:Lpe};function Wpe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l}=s,c=n.readSync(r.dataId),u=n.readSync(a.dataId),{selectedIndices:d}=er.nonMaxSuppressionV3Impl(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Vpe={kernelName:_i,backendName:"webgpu",kernelFunc:Wpe};function Upe(e){console.warn("tf.nonMaxSuppression() in webgpu locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:s}=e,{boxes:r,scores:a}=t,{maxOutputSize:o,iouThreshold:i,scoreThreshold:l,softNmsSigma:c}=s,u=n.readSync(r.dataId),d=n.readSync(a.dataId),p=o,h=i,f=l,m=c,{selectedIndices:g,selectedScores:y}=er.nonMaxSuppressionV5Impl(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var Gpe={kernelName:$i,backendName:"webgpu",kernelFunc:Upe};function o0(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=_p({inputs:{input:s},backend:n}),a=o0({inputs:{x:r},backend:n}),o=a0({inputs:{input:s},backend:n}),i=o0({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 Hpe={kernelName:Yi,backendName:"webgpu",kernelFunc:o0};function e6(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=e6({inputs:{x:r},backend:n}),o=a0({inputs:{input:s},backend:n}),i=o0({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 jpe={kernelName:Di,backendName:"webgpu",kernelFunc:e6};function qpe(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Bx({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=Bx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=B4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var Xpe={kernelName:Pi,backendName:"webgpu",kernelFunc:qpe},Kpe=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}));
|
|
}
|
|
}
|
|
}
|
|
`}},t6=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{paddings:a,constantValue:o}=s;if(a.every(c=>v.arraysEqual(c,[0,0])))return ar({inputs:{x:r},backend:n});if(v.sizeFromShape(r.shape)===0){let c=a.map((u,d)=>u[0]+r.shape[d]+u[1]);return kc({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=[{type:"float32",data:[o]}];a.map(c=>i.push({type:"int32",data:[c[0],c[1]]}));let l=new Kpe(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},Zpe={kernelName:ao,backendName:"webgpu",kernelFunc:t6},Ype=jn({opSnippet:jt.POW}),Jpe={kernelName:oo,backendName:"webgpu",kernelFunc:Ype};function Qpe(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new F4(jt.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var ehe={kernelName:io,backendName:"webgpu",kernelFunc:Qpe};function the(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return $p(r,a,o,"prod",n)}var nhe={kernelName:Oi,backendName:"webgpu",kernelFunc:the},she=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=gce(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},rhe={kernelName:Cu,backendName:"webgpu",kernelFunc:she},n6=jn({opSnippet:jt.DIV}),ahe={kernelName:Wa,backendName:"webgpu",kernelFunc:n6},ohe=Cn({opType:wt.RELU}),ihe={kernelName:lo,backendName:"webgpu",kernelFunc:ohe},lhe=Cn({opType:wt.RELU6}),uhe={kernelName:co,backendName:"webgpu",kernelFunc:lhe};function che(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=[{type:"float32",data:[a]}],i=new Rp(r.shape,wt.LEAKYRELU);return i.uniforms="alpha : f32;",n.runWebGPUProgram(i,[r],"float32",o)}var dhe={kernelName:Ka,backendName:"webgpu",kernelFunc:che},phe=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 hhe(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 phe(r.shape,l,c);return n.runWebGPUProgram(f,[r],"float32",h)}var fhe={kernelName:uo,backendName:"webgpu",kernelFunc:hhe},mhe=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 ghe(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 mhe(r.shape,l,c,o);return n.runWebGPUProgram(f,[r],r.dtype,h)}var yhe={kernelName:Nu,backendName:"webgpu",kernelFunc:ghe},Ahe=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));
|
|
${this.fillSnippet}
|
|
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
|
|
coordY < uniforms.xShape[1]) {
|
|
outputValue = getX(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutputFlat(index, outputValue);
|
|
}
|
|
}
|
|
`}},xhe={kernelName:Ji,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Ahe(s.shape,a),[c,u]=N.getImageCenter(o,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,d)}},bhe=Cn({opType:wt.RSQRT,cpuKernelImpl:yce}),vhe={kernelName:po,backendName:"webgpu",kernelFunc:bhe},whe=class{constructor(e,t,n,s,r,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=He(e),this.dispatch=Oe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${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 khe(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 whe(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 She={kernelName:Bi,backendName:"webgpu",kernelFunc:khe},Ihe=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 Che(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new Ihe(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],zn(r.dtype,a.dtype))}var The={kernelName:Wi,backendName:"webgpu",kernelFunc:Che},Nhe=Cn({opType:wt.SIGMOID}),Ehe={kernelName:fo,backendName:"webgpu",kernelFunc:Nhe},Rhe=Cn({opType:wt.SIN}),_he={kernelName:ho,backendName:"webgpu",kernelFunc:Rhe},$he=Cn({opType:wt.SINH}),Dhe={kernelName:Ui,backendName:"webgpu",kernelFunc:$he},s6=jn({opSnippet:jt.SUB,cpuKernelImpl:wce,supportsComplex:!0}),Fhe={kernelName:xo,backendName:"webgpu",kernelFunc:s6};function Phe(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=Q4({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=s6({inputs:{a:r,b:c},backend:n}),d=X4({inputs:{x:u},backend:n}),p=Lx({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=qe({inputs:{x:p},backend:n,attrs:{shape:l}}),f=n6({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 Ohe={kernelName:yo,backendName:"webgpu",kernelFunc:Phe},Mhe=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=t6({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=Fl({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},zhe={kernelName:Gi,backendName:"webgpu",kernelFunc:Mhe},Lhe=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 Bhe(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 Lhe(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 Whe={kernelName:wd,backendName:"webgpu",kernelFunc:Bhe};function Vhe(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 Uhe={kernelName:Hi,backendName:"webgpu",kernelFunc:Vhe},Ghe=Cn({opType:wt.SQRT}),Hhe={kernelName:mo,backendName:"webgpu",kernelFunc:Ghe},jhe={kernelName:Du,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new Rp(n.shape,wt.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},qhe=jn({opSnippet:jt.SQUARED_DIFFERENCE}),Xhe={kernelName:Ao,backendName:"webgpu",kernelFunc:qhe},Khe=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 Zhe(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=bce(h,E,b,x);w=n.makeTensorInfo(f,r.dtype,R.values)}else{let I=new Khe(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 Yhe={kernelName:ji,backendName:"webgpu",kernelFunc:Zhe};function Jhe(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]=vce(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Qhe={kernelName:kd,backendName:"webgpu",kernelFunc:Jhe},efe=Cn({opType:wt.TANH}),tfe={kernelName:bo,backendName:"webgpu",kernelFunc:efe},nfe=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=sfe(this.rank,"uniforms.");return`
|
|
${Ke()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function sfe(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 rfe(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=kce(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new nfe(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var afe={kernelName:Kr,backendName:"webgpu",kernelFunc:rfe},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; 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));
|
|
}
|
|
}
|
|
}
|
|
`}},ife=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 r6(e){let t=1;for(;t<e;)t*=2;return t}function lfe(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]=Sce(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=r6(a),h=r6(l),f=null,m=()=>f===null?[d,d]:[d,f],g=(w,k,I)=>{let E=m(),R=new ofe(I),$=[{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",$),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 ife([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 $=p/2,_=$*2;for(let T=$;T>=1;T/=2)g(_,T,f.shape)}let y=f;f=wc({inputs:{x:f},backend:n,attrs:{begin:0,size:[u,a]}}),Ic(n,y);let x=J4({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 ufe={kernelName:Xi,backendName:"webgpu",kernelFunc:lfe},cfe=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 dfe(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 cfe(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 pfe={kernelName:Ki,backendName:"webgpu",kernelFunc:dfe};function hfe(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 ffe={kernelName:Zi,backendName:"webgpu",kernelFunc:hfe},mfe=[Gue,Tce,Ece,$ce,zce,Bce,Vce,Gce,Kce,Qce,tde,ade,Xue,ude,hde,yde,xde,vde,Sde,Tde,Ede,Fde,Ode,zde,Bde,Lde,Vde,Gde,jde,Jde,Xde,Zde,tpe,spe,ape,lpe,dpe,hpe,mpe,que,ide,ype,xpe,vpe,kpe,Ipe,Cpe,Npe,Rpe,$pe,Fpe,Ope,zpe,Rde,Bpe,Vpe,Gpe,Zce,jpe,Xpe,Zpe,ehe,nhe,Jpe,rhe,Yce,ahe,ihe,uhe,dhe,Vue,fhe,yhe,xhe,vhe,She,The,Ehe,_he,Dhe,qce,Yhe,Qhe,Ohe,zhe,Uhe,Whe,Hhe,jhe,Xhe,Fhe,$de,tfe,afe,ufe,pfe,Oce,ffe,Hpe];for(let e of mfe)cr(e);var gfe=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=a6(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=a6(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 a6(e,t){return`${e}_${t}`}var o6=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}}},yfe=class extends o6{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}}},Afe=Y().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),i6=class extends su{constructor(e,t=!1){super();if(this.commandQueueOwnedIds=new WeakSet,this.tensorDisposalQueue=[],this.uniformDisposalQueue=[],this.disposed=!1,this.uploadWaitMs=0,this.downloadWaitMs=0,this.dispatchNumberInEncoder=0,!Dx())throw new Error("WebGPU is not supported on this device");this.layoutCache={},this.pipelineCache={},this.device=e,this.queue=e.queue,this.currentCommandEncoder=null,this.currentComputePass=null,this.supportTimeQuery=t,this.bufferManager=new gfe(this.device),this.tensorMap=new ad(this,rs()),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 i6.nextDataId++}floatPrecision(){return 32}defaultGpuBufferUsage(){return GPUBufferUsage.STORAGE|GPUBufferUsage.COPY_SRC|GPUBufferUsage.COPY_DST}flushDisposalQueue(){this.tensorDisposalQueue.forEach(e=>{this.maybeReleaseBuffer(e),this.tensorMap.delete(e)}),this.uniformDisposalQueue.forEach(e=>this.bufferManager.releaseBuffer(e.buffer,e.byteSize,e.usage)),this.tensorDisposalQueue=[],this.uniformDisposalQueue=[]}disposeData(e,t=!1){if(this.tensorMap.has(e)){let n=this.tensorMap.get(e);if(n.refCount--,!t&&n.refCount>0)return!1;if(this.commandQueueOwnedIds.has(e))return this.tensorDisposalQueue.push(e),!1;this.maybeReleaseBuffer(e);let{complexTensorInfos:s}=this.tensorMap.get(e);s!=null&&(this.disposeData(s.real.dataId,!0),this.disposeData(s.imag.dataId,!0)),this.tensorMap.delete(e)}return!0}memory(){return{numBytesInGPU:this.bufferManager.numBytesUsed,numBytesAllocatedInGPU:this.bufferManager.numBytesAllocated,unreliable:!1}}getBufferManager(){return this.bufferManager}acquireBuffer(e,t=this.defaultGpuBufferUsage()){return this.bufferManager.acquireBuffer(e,t)}maybeReleaseBuffer(e){let t=this.tensorMap.get(e);t!=null&&t.bufferInfo.buffer!=null&&(this.bufferManager.releaseBuffer(t.bufferInfo.buffer,t.bufferInfo.byteSize,t.bufferInfo.usage),t.bufferInfo.buffer=null)}refCount(e){return this.tensorMap.has(e)?this.tensorMap.get(e).refCount:0}incRef(e){let t=this.tensorMap.get(e);t.refCount++}decRef(e){if(this.tensorMap.has(e)){let t=this.tensorMap.get(e);t.refCount--}}write(e,t,n){if(n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let s={id:this.nextDataId()},r=v.sizeFromShape(t)*$x(n);return n==="bool"&&e instanceof Uint8Array&&(e=Int32Array.from(e)),this.tensorMap.set(s,{dtype:n,values:e,bufferInfo:{byteSize:r,usage:this.defaultGpuBufferUsage()},refCount:1}),s}move(e,t,n,s,r){if(s==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a=v.sizeFromShape(n)*$x(s);this.tensorMap.set(e,{dtype:s,values:t,bufferInfo:{byteSize:a,usage:this.defaultGpuBufferUsage()},refCount:r})}submitQueue(){this.ensureComputePassEnded(),this.queue.submit([this.currentCommandEncoder.finish()]),this.currentCommandEncoder=null,this.dispatchNumberInEncoder=0,this.commandQueueOwnedIds=new WeakSet,this.flushDisposalQueue()}getBuffer(e){return this.uploadToGPU(e),this.tensorMap.get(e).bufferInfo.buffer}getFromPixelsProgram(e){switch(e){case"copyExternal":return this.fromPixelProgram||(this.fromPixelProgram=new o6),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new yfe),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 ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=N.mergeRealAndImagArrays(a,o)}else{let r=await this.getBufferData(t);s=N4(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${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=Z4(e,o,h,g,m),{bindGroupLayout:x,pipelineLayout:A}=this.getCachedOrCreateLayout(e.variableNames.length),b=this.getAndSavePipeline(y,()=>K4(this.device,e,A,p,r)),w=this.activeTimers!=null,k=Yde(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=Afe){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)}},Wx=i6;Wx.nextDataId=0;var l6={};Me(l6,{WebGPUBackend:()=>Wx,webgpu_util:()=>T4});Ou.isBrowser()&&Dx()&&il("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 Wx(r,s)},3);var qt=(e=>(e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64",e))(qt||{}),i0=(e=>(e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu",e))(i0||{}),u6;function xfe(e){u6=e.wasm.cwrap(ko,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function bfe(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 ${E.shape.length}.`);f=E.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=i0[u];if(g==null)throw new Error(`${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=rl.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 u6(p,k,r.shape.length,h,I,a.shape.length,l,c,g,f,m,d||0,w),b}var vfe={kernelName:ko,backendName:"wasm",setupFunc:xfe,kernelFunc:bfe};function Tn(e,t){let n;function s(a){n=a.wasm.cwrap(e,null,["number","number","number"])}function r(a){let{backend:o,inputs:{x:i}}=a,l=o.dataIdMap.get(i.dataId).id,c=o.makeOutput(i.shape,t||i.dtype),u=o.dataIdMap.get(c.dataId).id;return v.sizeFromShape(c.shape)===0||n(l,qt[i.dtype],u),c}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var wfe=Tn(hi);function qn(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:c,b:u}=l,d=i.dataIdMap.get(c.dataId).id,p=i.dataIdMap.get(u.dataId).id,h=n!=null?n:c.dtype,f=N.assertAndGetBroadcastShape(c.shape,u.shape),m=i.makeOutput(f,h);if(v.sizeFromShape(f)===0)return m;let g=new Uint8Array(new Int32Array(c.shape).buffer),y=new Uint8Array(new Int32Array(u.shape).buffer),x=i.dataIdMap.get(m.dataId).id;return(()=>s(d,g,c.shape.length,p,y,u.shape.length,qt[c.dtype],x))(),m}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var kfe=!0,Sfe=qn(qr,kfe),c6;function Ife(e){c6=e.wasm.cwrap(Ra,null,["array","number","number","number"])}function Cfe(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 c6(a,r.length,qt[s.dtype],o),s}var Tfe={kernelName:Ra,backendName:"wasm",setupFunc:Ife,kernelFunc:Cfe};function l0(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 Nfe={kernelName:Xa,backendName:"wasm",kernelFunc:l0},d6;function Efe(e){d6=e.wasm.cwrap(vo,null,["number","array","number","number","number","array","number"])}function Cc(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=_fe(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=Rfe(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=l0({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 d6(u,h,l.shape.length,qt[l.dtype],d,p,a.length),c}function Rfe(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function _fe(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:vo,backendName:"wasm",kernelFunc:Cc,setupFunc:Efe};function Ko(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 p6;function Dfe(e){p6=e.wasm.cwrap(uu,null,["number, number, number"])}function Ffe(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}=Ko(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;p6(l,y,A)}if(h&&t.disposeData(u.dataId),a){let A=N.expandShapeToKeepDim(x.shape,p);x.shape=A}return x}var Pfe={kernelName:uu,backendName:"wasm",setupFunc:Dfe,kernelFunc:Ffe},h6;function Ofe(e){h6=e.wasm.cwrap(cu,null,["number, number, number"])}function Mfe(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}=Ko(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;h6(l,y,A)}if(h&&t.disposeData(u.dataId),a){let A=N.expandShapeToKeepDim(x.shape,p);x.shape=A}return x}var zfe={kernelName:cu,backendName:"wasm",setupFunc:Ofe,kernelFunc:Mfe},f6;function Lfe(e){f6=e.wasm.cwrap(_a,null,["number","number","number","number","number"])}function Bfe(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}=Ko(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 f6(i,qt[l.dtype],m,g,f),d&&t.disposeData(c.dataId),h}var Wfe={kernelName:_a,backendName:"wasm",kernelFunc:Bfe,setupFunc:Lfe},m6;function Vfe(e){m6=e.wasm.cwrap($a,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ufe(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}, ${u.dilationWidth}].`);let b=s.makeOutput(u.outShape,"float32"),w=s.dataIdMap.get(b.dataId).id;return m6(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,y,x,A,w),b}var Gfe={kernelName:$a,backendName:"wasm",setupFunc:Vfe,kernelFunc:Ufe};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: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Hfe={kernelName:Mi,backendName:"wasm",kernelFunc:ps},g6;function jfe(e){g6=e.wasm.cwrap(Da,null,["number","array","number","number","array","number","number","number","number"])}function qfe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=a.shape.length,u=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[c-1]:a.shape[c-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[c-2]:a.shape[c-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),y=v.sizeFromShape(m),A=rl.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([p,h]);v.assert(u===d,()=>`Error in matMul: inner shapes (${u}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let b=o?[g,u,p]:[g,p,u],w=i?[y,h,d]:[y,d,h],k=ps({inputs:{x:r},backend:n,attrs:{shape:b}}),I=ps({inputs:{x:a},backend:n,attrs:{shape:w}}),E=n.dataIdMap.get(k.dataId).id,R=n.dataIdMap.get(I.dataId).id,P=o?k.shape[2]:k.shape[1],$=i?I.shape[1]:I.shape[2],_=Math.max(g,y),T=n.makeOutput([_,P,$],k.dtype),O=n.dataIdMap.get(T.dataId).id,V=new Uint8Array(new Int32Array(k.shape).buffer),X=new Uint8Array(new Int32Array(I.shape).buffer);return g6(E,V,k.shape.length,R,X,I.shape.length,o,i,O),n.disposeData(k.dataId),n.disposeData(I.dataId),T.shape=A,T}var Xfe={kernelName:Da,backendName:"wasm",setupFunc:jfe,kernelFunc:qfe};function Pl(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=Dm(l,a,o,t.shape,t.dtype);return d.stringBytes=f,c}let p=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)Kfe(l,u[0],p,a,o);else if(h===3)Zfe(l,u[0],u[1],p,a,o);else if(h===4)Yfe(l,u[0],u[1],u[2],p,a,o);else{let f=Dm(l,a,o,t.shape,t.dtype);p.set(f)}return c}function Kfe(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 Zfe(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 Yfe(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 Jfe={kernelName:Vi,backendName:"wasm",kernelFunc:Pl};function Qfe(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=Pl({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 eme={kernelName:fi,backendName:"wasm",kernelFunc:Qfe};function Dp(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 tme={kernelName:Fa,backendName:"wasm",kernelFunc:Dp},nme=Tn(Pa),y6;function sme(e){y6=e.wasm.cwrap(Xr,null,["number","number","number","number"])}function rme(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 y6(i,a,o,c),l}var ame={kernelName:Xr,backendName:"wasm",setupFunc:sme,kernelFunc:rme};function A6(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 l0({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=ZA(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 ome={kernelName:mi,backendName:"wasm",kernelFunc:A6},x6;function ime(e){x6=e.wasm.cwrap(Oa,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function lme(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,$=f.padInfo.type==="SAME"?1:0;if(f.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${f.dataFormat}'. Please use 'channelsLast'.`);let _=s.makeOutput(f.outShape,"float32"),T=s.dataIdMap.get(_.dataId).id;return x6(o,r.shape[0],r.shape[1],r.shape[2],i,m,g,y,x,A,b,$,w,k,I,E,R,P,T),_}var ume={kernelName:Oa,backendName:"wasm",setupFunc:ime,kernelFunc:lme},b6;function cme(e){b6=e.wasm.cwrap(Ma,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 dme(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,$=h.dataFormat==="channelsLast",_=v.computeStrides(h.inShape),T=v.computeStrides(r.shape),[O,V,X]=v.computeStrides(a.shape),z=_[0],j=$?_[1]:_[2],W=$?_[2]:1,Q=$?1:_[1],ne=T[0],ae=$?T[1]:T[2],Z=$?T[2]:1,oe=$?1:T[1],re=t.makeOutput(h.inShape,"float32"),ge=t.dataIdMap.get(re.dataId).id,ve=t.dataIdMap.get(r.dataId).id,Re=t.dataIdMap.get(a.dataId).id;return b6(ve,Re,f,m,g,x,A,y,w,k,b,I,E,R,P,O,V,X,z,j,W,Q,ne,ae,Z,oe,ge),re}var pme={kernelName:Ma,backendName:"wasm",setupFunc:cme,kernelFunc:dme},hme=Tn(za),fme=Tn(La),v6=(e=>(e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest",e))(v6||{}),w6;function mme(e){w6=e.wasm.cwrap(yi,null,["number","number","number","number","array","number","number","number","number","number"])}function gme(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=Dp({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 w6(g,y,x,u,w,d,p,v6[r],a,b),m!=null&&t.disposeData(m.dataId),A}var yme={kernelName:yi,backendName:"wasm",setupFunc:mme,kernelFunc:gme},k6;function Ame(e){k6=e.wasm.cwrap(gi,null,["number","number","number","number","number","number"])}function xme(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;k6(f,o?1:0,i?1:0,h,m,qt[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 bme={kernelName:gi,backendName:"wasm",setupFunc:Ame,kernelFunc:xme},S6;function vme(e){S6=e.wasm.cwrap(Ai,null,["number","number","number","array","number","array","array","number","number"])}function wme(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 S6(y,a,o==="NHWC"?1:0,x,r.shape.length-1,A,b,f.length,w),m}var kme={kernelName:Ai,backendName:"wasm",setupFunc:vme,kernelFunc:wme},I6;function Sme(e){I6=e.wasm.cwrap(Ba,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}=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 $=s.makeOutput(h.outShape,"float32"),_=s.dataIdMap.get($.dataId).id;return I6(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,y,x,A,P,b,w,k,I,E,R,_),$}var Cme={kernelName:Ba,backendName:"wasm",setupFunc:Sme,kernelFunc:Ime},Tme=Tn(Va),Nme=!1,Eme=qn(xi,Nme,"bool"),Rme=Tn(Ua,"float32");function Vx(e){let{inputs:t,attrs:n,backend:s}=e,{input:r}=t,{dim:a}=n,o=r.shape.length,i=r.shape.slice(),l=a;return a<0&&(v.assert(-(o+1)<=a,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+a+1),i.splice(l,0,1),ps({inputs:{x:r},backend:s,attrs:{shape:i}})}var _me={kernelName:bi,backendName:"wasm",kernelFunc:Vx};function C6(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 $me={kernelName:Au,backendName:"wasm",kernelFunc:C6},T6;function Dme(e){T6=e.wasm.cwrap(wi,null,["number","number","number","number","number","number"])}function Fme(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,c,u]=s.shape;return T6(a,i,l,c,u,o),r}var Pme={kernelName:wi,backendName:"wasm",kernelFunc:Fme,setupFunc:Dme},Ome=Tn(Ga),Mme=!1,zme=qn(Ha,Mme),N6;function Lme(e){N6=e.wasm.cwrap(ja,null,["number","number","number","number","number","number","number"])}function Bme(e){let{backend:t,inputs:n,attrs:s}=e,{varianceEpsilon:r}=s,{x:a,mean:o,variance:i,offset:l,scale:c}=n,u=t.dataIdMap.get(a.dataId).id,d=t.dataIdMap.get(o.dataId).id,p=t.dataIdMap.get(i.dataId).id,h=l!=null?t.dataIdMap.get(l.dataId).id:0,f=c!=null?t.dataIdMap.get(c.dataId).id:0,m=t.makeOutput(a.shape,a.dtype);if(v.sizeFromShape(a.shape)===0)return m;let g=t.dataIdMap.get(m.dataId).id;return N6(u,d,p,h,f,r,g),m}var Wme={kernelName:ja,backendName:"wasm",setupFunc:Lme,kernelFunc:Bme},E6;function Vme(e){E6=e.wasm.cwrap(So,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ume(e){let{inputs:t,attrs:n,backend:s}=e,{x:r,filter:a,bias:o,preluActivationWeights:i}=t,{strides:l,pad:c,dilations:u,dataFormat:d,dimRoundingMode:p,activation:h,leakyreluAlpha:f}=n,m=N.computeConv2DInfo(r.shape,a.shape,l,u,c,p),g=i0[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let y=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,A=m.outChannels,b=0;if(o!=null){let Z=s.dataIdMap.get(o.dataId);if(Z.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==A)throw new Error(`FusedConv2D bias shape (${Z.shape}) does not match the number of output channels (${A})`);b=Z.id}let w=m.filterHeight,k=m.filterWidth,I=m.padInfo.top,E=m.padInfo.right,R=m.padInfo.bottom,P=m.padInfo.left,$=m.dilationHeight,_=m.dilationWidth,T=m.strideHeight,O=m.strideWidth,V=m.inChannels,X=m.padInfo.type==="SAME"?1:0,z=m.batchSize,j=m.inHeight,W=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. 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T0e={kernelName:to,backendName:"wasm",setupFunc:I0e,kernelFunc:C0e},N0e=!1,E0e=qn(no,N0e),z6=(e=>(e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric",e))(z6||{}),L6;function R0e(e){L6=e.wasm.cwrap(so,null,["number","array","number","number","array","array","number","number"])}function _0e(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 L6(o,c,t.shape.length,qt[t.dtype],p,h,z6[r],l),i}var $0e={kernelName:so,backendName:"wasm",kernelFunc:_0e,setupFunc:R0e},D0e=!0,F0e=qn(ro,D0e),P0e=Tn(Ei);function Ux(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 B6;function O0e(e){B6=e.wasm.cwrap(_i,"number",["number","number","number","number","number"])}function M0e(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=B6(c,u,a,r,o),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=Ux(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var z0e={kernelName:_i,backendName:"wasm",setupFunc:O0e,kernelFunc:M0e},W6;function L0e(e){W6=e.wasm.cwrap(Iu,"number",["number","number","number","number","number","bool"])}function B0e(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=W6(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=Ux(t,p);t.wasm._free(m);let 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Q0e={kernelName:Pi,backendName:"wasm",kernelFunc:J0e},G6;function ege(e){G6=e.wasm.cwrap(ao,null,["number","array","number","number","array","array","number","number"])}function tge(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,constantValue:r}}=e,a=s.map((m,g)=>m[0]+t.shape[g]+m[1]);if(v.sizeFromShape(t.shape)===0)return C6({backend:n,attrs:{shape:a,value:r,dtype:t.dtype}});let o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),c=n.dataIdMap.get(i.dataId).id,u=new Uint8Array(new Int32Array(t.shape).buffer),d=s.map(m=>m[0]),p=s.map(m=>m[1]),h=new Uint8Array(new Int32Array(d).buffer),f=new Uint8Array(new Int32Array(p).buffer);return G6(o,u,t.shape.length,qt[t.dtype],h,f,r,c),i}var H6={kernelName:ao,backendName:"wasm",kernelFunc:tge,setupFunc:ege},nge=!1,sge=qn(oo,nge),j6;function rge(e){j6=e.wasm.cwrap(io,null,["number","number","number"])}function 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Wge={kernelName:Gi,backendName:"wasm",kernelFunc:Bge},t8;function Vge(e){t8=e.wasm.cwrap("SparseFillEmptyRows","number",["number","number","number","number","number","number","number","number","number","number","number","number"])}function Uge(e){let{backend:t,inputs:n}=e,{indices:s,values:r,denseShape:a,defaultValue:o}=n,i=s.shape[0],l=s.shape[1],c=t.readSync(a.dataId)[0],u=[i+c,l],d=t.dataIdMap.get(s.dataId).id,p=t.dataIdMap.get(r.dataId).id,h=t.dataIdMap.get(o.dataId).id,f=t.makeOutput(u,s.dtype),m=t.dataIdMap.get(f.dataId).id,g=t.makeOutput(u.slice(0,1),r.dtype),y=t.dataIdMap.get(g.dataId).id,x=t.makeOutput([c],"bool"),A=t.dataIdMap.get(x.dataId).id,b=t.makeOutput([i],s.dtype),w=t.dataIdMap.get(b.dataId).id,k=t.makeOutput([4],"int32"),I=t.dataIdMap.get(k.dataId).id,E=t8(d,p,qt[r.dtype],i,c,l,h,m,y,A,w,I),R=t.readSync(k.dataId),P;switch(R[0]){case 1:{P=N.getSparseFillEmptyRowsIndicesDenseShapeMismatch(R[1]);break}case 2:{P=N.getSparseFillEmptyRowsNegativeIndexErrorMessage(R[1],R[2]);break}case 3:P=N.getSparseFillEmptyRowsOutOfRangeIndexErrorMessage(R[1],R[2],R[3]);break;default:P=""}if(t.disposeData(k.dataId),P)throw t.disposeData(f.dataId),t.disposeData(g.dataId),t.disposeData(x.dataId),t.disposeData(b.dataId),new Error(P);let $=f,_=g;return E!==u[0]&&($=Pl({inputs:{x:f},attrs:{begin:0,size:[E,l]},backend:t}),_=Pl({inputs:{x:g},attrs:{begin:0,size:E},backend:t}),t.disposeData(f.dataId),t.disposeData(g.dataId)),[$,_,x,b]}var Gge={kernelName:xd,backendName:"wasm",setupFunc:Vge,kernelFunc:Uge},n8;function Hge(e){n8=e.wasm.cwrap($u,null,["number","number","number","number","number","number","number"])}function jge(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
|
|
${r.shape}`);if(a.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${a.shape}`);let o=t.dataIdMap.get(s.dataId).id,i=t.dataIdMap.get(r.dataId).id,l=t.dataIdMap.get(a.dataId).id,c=s.shape[0],u=v.sizeFromShape(a.shape),d=t.makeOutput([c,u],s.dtype),p=t.dataIdMap.get(d.dataId).id,h=t.makeOutput([u],a.dtype),f=t.dataIdMap.get(h.dataId).id,m=t.makeOutput([3],"int32"),g=t.dataIdMap.get(m.dataId).id;n8(o,i,l,c,p,f,g);let y=t.readSync(m.dataId),x;switch(y[0]){case 0:{x=N.getSparseReshapeMultipleNegativeOneOutputDimErrorMessage(y[1],y[2]);break}case 1:{x=N.getSparseReshapeNegativeOutputDimErrorMessage(y[1],y[2]);break}case 2:x=N.getSparseReshapeEmptyTensorZeroOutputDimErrorMessage();break;case 3:{let A=Array.from(t.readSync(r.dataId)),b=Array.from(t.readSync(h.dataId));x=N.getSparseReshapeInputOutputMultipleErrorMessage(A,b);break}case 4:{let 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h=0;h<u.length;h++)d[a]=h,u[h]=Pl({inputs:{x:r},attrs:{begin:d,size:p},backend:n});return u.map(({dataId:h,dtype:f})=>({dataId:h,dtype:f,shape:l}))}var T2e={kernelName:Zi,backendName:"wasm",kernelFunc:C2e};function N2e(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(0),s}var E2e={kernelName:Yi,backendName:"wasm",kernelFunc:N2e},R2e=[wfe,Sfe,Tfe,Pfe,zfe,Wfe,Gfe,Xfe,eme,tme,nme,ame,ome,ume,pme,hme,fme,yme,bme,kme,Cme,Tme,Eme,Rme,_me,$me,Pme,Ome,zme,vfe,Wme,Gme,qme,Zme,Qme,t0e,s0e,Nfe,o0e,l0e,c0e,d0e,h0e,g0e,A0e,v0e,S0e,T0e,E0e,$0e,F0e,P0e,z0e,W0e,G0e,j0e,K0e,Y0e,Q0e,H6,sge,oge,uge,dge,hge,fge,mge,Hfe,Age,vge,Sge,Cge,Ige,Ege,$ge,Pge,Oge,Jfe,Lge,Wge,Gge,qge,Kge,Yge,Qge,e2e,t2e,s2e,o2e,u2e,d2e,f2e,m2e,g2e,x2e,w2e,I2e,$fe,T2e,E2e];for(let e of R2e)cr(e);var Gx=Y();Gx.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])));Gx.registerFlag("WASM_HAS_MULTITHREAD_SUPPORT",async()=>{if(Gx.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 p8=ci(r9()),_2e='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=ci(a9()),h8=class extends su{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(g8),jx=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new ad(this,rs())}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 P2e(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 f8(e,t,n){if(u0!=null)return u0;let s="tfjs-backend-wasm.wasm";return e&&t?s="tfjs-backend-wasm-threaded-simd.wasm":e&&(s="tfjs-backend-wasm-simd.wasm"),Pp!=null&&Pp[s]!=null?Pp[s]:n+s}async function F2e(){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=_2e,u=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(u)}return i.endsWith(".wasm")?f8(e,t,Fp!=null?Fp:l):l+i},Hx&&(r.instantiateWasm=D2e(f8(e,t,Fp!=null?Fp:"")));let a=!1;r.onAbort=()=>{if(a||Op)return;Op=!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&&u0==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+p8.default.toString()],{type:"text/javascript"}),o=(0,p8.default)(r)):o=(0,$2e.default)(r),o.then(i=>{a=!0,Op=!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 P2e(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 O2e=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],u0=null,Fp=null,Pp={},Op=!1,Hx=!1;function M2e(e,t=!1){if(J2("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Op)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");u0=e,Hx=t}function m8(e,t=!1){if(Op)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")Fp=e;else{Pp=e;let n=O2e.filter(s=>Pp[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.`)}Hx=t}var g8=-1,jx=-1;function z2e(e){g8=e}function L2e(){if(jx===-1)throw new Error("WASM backend not initialized.");return jx}var B2e="0.0.0",W2e=2;il("wasm",async()=>{let{wasm:e}=await F2e();return new h8(e)},W2e);var Zo="3.12.0-20211215",c0={tfjs:Zo,"tfjs-core":Zo,"tfjs-data":Zo,"tfjs-layers":Zo,"tfjs-converter":Zo,"tfjs-backend-cpu":Zo,"tfjs-backend-webgl":Zo,"tfjs-backend-wasm":Zo};var y8=`
|
|
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 A8=`
|
|
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];
|
|
}
|
|
`,x8=`
|
|
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;
|
|
}
|
|
`,b8=`
|
|
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);
|
|
}
|
|
`,v8=`
|
|
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;
|
|
}
|
|
`,w8=`
|
|
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 qx=(e,t,n)=>{let s=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(s,(r,a)=>(n[a]=0,r))},k8=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),qx(n,"attribute",this.attribute);for(let o in this.attribute)this.attribute[o]=this.gl.getAttribLocation(this.id,o);qx(n,"uniform",this.uniform),qx(s,"uniform",this.uniform);for(let o in this.uniform)this.uniform[o]=this.gl.getUniformLocation(this.id,o)}}};function S8(){let e=0,t=null,n=!1,s=-1,r=[null,null],a=[],o=null,i=null,l=Xn(100,100),c={},u={INTERMEDIATE:1},d=l.getContext("webgl");if(this.gl=d,!d){J("filter: cannot get webgl context");return}function p(x,A){if(!(x===l.width&&A===l.height)){if(l.width=x,l.height=A,!o){let b=new Float32Array([-1,-1,0,1,1,-1,1,1,-1,1,0,0,-1,1,0,0,1,-1,1,1,1,1,1,0]);o=d.createBuffer(),d.bindBuffer(d.ARRAY_BUFFER,o),d.bufferData(d.ARRAY_BUFFER,b,d.STATIC_DRAW),d.pixelStorei(d.UNPACK_PREMULTIPLY_ALPHA_WEBGL,!0)}d.viewport(0,0,l.width,l.height),r=[null,null]}}function h(x,A){let b=d.createFramebuffer();d.bindFramebuffer(d.FRAMEBUFFER,b);let w=d.createRenderbuffer();d.bindRenderbuffer(d.RENDERBUFFER,w);let k=d.createTexture();return d.bindTexture(d.TEXTURE_2D,k),d.texImage2D(d.TEXTURE_2D,0,d.RGBA,x,A,0,d.RGBA,d.UNSIGNED_BYTE,null),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MAG_FILTER,d.LINEAR),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MIN_FILTER,d.LINEAR),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_WRAP_S,d.CLAMP_TO_EDGE),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_WRAP_T,d.CLAMP_TO_EDGE),d.framebufferTexture2D(d.FRAMEBUFFER,d.COLOR_ATTACHMENT0,d.TEXTURE_2D,k,0),d.bindTexture(d.TEXTURE_2D,null),d.bindFramebuffer(d.FRAMEBUFFER,null),{fbo:b,texture:k}}function f(x){return r[x]=r[x]||h(l.width,l.height),r[x]}function m(x=0){if(!i)return;let A=null,b=null,w=!1;e===0?A=t:A=f(s).texture||null,e++,n&&!(x&u.INTERMEDIATE)?(b=null,w=e%2==0):(s=(s+1)%2,b=f(s).fbo||null),d.bindTexture(d.TEXTURE_2D,A),d.bindFramebuffer(d.FRAMEBUFFER,b),d.uniform1f(i.uniform.flipY,w?-1:1),d.drawArrays(d.TRIANGLES,0,6)}function g(x){if(c[x])return i=c[x],d.useProgram((i?i.id:null)||null),i;if(i=new k8(d,y8,x),!i)return J("filter: could not get webgl 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l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(pe.browser&&Ys)l=Ys?Ys.fromPixels(e):null;else{c=e.data.length/e.height/e.width;let p=new Uint8Array(e.data.buffer);l=pt(p,[e.height,e.width,c],"int32")}else if((!Tc||cn.width!==Tc.width||cn.height!==Tc.height)&&(Tc=Xn(cn.width,cn.height)),Ys&&pe.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=Ys.fromPixels(cn):(Tc=Xx(cn),l=Ys.fromPixels(Tc));else{let f=Xx(cn).getContext("2d").getImageData(0,0,a,o);c=f.data.length/a/o;let m=new Uint8Array(f.data.buffer);l=pt(m,[a,o,c])}if(c===4){let p=pl(l,[0,0,0],[-1,-1,3]);te(l),l=p}if(!l)throw new Error("input error: cannot create tensor");let u=me(l,"float32"),d=t.filter.equalization?await d0(u):Zt(u,0);return te([l,u]),{tensor:d,canvas:t.filter.return?cn:null}}}async function I8(e,t){let n=!1;if(e.cacheSensitivity===0||!t.shape||t.shape.length!==4||t.shape[1]>2048||t.shape[2]>2048)return 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OffscreenCanvas!="undefined",this.initial=!0,this.worker=this.browser&&this.offscreen?typeof WorkerGlobalScope!="undefined":void 0,typeof navigator!="undefined"){let t=navigator.userAgent.match(/\(([^()]+)\)/g);if(t&&t[0]){let n=t[0].match(/\(([^()]+)\)/g);this.platform=n&&n[0]?n[0].replace(/\(|\)/g,""):"",this.agent=navigator.userAgent.replace(t[0],""),this.platform[1]&&(this.agent=this.agent.replace(t[1],"")),this.agent=this.agent.replace(/ /g," ")}}else typeof process!="undefined"&&(this.platform=`${process.platform} ${process.arch}`,this.agent=`NodeJS ${process.version}`)}async updateBackend(){this.backends=Object.keys(rs().registryFactory),this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&Ds()==="wasm"&&(this.wasm.simd=await Y().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await Y().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Xn(100,100),n=t?t.getContext("webgl2"):void 0;if(this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&(Ds()==="webgl"||Ds()==="humangl")){let s=Rr().gpgpu!=="undefined"?await Rr().getGPGPUContext().gl:null;s&&(this.webgl.version=s.getParameter(s.VERSION),this.webgl.renderer=s.getParameter(s.RENDERER))}this.webgpu.supported=this.browser&&typeof navigator.gpu!="undefined",this.webgpu.backend=this.backends.includes("webgpu");try{this.webgpu.supported&&(this.webgpu.adapter=(await navigator.gpu.requestAdapter()).name)}catch(s){this.webgpu.supported=!1}try{this.kernels=Zr(Ds()).map(s=>s.kernelName.toLowerCase())}catch(s){}}async updateCPU(){let t={model:"",flags:[]};this.node&&this.platform.startsWith("linux"),this.cpu?this.cpu=t:Object.defineProperty(this,"cpu",{value:t})}},pe=new T8;var Kx="2.5.6";var hs,Zx=[],U2e=["white","black","asian","indian","other"],G2e=[15,23,28,35.5,45.5,55.5,65],N8=0,E8=0,Yx=Number.MAX_SAFE_INTEGER;async function 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H2e=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],j2e=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],q2e=[33,133,362,263,1,78,308],O1e=H2e.map(e=>zp[e]),M1e=j2e.map(e=>zp[e]),z1e=q2e.map(e=>zp[e]);var Lp=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],g0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2],lb=(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],ub=(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],W8=(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}},cb=(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},Bp=(e,t)=>{let n=g0(e),s=Lp(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}},Wp=e=>{let t=g0(e),n=Lp(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}},y0=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}},db=[[1,0,0],[0,1,0],[0,0,1]],X2e=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),K2e=(e,t)=>X2e(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var V8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],zl=(e,t)=>{let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n},Z2e=(e,t)=>{let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n},U8=(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(zl(e[r],Z2e(t,a)))}return n},G8=(e,t)=>{let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=V8(t[0],t[1]),o=U8(a,r),i=V8(-t[0],-t[1]);return U8(o,i)},Y2e=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-zl(t[0],n),-zl(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]},J2e=(e,t)=>[zl(e,t[0]),zl(e,t[1])];function H8(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 j8(e,t,n,s,r){let a=Lp(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?G8(n,[0,0]):db,c=i?o.map(p=>[...J2e(p,l),p[2]]):o,u=i?Y2e(s):db,d=[...g0({startPoint:t.startPoint,endPoint:t.endPoint}),1];return c.map(p=>[Math.round(p[0]+zl(d,u[0])),Math.round(p[1]+zl(d,u[1])),Math.round(p[2]||0)])}function pb(e,t,n,s){let r=t.landmarks.length>=ob.count?ob.symmetryLine:Mp.symmetryLine,a=0,o=db,i;if(e&&pe.kernels.includes("rotatewithoffset"))if(a=K2e(t.landmarks[r[0]],t.landmarks[r[1]]),a&&a!==0&&Math.abs(a)>.2){let c=g0({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=G8(-a,c),i=cb(t,d,[s,s]),te(d)}else i=cb(t,n,[s,s]);else i=cb(t,n,[s,s]);return[a,o,i]}var q8=6,Us,X8=null,Yo=0,Vp=null,A0=()=>Yo;async function K8(e){var t,n;return pe.initial&&(Us=null),Us?e.debug&&J("cached model:",Us.modelUrl):(Us=await Be(We(e.modelBasePath,((t=e.face.detector)==null?void 0:t.modelPath)||"")),!Us||!Us.modelUrl?J("load model failed:",(n=e.face.detector)==null?void 0:n.modelPath):e.debug&&J("load model:",Us.modelUrl)),Yo=Us.inputs[0].shape?Us.inputs[0].shape[2]:0,Vp=Ce(Yo,"int32"),X8=fr(H8(Yo)),Us}function Q2e(e){let t={};t.boxStarts=Pe(e,[0,1],[-1,2]),t.centers=ue(t.boxStarts,X8),t.boxSizes=Pe(e,[0,3],[-1,2]),t.boxSizesNormalized=de(t.boxSizes,Vp),t.centersNormalized=de(t.centers,Vp),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,Vp),t.endNormalized=L(t.ends,Vp);let n=Vu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(s=>te(t[s])),n}async function Z8(e,t){var i,l,c,u;if(!e||e.isDisposedInternal||e.shape.length!==4||e.shape[1]<1||e.shape[2]<1)return{boxes:[]};let n={};n.resized=Se.resizeBilinear(e,[Yo,Yo]),n.div=de(n.resized,Xe.tf127),n.normalized=he(n.div,Xe.tf05);let s=Us==null?void 0:Us.execute(n.normalized);if(Array.isArray(s)){let d=s.sort((p,h)=>p.size-h.size);n.concat384=St([d[0],d[2]],2),n.concat512=St([d[1],d[3]],2),n.concat=St([n.concat512,n.concat384],1),n.batch=rt(n.concat,0)}else n.batch=rt(s);te(s),n.boxes=Q2e(n.batch),n.logits=Pe(n.batch,[0,0],[-1,1]),n.sigmoid=as(n.logits),n.scores=rt(n.sigmoid),n.nms=await Se.nonMaxSuppressionAsync(n.boxes,n.scores,((i=t.face.detector)==null?void 0:i.maxDetected)||0,((l=t.face.detector)==null?void 0:l.iouThreshold)||0,((c=t.face.detector)==null?void 0:c.minConfidence)||0);let r=await n.nms.array(),a=[],o=await n.scores.data();for(let d=0;d<r.length;d++){let p=o[r[d]];if(p>(((u=t.face.detector)==null?void 0:u.minConfidence)||0)){let h={};h.bbox=Pe(n.boxes,[r[d],0],[1,-1]),h.slice=Pe(n.batch,[r[d],q8-1],[1,-1]),h.squeeze=rt(h.slice),h.landmarks=H(h.squeeze,[q8,-1]);let f=await h.bbox.data();a.push({startPoint:[f[0],f[1]],endPoint:[f[2],f[3]],landmarks:await h.landmarks.array(),confidence:p}),Object.keys(h).forEach(m=>te(h[m]))}}return Object.keys(n).forEach(d=>te(n[d])),{boxes:a,scaleFactor:[e.shape[2]/Yo,e.shape[1]/Yo]}}var mb={};ed(mb,{connected:()=>fb,kpt:()=>hb});var hb=["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"],fb={leftLeg:["leftHip","leftKnee","leftAnkle","leftHeel","leftFoot"],rightLeg:["rightHip","rightKnee","rightAnkle","rightHeel","rightFoot"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder","rightShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist","leftPalm"],rightArm:["rightShoulder","rightElbow","rightWrist","rightPalm"],leftEye:["leftEyeInside","leftEye","leftEyeOutside"],rightEye:["rightEyeInside","rightEye","rightEyeOutside"],mouth:["leftMouth","rightMouth"]};var Y8=224,eye,tye=5,x0=[8,16,32,32,32];async function J8(){let e=[],t=0;for(;t<tye;){let n=0,s=t;for(;s<x0.length&&x0[s]===x0[t];)n+=2,s++;let r=x0[t],a=Math.ceil(Y8/r),o=Math.ceil(Y8/r);for(let i=0;i<a;++i)for(let l=0;l<o;++l)for(let c=0;c<n;++c)e.push({x:(l+.5)/o,y:(i+.5)/a});t=s}eye={x:Ct(e.map(n=>n.x)),y:Ct(e.map(n=>n.y))}}function da(e,t=[1,1]){let n=[e.map(i=>i[0]),e.map(i=>i[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[s[0],s[1],r[0]-s[0],r[1]-s[1]],o=[a[0]/t[0],a[1]/t[1],a[2]/t[0],a[3]/t[1]];return{box:a,boxRaw:o}}function Q8(e,t=[1,1]){let n=[e.map(c=>c[0]),e.map(c=>c[1])],s=[Math.min(...n[0]),Math.min(...n[1])],r=[Math.max(...n[0]),Math.max(...n[1])],a=[(s[0]+r[0])/2,(s[1]+r[1])/2],o=Math.max(a[0]-s[0],a[1]-s[1],-a[0]+r[0],-a[1]+r[1]),i=[Math.trunc(a[0]-o),Math.trunc(a[1]-o),Math.trunc(2*o),Math.trunc(2*o)],l=[i[0]/t[0],i[1]/t[1],i[2]/t[0],i[3]/t[1]];return{box:i,boxRaw:l}}function b0(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 gb(e){return[Math.max(0,e[1]),Math.max(0,e[0]),Math.min(1,e[3]+e[1]),Math.min(1,e[2]+e[0])]}var eT={initial:!0},pn={detector:null,landmarks:null},Ec={detector:[224,224],landmarks:[256,256]},yb=Number.MAX_SAFE_INTEGER,nye={landmarks:["ld_3d","activation_segmentation","activation_heatmap","world_3d","output_poseflag"],detector:[]},v0=null,Up,Jo=[[0,0],[0,0],[0,0],[0,0]],tT=0,nT=e=>1-1/(1+Math.exp(e));async function sT(e){if(eT.initial&&(pn.detector=null),!pn.detector&&e.body.detector&&e.body.detector.modelPath){pn.detector=await Be(We(e.modelBasePath,e.body.detector.modelPath||""));let t=Object.values(pn.detector.modelSignature.inputs);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 J8(),pn.detector}async function rT(e){if(eT.initial&&(pn.landmarks=null),pn.landmarks)e.debug&&J("cached model:",pn.landmarks.modelUrl);else{pn.landmarks=await Be(We(e.modelBasePath,e.body.modelPath||""));let t=Object.values(pn.landmarks.modelSignature.inputs);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 sye(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;let s;if(Up&&(n.cropped=Se.cropAndResize(e,[Up],[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];Jo=[[0,0],r,a,[0,0]],n.pad=Qs(n.cropped||e,Jo),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 rye(e,t){for(let n of e)n.position=[Math.trunc(n.position[0]*(t[0]+Jo[2][0]+Jo[2][1])/t[0]-Jo[2][0]),Math.trunc(n.position[1]*(t[1]+Jo[1][0]+Jo[1][1])/t[1]-Jo[1][0]),n.position[2]],n.positionRaw=[n.position[0]/t[0],n.position[1]/t[1],n.position[2]];if(Up)for(let n of e)n.positionRaw=[n.positionRaw[0]+Up[1],n.positionRaw[1]+Up[0],n.positionRaw[2]],n.position=[Math.trunc(n.positionRaw[0]*t[0]),Math.trunc(n.positionRaw[1]*t[1]),n.positionRaw[2]];return e}async function aye(e,t,n){var h;let s={};[s.ld,s.segmentation,s.heatmap,s.world,s.poseflag]=(h=pn.landmarks)==null?void 0:h.execute(e,nye.landmarks);let r=(await s.poseflag.data())[0],a=await s.ld.data();Object.keys(s).forEach(f=>te(s[f]));let o=[],i=5;for(let f=0;f<a.length/i;f++){let m=nT(a[i*f+3]),g=nT(a[i*f+4]),y=Math.trunc(100*m*g*r)/100,x=[a[i*f+0]/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:hb[f],positionRaw:x,position:A,score:y})}if(r<(t.body.minConfidence||0))return null;let l=rye(o,n),c=l.map(f=>f.position),u=da(c,[n[0],n[1]]),d={};for(let[f,m]of Object.entries(fb)){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 Ab(e,t){let n=[e.shape[2]||0,e.shape[1]||0],s=(t.body.skipTime||0)>ie()-tT,r=yb<(t.body.skipFrames||0);if(t.skipAllowed&&s&&r&&v0!==null)yb++;else{let a={};a.landmarks=await sye(e,256),v0=await aye(a.landmarks,t,n),Object.keys(a).forEach(o=>te(a[o])),tT=ie(),yb=0}return v0?[v0]:[]}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 ir,Ll=0,xb=[],aT=0,bb=Number.MAX_SAFE_INTEGER;async function oT(e){if(pe.initial&&(ir=null),ir)e.debug&&J("cached model:",ir.modelUrl);else{ir=await Be(We(e.modelBasePath,e.object.modelPath||""));let t=Object.values(ir.modelSignature.inputs);Ll=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!ir||!ir.modelUrl?J("load model failed:",e.object.modelPath):e.debug&&J("load model:",ir.modelUrl)}return ir}async function oye(e,t,n){if(!e)return[];let s={},r=[],a=await e.array();s.squeeze=rt(e);let o=Yt(s.squeeze,6,1);s.stack=an([o[1],o[0],o[3],o[2]],1),s.boxes=rt(s.stack),s.scores=rt(o[4]),s.classes=rt(o[5]),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]/Ll,a[0][c][1]/Ll],m=[h,f,a[0][c][2]/Ll-h,a[0][c][3]/Ll-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 vb(e,t){let n=(t.object.skipTime||0)>ie()-aT,s=bb<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&xb.length>0?(bb++,xb):(bb=0,new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=Se.resizeBilinear(e,[Ll,Ll]),i=t.object.enabled?ir==null?void 0:ir.execute(o,["tower_0/detections"]):null;aT=ie(),te(o);let l=await oye(i,a,t);xb=l,r(l)}))}var Sb={};ed(Sb,{connected:()=>kb,kpt:()=>wb});var wb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],kb={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var hn,iT=0,Kn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},Ib=Number.MAX_SAFE_INTEGER;async function lT(e){return pe.initial&&(hn=null),hn?e.debug&&J("cached model:",hn.modelUrl):(hn=await Be(We(e.modelBasePath,e.body.modelPath||"")),!hn||!hn.modelUrl?J("load model failed:",e.body.modelPath):e.debug&&J("load model:",hn.modelUrl)),hn}async function iye(e,t){let[n,s]=e.shape,r=H(e,[s*n]),a=An(r,0),o=(await a.data())[0];if(te([r,a]),o>t){let i=Fs(r,0),l=Gd(i,n),c=(await 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0:d.minConfidence)||0)&&Kn.keypoints.push({score:Math.round(100*y)/100,part:wb[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))}Kn.score=Kn.keypoints.reduce((p,h)=>h.score>p?h.score:p,0);let i=Kn.keypoints.map(p=>p.position[0]),l=Kn.keypoints.map(p=>p.position[1]);Kn.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let c=Kn.keypoints.map(p=>p.positionRaw[0]),u=Kn.keypoints.map(p=>p.positionRaw[1]);Kn.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(kb)){let f=[];for(let m=0;m<h.length-1;m++){let g=Kn.keypoints.find(x=>x.part===h[m]),y=Kn.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])}Kn.annotations[p]=f}r([Kn])}))}var lye=["angry","disgust","fear","happy","sad","surprise","neutral"],Zn,w0=[],uT=0,cT=0,Tb=Number.MAX_SAFE_INTEGER;async function dT(e){var t,n;return pe.initial&&(Zn=null),Zn?e.debug&&J("cached model:",Zn.modelUrl):(Zn=await Be(We(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!Zn||!Zn.modelUrl?J("load model failed:",(n=e.face.emotion)==null?void 0:n.modelPath):e.debug&&J("load model:",Zn.modelUrl)),Zn}async function Nb(e,t,n,s){var o,i;if(!Zn)return[];let r=Tb<(((o=t.face.emotion)==null?void 0:o.skipFrames)||0),a=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>ie()-cT;return t.skipAllowed&&a&&r&&uT===s&&w0[n]&&w0[n].length>0?(Tb++,w0[n]):(Tb=0,new Promise(async l=>{var u,d;let c=[];if((u=t.face.emotion)==null?void 0:u.enabled){let p={},h=(Zn==null?void 0:Zn.inputs[0].shape)?Zn.inputs[0].shape[2]:0;p.resize=Se.resizeBilinear(e,[h,h],!1),p.channels=L(p.resize,Xe.rgb),p.grayscale=we(p.channels,3,!0),p.grayscaleSub=he(p.grayscale,Xe.tf05),p.grayscaleMul=L(p.grayscaleSub,Xe.tf2),p.emotion=Zn==null?void 0:Zn.execute(p.grayscaleMul),cT=ie();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((d=t.face.emotion)==null?void 0:d.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:lye[m]});c.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>te(p[m]))}w0[n]=c,uT=s,l(c)}))}var Is,Eb=[],pT=0,hT=0,fT=Number.MAX_SAFE_INTEGER;async function mT(e){let t=We(e.modelBasePath,e.face.mobilefacenet.modelPath);return pe.initial&&(Is=null),Is?e.debug&&J("cached model:",t):(Is=await Be(t),Is?e.debug&&J("load model:",t):J("load model failed:",e.face.mobilefacenet.modelPath)),Is}async function Rb(e,t,n,s){var o,i;if(!Is)return[];let r=fT<(((o=t.face.embedding)==null?void 0:o.skipFrames)||0),a=(((i=t.face.embedding)==null?void 0:i.skipTime)||0)>ie()-hT;return t.skipAllowed&&a&&r&&pT===s&&Eb[n]?(fT++,Eb[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)}Eb[n]=c,pT=s,hT=ie(),l(c)})}var lr,Qo=0,uye=2.3,_b=or.leftEyeLower0,$b=or.rightEyeLower0,_c={leftBounds:[_b[0],_b[_b.length-1]],rightBounds:[$b[0],$b[$b.length-1]]},$c={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function gT(e){var t,n;return pe.initial&&(lr=null),lr?e.debug&&J("cached model:",lr.modelUrl):(lr=await Be(We(e.modelBasePath,((t=e.face.iris)==null?void 0:t.modelPath)||"")),!lr||!lr.modelUrl?J("load model failed:",(n=e.face.iris)==null?void 0:n.modelPath):e.debug&&J("load 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t.map((o,i)=>{let l=a;return i===2?l=s:i===4&&(l=r),[o[0],o[1],l]})};async function bT(e,t,n,s){if(!lr)return n.debug&&J("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:a,crop:o}=yT(e,t,_c.leftBounds[0],_c.leftBounds[1],s,!0),{box:i,boxSize:l,crop:c}=yT(e,t,_c.rightBounds[0],_c.rightBounds[1],s,!0),u=St([o,c]);te(o),te(c);let d=lr.execute(u);te(u);let p=await d.data();te(d);let h=p.slice(0,$c.numCoordinates*3),{rawCoords:f,iris:m}=AT(h,r,a,!0),g=p.slice($c.numCoordinates*3),{rawCoords:y,iris:x}=AT(g,i,l),A=cye(e);Math.abs(A)<30?(k0(e,f,"left",null),k0(e,y,"right",null)):A<1?k0(e,f,"left",["EyeUpper0","EyeLower0"]):k0(e,y,"right",["EyeUpper0","EyeLower0"]);let b=xT(e,m,"left"),w=xT(e,x,"right");return e.concat(b).concat(w)}var Dc=[],ur=null,Bl=0,Db=Number.MAX_SAFE_INTEGER,vT=0;async function wT(e,t){var i,l,c,u,d,p,h,f,m,g,y;let n=(((i=t.face.detector)==null?void 0:i.skipTime)||0)>ie()-vT,s=Db<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);if(!t.skipAllowed||!n||!s||Dc.length===0){let x=await Z8(e,t);vT=ie(),Dc=[];for(let A of x.boxes){let b=W8(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=Bp(b,k),E=Wp(I);Dc.push(E)}Db=0}else Db++;let r=[],a=[],o=0;for(let x=0;x<Dc.length;x++){let A=Dc[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]=pb((u=t.face.detector)==null?void 0:u.rotation,A,e,((d=t.face.mesh)==null?void 0:d.enabled)?Bl:A0()),(p=t==null?void 0:t.filter)==null?void 0:p.equalization){let I=await d0(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(!ur)t.debug&&J("face mesh detection requested, but model is not loaded");else{let[I,E,R]=ur.execute(k.tensor),P=await E.data();k.faceScore=Math.round(100*P[0])/100;let $=H(R,[-1,3]),_=await $.array();if(te([R,$,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 bT(_,k.tensor,t,Bl)),k.mesh=j8(_,A,b,w,Bl),k.meshRaw=k.mesh.map(X=>[X[0]/(e.shape[2]||0),X[1]/(e.shape[1]||0),(X[2]||0)/Bl]);for(let X of Object.keys(or))k.annotations[X]=or[X].map(z=>k.mesh[z]);let T=y0(k.mesh),O=Bp(T,((g=t.face.detector)==null?void 0:g.cropFactor)||1.6);A={...Wp(O),confidence:A.confidence},k.box=lb(A,e),k.boxRaw=ub(A,e),k.score=k.faceScore,a.push(A),te(k.tensor),[b,w,k.tensor]=pb((y=t.face.detector)==null?void 0:y.rotation,A,e,Bl)}}else{k.box=lb(A,e),k.boxRaw=ub(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]/A0(),(A.startPoint[1]+A.endPoint[1])/2+(A.endPoint[1]+A.startPoint[1])*I[1]/A0()]),k.meshRaw=k.mesh.map(I=>[I[0]/(e.shape[2]||0),I[1]/(e.shape[1]||0),(I[2]||0)/Bl]);for(let I of Object.keys(Mp))k.annotations[I]=[k.mesh[Mp[I]]]}r.push(k)}return Dc=[...a],r}async function kT(e){var t,n;return pe.initial&&(ur=null),ur?e.debug&&J("cached model:",ur.modelUrl):(ur=await Be(We(e.modelBasePath,((t=e.face.mesh)==null?void 0:t.modelPath)||"")),!ur||!ur.modelUrl?J("load model failed:",(n=e.face.mesh)==null?void 0:n.modelPath):e.debug&&J("load model:",ur.modelUrl)),Bl=ur.inputs[0].shape?ur.inputs[0].shape[2]:0,ur}var ST=Ml,IT=zp;var Cs,S0=[],CT=0,TT=0,Fb=Number.MAX_SAFE_INTEGER;async function NT(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 Pb(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 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I=m.find(R=>R.shape[1]===1024),E=I?await I.data():[];d.descriptor=Array.from(E),m.forEach(R=>te(R))}S0[n]=d,TT=s,u(d)}))}function I0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Gp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function ET(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 RT(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 C0(e,t=1.5){let n=Gp(e),s=I0(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 T0(e){let t=Gp(e),n=I0(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],o=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function dye(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function _T(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return dye(n)}var $T=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ei(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function pye(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function DT(e,t){let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(ei(e[r],pye(t,a)))}return n}function Mb(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=$T(t[0],t[1]),o=DT(a,r),i=$T(-t[0],-t[1]);return DT(o,i)}function FT(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-ei(t[0],n),-ei(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function zb(e,t){return[ei(e,t[0]),ei(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=as(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=RT(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 hye=5,OT=1.65,MT=[0,5,9,13,17,1,2],fye=0,mye=2,zT=0,Bb=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=>zb([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return C0(T0(r),hye)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=C0(T0(n),OT);s.palmLandmarks=[];for(let r=0;r<MT.length;r++)s.palmLandmarks.push(t[MT[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=I0(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=Mb(s,[0,0]),c=i.map(h=>[...zb(h,l),h[2]]),u=FT(r),d=[...Gp(n),1],p=[ei(d,u[0]),ei(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()-zT,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[fye],c.palmLandmarks[mye]):0,d=Gp(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=Mb(-u,d),m=s?this.getBoxForPalmLandmarks(c.palmLandmarks,f):c,g=ET(m,h,[this.inputSize,this.inputSize]),y=de(g,Xe.tf255);te(g),te(h);let[x,A]=this.handPoseModel.execute(y);zT=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=C0(T0(c),OT),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 Yn={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=>Yn.nameMapping[e],getPoints:e=>Yn.pointsMapping[e]},ti={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>ti.nameMapping[e]},Bt={verticalUp:0,verticalDown:1,horizontalLeft:2,horizontalRight:3,diagonalUpRight:4,diagonalUpLeft:5,diagonalDownRight:6,diagonalDownLeft:7,nameMapping:{0:"verticalUp",1:"verticalDown",2:"horizontalLeft",3:"horizontalRight",4:"diagonalUpRight",5:"diagonalUpLeft",6:"diagonalDownRight",7:"diagonalDownLeft"},getName:e=>Bt.nameMapping[e]},Wl=class{constructor(t){fe(this,"name");fe(this,"curls");fe(this,"directions");fe(this,"weights");fe(this,"weightsRelative");this.name=t,this.curls={},this.directions={},this.weights=[1,1,1,1,1],this.weightsRelative=[1,1,1,1,1]}curl(t,n,s){typeof this.curls[t]=="undefined"&&(this.curls[t]=[]),this.curls[t].push([n,s])}direction(t,n,s){this.directions[t]||(this.directions[t]=[]),this.directions[t].push([n,s])}weight(t,n){this.weights[t]=n;let s=this.weights.reduce((r,a)=>r+a,0);this.weightsRelative=this.weights.map(r=>r*5/s)}matchAgainst(t,n){let s=0;for(let r in t){let a=t[r],o=this.curls[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}for(let r in n){let a=n[r],o=this.directions[r];if(typeof o=="undefined"){s+=this.weightsRelative[r];continue}for(let[i,l]of o)if(a===i){s+=l*this.weightsRelative[r];break}}return s/10}};var{thumb:Sr,index:pa,middle:ha,ring:Vl,pinky:Ul}=Yn,{none:Ir,half:gye,full:Cr}=ti,{verticalUp:Fc,verticalDown:cxe,horizontalLeft:Wb,horizontalRight:yye,diagonalUpRight:Aye,diagonalUpLeft:Pc,diagonalDownRight:dxe,diagonalDownLeft:pxe}=Bt,ni=new Wl("thumbs up");ni.curl(Sr,Ir,1);ni.direction(Sr,Fc,1);ni.direction(Sr,Pc,.25);ni.direction(Sr,Aye,.25);for(let e of[Yn.index,Yn.middle,Yn.ring,Yn.pinky])ni.curl(e,Cr,1),ni.direction(e,Wb,1),ni.direction(e,yye,1);var en=new Wl("victory");en.curl(Sr,gye,.5);en.curl(Sr,Ir,.5);en.direction(Sr,Fc,1);en.direction(Sr,Pc,1);en.curl(pa,Ir,1);en.direction(pa,Fc,.75);en.direction(pa,Pc,1);en.curl(ha,Ir,1);en.direction(ha,Fc,1);en.direction(ha,Pc,.75);en.curl(Vl,Cr,1);en.direction(Vl,Fc,.2);en.direction(Vl,Pc,1);en.direction(Vl,Wb,.2);en.curl(Ul,Cr,1);en.direction(Ul,Fc,.2);en.direction(Ul,Pc,1);en.direction(Ul,Wb,.2);en.weight(pa,2);en.weight(ha,2);var si=new Wl("point");si.curl(Sr,Cr,1);si.curl(pa,Ir,.5);si.curl(ha,Cr,.5);si.curl(Vl,Cr,.5);si.curl(Ul,Cr,.5);si.weight(pa,2);si.weight(ha,2);var ri=new Wl("middle finger");ri.curl(Sr,Ir,1);ri.curl(pa,Cr,.5);ri.curl(ha,Cr,.5);ri.curl(Vl,Cr,.5);ri.curl(Ul,Cr,.5);ri.weight(pa,2);ri.weight(ha,2);var Oc=new Wl("open palm");Oc.curl(Sr,Ir,.75);Oc.curl(pa,Ir,.75);Oc.curl(ha,Ir,.75);Oc.curl(Vl,Ir,.75);Oc.curl(Ul,Ir,.75);var LT=[ni,en,si,ri,Oc];var xye=.7,Gl={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 BT(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 WT(e,t){if(!e||!t)return[0,0];let n=BT(e[0],e[1],t[0],t[1]);if(e.length===2)return n;let s=BT(e[1],e[2],t[1],t[2]);return[n,s]}function VT(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 bye(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>Gl.NO_CURL_START_LIMIT?y=ti.none:g>Gl.HALF_CURL_START_LIMIT?y=ti.half:y=ti.full,y}function UT(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 GT(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 vye(e,t,n,s,r,a,o,i){let l,c=GT(e,t,n,s),u=UT(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 wye(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+=Gl.DISTANCE_VOTE_POWER:m>.66?h+=Gl.DISTANCE_VOTE_POWER:f+=Gl.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=WT([b,w],[k,I]),$=VT(P,Gl.TOTAL_ANGLE_VOTE_POWER);p+=$[0],h+=$[1],f+=$[2];for(let T of s){let O=VT(T,Gl.SINGLE_ANGLE_VOTE_POWER);p+=O[0],h+=O[1],f+=O[2]}let _;return p===Math.max(p,h,f)?_=GT(l,i,c,d):f===Math.max(h,f)?_=UT(a,r,o,u):_=vye(l,i,c,d,a,r,o,u),_}function HT(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of Yn.all){let o=Yn.getPoints(a),i=[],l=[];for(let c of o){let u=e[c[0]],d=e[c[1]],p=WT(u,d),h=p[0],f=p[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of Yn.all){let o=a===Yn.thumb?1:0,i=Yn.getPoints(a),l=e[i[o][0]],c=e[i[o+1][1]],u=e[i[3][1]],d=bye(l,c,u),p=wye(l,c,u,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function N0(e){if(!e||e.length===0)return null;let t=HT(e),n={};for(let s of Yn.all)n[Yn.getName(s)]={curl:ti.getName(t.curls[s]),direction:Bt.getName(t.directions[s])};return n}function jT(e){let t=[];if(!e||e.length===0)return t;let n=HT(e);for(let s of LT){let r=s.matchAgainst(n.curls,n.directions);r>=xye&&t.push({name:s.name,confidence:r})}return t}var qT={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]},fa,ma,XT;async function Vb(e,t){let n=await XT.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(qT))a[u]=qT[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=N0(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 Ub(e){var n,s,r,a,o,i;pe.initial&&(fa=null,ma=null),!fa||!ma?([fa,ma]=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&&(!fa||!fa.modelUrl?J("load model failed:",((o=e.hand.detector)==null?void 0:o.modelPath)||""):e.debug&&J("load model:",fa.modelUrl),!ma||!ma.modelUrl?J("load model failed:",((i=e.hand.skeleton)==null?void 0:i.modelPath)||""):e.debug&&J("load model:",ma.modelUrl))):(e.debug&&J("cached model:",fa.modelUrl),e.debug&&J("cached model:",ma.modelUrl));let t=new Lb(fa);return XT=new Bb(t,ma),[fa,ma]}var Et=[null,null],kye=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],ai=[[0,0],[0,0]],Sye=["hand","fist","pinch","point","face","tip","pinchtip"],KT=4,ZT=1.6,Iye=512,Cye=1.4,E0=Number.MAX_SAFE_INTEGER,Gb=0,ga=[0,0],Xt={boxes:[],hands:[]},YT={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 JT(e){var t,n;if(pe.initial&&(Et[0]=null),Et[0])e.debug&&J("cached model:",Et[0].modelUrl);else{R0(["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);ai[0][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,ai[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 QT(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);ai[1][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,ai[1][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!Et[1]||!Et[1].modelUrl?J("load model failed:",(n=e.hand.skeleton)==null?void 0:n.modelPath):e.debug&&J("load model:",Et[1].modelUrl)}return Et[1]}async function Tye(e,t){let n=[];if(!e||!Et[0])return n;let s={},r=(e.shape[2]||1)/(e.shape[1]||1),a=Math.min(Math.round((e.shape[1]||0)/8)*8,Iye),o=Math.round(a*r/8)*8;s.resize=Se.resizeBilinear(e,[a,o]),s.cast=me(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await Et[0].executeAsync(s.cast,kye),s.boxes=rt(s.rawBoxes,[0,2]),s.scores=rt(s.rawScores,[0]);let i=os(s.scores,1);te(i[KT]),i.splice(KT,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=b0(m,Cye),y=gb(g),x=[Math.trunc(m[0]*ga[0]),Math.trunc(m[1]*ga[1]),Math.trunc(m[2]*ga[0]),Math.trunc(m[3]*ga[1])],A=u[p],b=Sye[d[p]],w={id:l++,score:A,box:x,boxRaw:g,boxCrop:y,label:b};n.push(w)}return Object.keys(s).forEach(p=>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 Hb(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],[ai[1][0],ai[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]/ai[1][1],u[1]/ai[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=>[ga[0]*(u[0]+t.boxRaw[0]),ga[1]*(u[1]+t.boxRaw[1]),u[2]||0]),s.landmarks=N0(s.keypoints);for(let u of 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d=Q8(Xt.hands[u].keypoints,ga);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=b0(d.box,ZT),h=b0(d.boxRaw,ZT),f=gb(h);Xt.boxes.push({...c[u],box:p,boxRaw:h,boxCrop:f})}}for(let u=0;u<Xt.hands.length;u++){let d=da(Xt.hands[u].keypoints,ga);Xt.hands[u].box=d.box,Xt.hands[u].boxRaw=d.boxRaw}o(Xt.hands)})}var fn,_0=[],qb=Number.MAX_SAFE_INTEGER,eN=0,tN=0;async function nN(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 Xb(e,t,n,s){var o,i;if(!fn)return 0;let r=(((o=t.face.liveness)==null?void 0:o.skipTime)||0)>ie()-tN,a=qb<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&r&&a&&eN===s&&_0[n]?(qb++,_0[n]):(qb=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,eN=s,tN=ie(),te([c,u]),l(_0[n])}))}var Jb={};ed(Jb,{connected:()=>D0,horizontal:()=>Kb,kpt:()=>$0,relative:()=>Yb,vertical:()=>Zb});var 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u=c*13,d=(g=e.find(x=>x.shape[1]===u**2&&x.shape[2]===Rc.length))==null?void 0:g.squeeze(),p=(y=e.find(x=>x.shape[1]===u**2&&x.shape[2]<Rc.length))==null?void 0:y.squeeze(),f=await p.reshape([-1,4,p.shape[1]/4]).argMax(2).array(),m=await d.array();for(let x=0;x<d.shape[0];x++)for(let A=0;A<d.shape[1];A++){let b=m[x][A];if(b>s.object.minConfidence&&A!==61){let w=(.5+Math.trunc(x%u))/u,k=(.5+Math.trunc(x/u))/u,I=f[x].map(V=>V*(u/c/t)),[E,R]=[w-P0/c*I[0],k-P0/c*I[1]],[P,$]=[w+P0/c*I[2]-E,k+P0/c*I[3]-R],_=[E,R,P,$];_=_.map(V=>Math.max(0,Math.min(V,1)));let T=[_[0]*n[0],_[1]*n[1],_[2]*n[0],_[3]*n[1]],O={id:r++,score:Math.round(100*b)/100,class:A+1,label:Rc[A].label,box:T.map(V=>Math.trunc(V)),boxRaw:_};a.push(O)}}});e.forEach(c=>te(c));let o=a.map(c=>[c.boxRaw[1],c.boxRaw[0],c.boxRaw[3],c.boxRaw[2]]),i=a.map(c=>c.score),l=[];if(o&&o.length>0){let c=await Se.nonMaxSuppressionAsync(o,i,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);l=await c.data(),te(c)}return 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Hp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_ye=Hp.length,jp=Hp.reduce((e,t,n)=>(e[t]=n,e),{}),$ye=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],ebe=$ye.map(([e,t])=>[jp[e],jp[t]]),dN=[["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 pN(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 hN(e,[t,n],[s,r]){let a=t/s,o=n/r,i=(c,u)=>({id:u,score:c.score,boxRaw:[c.box[0]/r,c.box[1]/s,c.box[2]/r,c.box[3]/s],box:[Math.trunc(c.box[0]*o),Math.trunc(c.box[1]*a),Math.trunc(c.box[2]*o),Math.trunc(c.box[3]*a)],keypoints:c.keypoints.map(({score:d,part:p,position:h})=>({score:d,part:p,position:[Math.trunc(h.x*o),Math.trunc(h.y*a)],positionRaw:[h.x/s,h.y/s]})),annotations:{}});return e.map((c,u)=>i(c,u))}var r5=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 kN(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 g5(e,t,n){let s=En(ya,n);if(!(!t||!e)&&s.drawGestures){let r=jl(e);if(!r)return;r.font=s.font,r.fillStyle=s.color;let a=1;for(let o=0;o<t.length;o++){let i=[],l=[];if([i,l]=Object.entries(t[o]),l.length>1&&l[1].length>0){let c=i[1]>0?`#${i[1]}`:"",u=`${i[0]} ${c}: ${l[1]}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(u,8,2+a*s.lineHeight)),r.fillStyle=s.labelColor,r.fillText(u,6,0+a*s.lineHeight),a+=1}}}}async function y5(e,t,n){var a,o,i,l,c;let s=En(ya,n);if(!t||!e)return;let r=jl(e);if(!!r)for(let u of t){if(r.font=s.font,r.strokeStyle=s.color,r.fillStyle=s.color,s.drawBoxes&&qp(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)m5(r,d[0],d[1],d[2],s);if(s.drawPolygons){if(r.lineWidth=1,u.mesh.length>450)for(let d=0;d<Ml.length/3;d++){let p=[Ml[d*3+0],Ml[d*3+1],Ml[d*3+2]].map(h=>u.mesh[h]);wN(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]}
|
|
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]];kN(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]];kN(r,[u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]],[p[0],p[1]],4)}}}}}async function A5(e,t,n){var a;let s=En(ya,n);if(!t||!e)return;let r=jl(e);if(!!r){r.lineJoin="round";for(let o=0;o<t.length;o++){if(r.strokeStyle=s.color,r.fillStyle=s.color,r.lineWidth=s.lineWidth,r.font=s.font,s.drawBoxes&&t[o].box&&((a=t[o].box)==null?void 0:a.length)===4&&(qp(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)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:s.color,m5(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s));if(s.drawLabels&&t[o].keypoints){r.font=s.font;for(let i of t[o].keypoints)!i.score||i.score===0||(r.fillStyle=s.useDepth&&i.position[2]?`rgba(${127.5+2*i.position[2]}, ${127.5-2*i.position[2]}, 255, 0.5)`:s.color,r.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4))}if(s.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let i of Object.values(t[o].annotations))for(let l of i)Vye(r,l,s)}}}async function x5(e,t,n){let s=En(ya,n);if(!t||!e)return;let r=jl(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,qp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels&&(s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(`hand:${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)}, ${127.5-2*(o[2]||0)}, 255, 0.5)`:s.color,m5(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{!i||i.length===0||!i[0]||(r.fillStyle=s.useDepth?`rgba(${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]}, ${127.5-l*i[l][2]}, 255, 0.5)`:s.color,r.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()};r.lineWidth=s.lineWidth,o(a.annotations.index),o(a.annotations.middle),o(a.annotations.ring),o(a.annotations.pinky),o(a.annotations.thumb)}}}}async function b5(e,t,n){let s=En(ya,n);if(!t||!e)return;let r=jl(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,qp(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}}async function SN(e,t,n){let s=En(ya,n);if(!t||!e)return;let r=jl(e);if(!!r){r.lineJoin="round",r.font=s.font;for(let a=0;a<t.length;a++)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,qp(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person #${a}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,t[a].box[0]+3,1+t[a].box[1]+s.lineHeight,t[a].box[2])),r.fillStyle=s.labelColor,r.fillText(o,t[a].box[0]+2,0+t[a].box[1]+s.lineHeight,t[a].box[2])}r.stroke()}}}async function IN(e,t){if(!e||!t)return;let n=jl(t);!n||n.drawImage(e,0,0)}async function CN(e,t,n){if(!t||!t.performance||!t||!e)return null;let s=ie(),r=En(ya,n),a=Promise.all([y5(e,t.face,r),A5(e,t.body,r),x5(e,t.hand,r),b5(e,t.object,r),g5(e,t.gesture,r)]);return f5=pe.perfadd?f5+Math.round(ie()-s):Math.round(ie()-s),t.performance.draw=f5,a}var Lc=.1,v5=.5;function Uye(e,t,n){let s=!1,r=n.length-1;for(let a=0;a<n.length;r=a++)n[a].y>t!=n[r].y>t&&e<(n[r].x-n[a].x)*(t-n[a].y)/(n[r].y-n[a].y)+n[a].x&&(s=!s);return s}async function TN(e){if(!e.tensor||!e.mesh||e.mesh.length<100)return e.tensor;let t=e.tensor.shape[2]||0,n=e.tensor.shape[1]||0,s=await e.tensor.buffer(),r=[];for(let o of or.silhouette)r.push({x:(e.mesh[o][0]-e.box[0])/e.box[2],y:(e.mesh[o][1]-e.box[1])/e.box[3]});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++)Uye(o/t,i/t,r)||(s.set(v5*s.get(0,i,o,0),0,i,o,0),s.set(v5*s.get(0,i,o,1),0,i,o,1),s.set(v5*s.get(0,i,o,2),0,i,o,2));let a=s.toTensor();return te(s),a}var Gye=e=>{let t=(d,p)=>Math.atan2(d[1]-p[1],d[0]-p[0]);if(!e.annotations.rightEyeIris||!e.annotations.leftEyeIris)return{bearing:0,strength:0};let n=[0,-.1],s=1,r=e.mesh[33][2]>e.mesh[263][2],a=r?e.mesh[473]:e.mesh[468],o=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],i=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],l=[(o[0]-a[0])/i[0]-n[0],s*(a[1]-o[1])/i[1]-n[1]],c=Math.sqrt(l[0]**2+l[1]**2);return c=Math.min(c,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],l)+Math.PI/2)%Math.PI,strength:c}},NN=(e,t)=>{let n=g=>{let y=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=y,g[1]/=y,g[2]/=y,g},s=(g,y)=>{let x=g[0]-y[0],A=g[1]-y[1],b=g[2]-y[2];return[x,A,b]},r=(g,y)=>{let x=g[1]*y[2]-g[2]*y[1],A=g[2]*y[0]-g[0]*y[2],b=g[0]*y[1]-g[1]*y[0];return[x,A,b]},a=g=>{let[y,x,A,b,w,k,I,E,R]=g,P,$,_;return b<1?b>-1?(_=Math.asin(b),$=Math.atan2(-I,y),P=Math.atan2(-k,w)):(_=-Math.PI/2,$=-Math.atan2(E,R),P=0):(_=Math.PI/2,$=Math.atan2(E,R),P=0),isNaN(P)&&(P=0),isNaN($)&&($=0),isNaN(_)&&(_=0),{pitch:2*-P,yaw:2*-$,roll:2*-_}},o=g=>{let y=(A,b,w,k)=>Math.atan2(k-b,w-A);return{pitch:y(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:y(g[33][0],g[33][2],g[263][0],g[263][2]),roll:y(g[33][0],g[33][1],g[263][0],g[263][1])}},i=e.meshRaw;if(!i||i.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let l=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,c=[i[10],i[152],i[234],i[454]].map(g=>[g[0]*t[0]/l,g[1]*t[1]/l,g[2]]),u=n(s(c[1],c[0])),d=n(s(c[3],c[2])),p=n(r(d,u));d=r(u,p);let h=[d[0],d[1],d[2],u[0],u[1],u[2],p[0],p[1],p[2]],f=a(h),m=i.length===478?Gye(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}};var w5=async(e,t)=>{var h,f,m,g,y,x,A,b,w,k,I,E,R,P,$,_,T,O,V,X,z,j;let n,s,r,a,o,i,l,c,u,d=[];e.state="run:face",n=ie();let p=await wT(t,e.config);if(e.performance.face=pe.perfadd?(e.performance.face||0)+Math.trunc(ie()-n):Math.trunc(ie()-n),!t.shape||t.shape.length!==4)return[];if(!p)return[];for(let W=0;W<p.length;W++){if(e.analyze("Get Face"),!p[W].tensor||p[W].tensor.isDisposedInternal){J("Face object is disposed:",p[W].tensor);continue}if((h=e.config.face.detector)==null?void 0:h.mask){let oe=await TN(p[W]);te(p[W].tensor),p[W].tensor=oe}let Q=p[W].mesh&&p[W].mesh.length>200?NN(p[W],[t.shape[2],t.shape[1]]):null;e.analyze("Start Emotion:"),e.config.async?o=((f=e.config.face.emotion)==null?void 0:f.enabled)?Nb(p[W].tensor||pt([]),e.config,W,p.length):null:(e.state="run:emotion",n=ie(),o=((m=e.config.face.emotion)==null?void 0:m.enabled)?await Nb(p[W].tensor||pt([]),e.config,W,p.length):null,e.performance.emotion=pe.perfadd?(e.performance.emotion||0)+Math.trunc(ie()-n):Math.trunc(ie()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=((g=e.config.face.antispoof)==null?void 0:g.enabled)?ab(p[W].tensor||pt([]),e.config,W,p.length):null:(e.state="run:antispoof",n=ie(),l=((y=e.config.face.antispoof)==null?void 0:y.enabled)?await ab(p[W].tensor||pt([]),e.config,W,p.length):null,e.performance.antispoof=pe.perfadd?(e.performance.antispoof||0)+Math.trunc(ie()-n):Math.trunc(ie()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?c=((x=e.config.face.liveness)==null?void 0:x.enabled)?Xb(p[W].tensor||pt([]),e.config,W,p.length):null:(e.state="run:liveness",n=ie(),c=((A=e.config.face.liveness)==null?void 0:A.enabled)?await Xb(p[W].tensor||pt([]),e.config,W,p.length):null,e.performance.liveness=pe.perfadd?(e.performance.antispoof||0)+Math.trunc(ie()-n):Math.trunc(ie()-n)),e.analyze("End Liveness:"),e.analyze("Start GEAR:"),e.config.async?r=((b=e.config.face.gear)==null?void 0:b.enabled)?Jx(p[W].tensor||pt([]),e.config,W,p.length):{}:(e.state="run:gear",n=ie(),r=((w=e.config.face.gear)==null?void 0:w.enabled)?await Jx(p[W].tensor||pt([]),e.config,W,p.length):{},e.performance.gear=Math.trunc(ie()-n)),e.analyze("End GEAR:"),e.analyze("Start SSRNet:"),e.config.async?(s=((k=e.config.face.ssrnet)==null?void 0:k.enabled)?eb(p[W].tensor||pt([]),e.config,W,p.length):{},a=((I=e.config.face.ssrnet)==null?void 0:I.enabled)?sb(p[W].tensor||pt([]),e.config,W,p.length):{}):(e.state="run:ssrnet",n=ie(),s=((E=e.config.face.ssrnet)==null?void 0:E.enabled)?await eb(p[W].tensor||pt([]),e.config,W,p.length):{},a=((R=e.config.face.ssrnet)==null?void 0:R.enabled)?await sb(p[W].tensor||pt([]),e.config,W,p.length):{},e.performance.ssrnet=Math.trunc(ie()-n)),e.analyze("End SSRNet:"),e.analyze("Start MobileFaceNet:"),e.config.async?i=((P=e.config.face.mobilefacenet)==null?void 0:P.enabled)?Rb(p[W].tensor||pt([]),e.config,W,p.length):{}:(e.state="run:mobilefacenet",n=ie(),i=(($=e.config.face.mobilefacenet)==null?void 0:$.enabled)?await Rb(p[W].tensor||pt([]),e.config,W,p.length):{},e.performance.mobilefacenet=Math.trunc(ie()-n)),e.analyze("End MobileFaceNet:"),e.analyze("Start Description:"),e.config.async?u=((_=e.config.face.description)==null?void 0:_.enabled)?Ob(p[W].tensor||pt([]),e.config,W,p.length):null:(e.state="run:description",n=ie(),u=((T=e.config.face.description)==null?void 0:T.enabled)?await Ob(p[W].tensor||pt([]),e.config,W,p.length):null,e.performance.description=pe.perfadd?(e.performance.description||0)+Math.trunc(ie()-n):Math.trunc(ie()-n)),e.analyze("End Description:"),e.config.async&&([s,a,o,i,u,r,l,c]=await Promise.all([s,a,o,i,u,r,l,c])),e.analyze("Finish Face:"),((O=e.config.face.ssrnet)==null?void 0:O.enabled)&&s&&a&&(u={age:s.age,gender:a.gender,genderScore:a.genderScore}),((V=e.config.face.gear)==null?void 0:V.enabled)&&r&&(u={age:r.age,gender:r.gender,genderScore:r.genderScore,race:r.race}),((X=e.config.face.mobilefacenet)==null?void 0:X.enabled)&&i&&(u.descriptor=i),!((z=e.config.face.iris)==null?void 0:z.enabled);let ne=p[W].annotations&&p[W].annotations.leftEyeIris&&p[W].annotations.leftEyeIris[0]&&p[W].annotations.rightEyeIris&&p[W].annotations.rightEyeIris[0]&&p[W].annotations.leftEyeIris.length>0&&p[W].annotations.rightEyeIris.length>0&&p[W].annotations.leftEyeIris[0]!==null&&p[W].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[W].annotations.leftEyeIris[3][0]-p[W].annotations.leftEyeIris[1][0]),Math.abs(p[W].annotations.rightEyeIris[4][1]-p[W].annotations.rightEyeIris[2][1]))/t.shape[2]:0,ae=((j=e.config.face.detector)==null?void 0:j.return)?rt(p[W].tensor):null;te(p[W].tensor),p[W].tensor&&delete p[W].tensor;let Z={...p[W],id:W};(u==null?void 0:u.age)&&(Z.age=u.age),(u==null?void 0:u.gender)&&(Z.gender=u.gender),(u==null?void 0:u.genderScore)&&(Z.genderScore=u==null?void 0:u.genderScore),(u==null?void 0:u.descriptor)&&(Z.embedding=u==null?void 0:u.descriptor),(u==null?void 0:u.race)&&(Z.race=u==null?void 0:u.race),o&&(Z.emotion=o),l&&(Z.real=l),c&&(Z.live=c),ne&&ne!==0&&(Z.iris=Math.trunc(500/ne/11.7)/100),Q&&(Z.rotation=Q),ae&&(Z.tensor=ae),d.push(Z),e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),d};var EN=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},RN=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 ${l<0?"up":"down"}`})}return t},_N=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.leftEyeIris[0]||!e[n].annotations.rightEyeIris||!e[n].annotations.rightEyeIris[0])continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),c=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(c=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2],p=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2];(d>.06||p>.06)&&(c=!1),d>p?d>.05&&t.push({iris:n,gesture:"looking right"}):p>.05&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(c=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),c&&t.push({iris:n,gesture:"looking center"})}return t},$N=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>(o.position[2]||0)<(i.position[2]||0)?o:i);t.push({hand:n,gesture:`${r.name} forward`});let a=s.reduce((o,i)=>o.position[1]<i.position[1]?o:i);t.push({hand:n,gesture:`${a.name} up`})}if(e[n].keypoints){let r=jT(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var $e={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0,error:null},k5=0;function DN(e,t){var o,i,l,c,u,d,p,h,f,m,g,y,x,A,b,w,k,I,E,R,P,$,_,T,O,V,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&&($e.canvas=e.canvas),e.error&&($e.error=e.error),!$e.body||e.body.length!==$e.body.length)$e.body=JSON.parse(JSON.stringify(e.body));else for(let z=0;z<e.body.length;z++){let j=e.body[z].box.map((Z,oe)=>((r-1)*$e.body[z].box[oe]+Z)/r),W=e.body[z].boxRaw.map((Z,oe)=>((r-1)*$e.body[z].boxRaw[oe]+Z)/r),Q=e.body[z].keypoints.map((Z,oe)=>({score:Z.score,part:Z.part,position:[$e.body[z].keypoints[oe]?((r-1)*($e.body[z].keypoints[oe].position[0]||0)+(Z.position[0]||0))/r:Z.position[0],$e.body[z].keypoints[oe]?((r-1)*($e.body[z].keypoints[oe].position[1]||0)+(Z.position[1]||0))/r:Z.position[1],$e.body[z].keypoints[oe]?((r-1)*($e.body[z].keypoints[oe].position[2]||0)+(Z.position[2]||0))/r:Z.position[2]],positionRaw:[$e.body[z].keypoints[oe]?((r-1)*($e.body[z].keypoints[oe].positionRaw[0]||0)+(Z.positionRaw[0]||0))/r:Z.position[0],$e.body[z].keypoints[oe]?((r-1)*($e.body[z].keypoints[oe].positionRaw[1]||0)+(Z.positionRaw[1]||0))/r:Z.position[1],$e.body[z].keypoints[oe]?((r-1)*($e.body[z].keypoints[oe].positionRaw[2]||0)+(Z.positionRaw[2]||0))/r:Z.position[2]]})),ne={},ae={connected:{}};((i=(o=t.body)==null?void 0:o.modelPath)==null?void 0:i.includes("efficientpose"))?ae=Sb:((c=(l=t.body)==null?void 0:l.modelPath)==null?void 0:c.includes("blazepose"))?ae=mb:((d=(u=t.body)==null?void 0:u.modelPath)==null?void 0:d.includes("movenet"))&&(ae=Jb);for(let[Z,oe]of Object.entries(ae.connected)){let re=[];for(let ge=0;ge<oe.length-1;ge++){let ve=Q.find(Te=>Te.part===oe[ge]),Re=Q.find(Te=>Te.part===oe[ge+1]);ve&&Re&&re.push([ve.position,Re.position])}ne[Z]=re}$e.body[z]={...e.body[z],box:j,boxRaw:W,keypoints:Q,annotations:ne}}if(!$e.hand||e.hand.length!==$e.hand.length)$e.hand=JSON.parse(JSON.stringify(e.hand));else for(let z=0;z<e.hand.length;z++){let j=e.hand[z].box.map((ae,Z)=>((r-1)*$e.hand[z].box[Z]+ae)/r),W=e.hand[z].boxRaw.map((ae,Z)=>((r-1)*$e.hand[z].boxRaw[Z]+ae)/r);$e.hand[z].keypoints.length!==e.hand[z].keypoints.length&&($e.hand[z].keypoints=e.hand[z].keypoints);let Q=e.hand[z].keypoints&&e.hand[z].keypoints.length>0?e.hand[z].keypoints.map((ae,Z)=>ae.map((oe,re)=>((r-1)*($e.hand[z].keypoints[Z][re]||1)+(oe||0))/r)):[],ne={};if(Object.keys($e.hand[z].annotations).length!==Object.keys(e.hand[z].annotations).length)$e.hand[z].annotations=e.hand[z].annotations,ne=$e.hand[z].annotations;else if(e.hand[z].annotations)for(let ae of Object.keys(e.hand[z].annotations))ne[ae]=e.hand[z].annotations[ae]&&e.hand[z].annotations[ae][0]?e.hand[z].annotations[ae].map((Z,oe)=>Z.map((re,ge)=>((r-1)*$e.hand[z].annotations[ae][oe][ge]+re)/r)):null;$e.hand[z]={...e.hand[z],box:j,boxRaw:W,keypoints:Q,annotations:ne}}if(!$e.face||e.face.length!==$e.face.length)$e.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)*$e.face[z].box[ne]+Q)/r),W=e.face[z].boxRaw.map((Q,ne)=>((r-1)*$e.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=$e.face[z].rotation)==null?void 0:h.angle)==null?void 0:f.roll)||0)+(((g=(m=e.face[z].rotation)==null?void 0:m.angle)==null?void 0:g.roll)||0))/r,yaw:((r-1)*(((x=(y=$e.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=$e.face[z].rotation)==null?void 0:w.angle)==null?void 0:k.pitch)||0)+(((E=(I=e.face[z].rotation)==null?void 0:I.angle)==null?void 0:E.pitch)||0))/r},Q.gaze={bearing:((r-1)*(((P=(R=$e.face[z].rotation)==null?void 0:R.gaze)==null?void 0:P.bearing)||0)+(((_=($=e.face[z].rotation)==null?void 0:$.gaze)==null?void 0:_.bearing)||0))/r,strength:((r-1)*(((O=(T=$e.face[z].rotation)==null?void 0:T.gaze)==null?void 0:O.strength)||0)+(((X=(V=e.face[z].rotation)==null?void 0:V.gaze)==null?void 0:X.strength)||0))/r},$e.face[z]={...e.face[z],rotation:Q,box:j,boxRaw:W}}$e.face[z]={...e.face[z],box:j,boxRaw:W}}if(!$e.object||e.object.length!==$e.object.length)$e.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)*$e.object[z].box[ne]+Q)/r),W=e.object[z].boxRaw.map((Q,ne)=>((r-1)*$e.object[z].boxRaw[ne]+Q)/r);$e.object[z]={...e.object[z],box:j,boxRaw:W}}if(e.persons){let z=e.persons;if(!$e.persons||z.length!==$e.persons.length)$e.persons=JSON.parse(JSON.stringify(z));else for(let j=0;j<z.length;j++)$e.persons[j].box=z[j].box.map((W,Q)=>((r-1)*$e.persons[j].box[Q]+W)/r)}e.gesture&&($e.gesture=e.gesture);let a=ie();return k5=pe.perfadd?k5+Math.round(a-n):Math.round(a-n),e.performance&&($e.performance={...e.performance,interpolate:k5}),$e}function z0(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 FN=(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 PN(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let s=z0(e,t,n);return FN(s,n.order||2,n.min||0,n.max||1)}function ON(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=z0(e,t[o],n);if(i<s&&(s=i,r=o),s<(n.threshold||0))break}let a=FN(s,n.order||2,n.min||0,n.max||1);return{index:r,distance:s,similarity:a}}function MN(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=[],$=O=>{O&&O.length===4&&(R.push(O[0],O[0]+O[2]),P.push(O[1],O[1]+O[3]))};$((y=E.face)==null?void 0:y.box),$((x=E.body)==null?void 0:x.box),$((b=(A=E.hands)==null?void 0:A.left)==null?void 0:b.box),$((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 L0=`
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lBhEMohlFerLlBjEMohMVTEARDKCITsAk2AEgAAAkAAAAAAAAAAAAAAAAAAAAAAAASAAAAAAAAD/
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2Q==`;async function Hye(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(L0);break;case"body":case"full":n=await t(B0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function jye(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+L0;break;case"full":case"body":n="data:image/jpeg;base64,"+B0;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=Xn(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 qye(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(L0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(B0)),!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 zN(e,t){let n=ie();if(e.state="warmup",t&&(e.config=En(e.config,t)),!e.config.warmup||e.config.warmup.length===0||e.config.warmup==="none")return{face:[],body:[],hand:[],gesture:[],object:[],performance:e.performance,timestamp:ie(),persons:[],error:null};let s;return new Promise(async r=>{typeof createImageBitmap=="function"?s=await Hye(e):typeof Image!="undefined"||pe.Canvas!==void 0?s=await jye(e):s=await qye(e);let a=ie();e.config.debug&&J("Warmup",e.config.warmup,Math.round(a-n),"ms"),e.emit("warmup"),r(s)})}var Bc,Xp,Kp,W0,BN=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,Xp,void 0);nd(this,Kp,void 0);fe(this,"gl");fe(this,"analyze",(...t)=>{if(!td(this,Xp))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,W0,t=>{if(!td(this,Kp))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",PN);fe(this,"distance",z0);fe(this,"match",ON);fe(this,"emit",t=>{var n;this.events&&this.events.dispatchEvent&&((n=this.events)==null||n.dispatchEvent(new Event(t)))});this.env=pe,Ca.wasmPath=c0["tfjs-core"].includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${rv}/dist/`,Ca.modelBasePath=pe.browser?"../models/":"file://models/",Ca.backend=pe.browser?"humangl":"tensorflow",this.version=Kx,Object.defineProperty(this,"version",{value:Kx}),this.config=JSON.parse(JSON.stringify(Ca)),Object.seal(this.config),t&&(this.config=En(this.config,t)),this.tf=Ol,this.state="idle",sd(this,Bc,0),sd(this,Xp,!1),sd(this,Kp,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new p5,this.draw={options:ya,canvas:(n,s)=>IN(n,s),face:(n,s,r)=>y5(n,s,r),body:(n,s,r)=>A5(n,s,r),hand:(n,s,r)=>x5(n,s,r),gesture:(n,s,r)=>g5(n,s,r),object:(n,s,r)=>b5(n,s,r),person:(n,s,r)=>SN(n,s,r),all:(n,s,r)=>CN(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[],error:null},this.process={tensor:null,canvas:null},this.faceTriangulation=ST,this.faceUVMap=IT,this.gl=Rt,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(Ca)),this.config.backend=t}validate(t){return m2(Ca,t||this.config)}now(){return ie()}image(t,n=!0){return Nc(t,this.config,n)}async segmentation(t,n){return AN(t,n,this.config)}enhance(t){return Pb(t)}compare(t,n){return C8(this.config,t,n)}async init(){await M0(this,!0),await this.tf.ready()}async load(t){this.state="load";let n=ie(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=En(this.config,t)),this.env.initial&&(this.config.debug&&J(`version: ${this.version}`),this.config.debug&&J(`tfjs version: ${this.tf.version["tfjs-core"]}`),await M0(this)||J("error: backend check failed"),await hf(),this.env.browser&&(this.config.debug&&J("configuration:",this.config),this.config.debug&&J("environment:",this.env),this.config.debug&&J("tf flags:",this.tf.ENV.flags))),await xN(this),this.env.initial&&this.config.debug&&J("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await bN(this),this.emit("load"));let a=Math.trunc(ie()-n);a>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+a:a)}next(t=this.result){return DN(t,this.config)}async warmup(t){let n=ie(),s=await zN(this,t),r=ie();return this.performance.warmup=Math.trunc(r-n),s}async profile(t,n){let s=await this.tf.profile(()=>this.detect(t,n)),r={};for(let i of s.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs;let a=[];Object.entries(r).forEach(i=>a.push({name:i[0],ms:i[1]})),a.sort((i,l)=>l.ms-i.ms),a.length=20;let o={};for(let i of a)o[i.name]=i.ms;return o}async detect(t,n){return this.state="detect",new Promise(async s=>{var g,y,x,A,b,w,k,I,E,R,P,$,_,T,O,V,X,z,j,W,Q,ne;this.state="config";let r;this.config=En(this.config,n),this.state="check";let a=td(this,W0).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 M0(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 I8(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?w5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=ie(),l=this.config.face.enabled?await w5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.config.async&&(this.config.body.maxDetected===-1||this.config.hand.maxDetected===-1)&&(l=await l),this.analyze("Start Body:"),this.state="detect:body";let p=this.config.body.maxDetected===-1?En(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?(((g=this.config.body.modelPath)==null?void 0:g.includes("posenet"))?c=this.config.body.enabled?u5(i.tensor,p):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("blazepose"))?c=this.config.body.enabled?Ab(i.tensor,p):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?c=this.config.body.enabled?Cb(i.tensor,p):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("movenet"))&&(c=this.config.body.enabled?t5(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 u5(i.tensor,p):[]:((w=this.config.body.modelPath)==null?void 0:w.includes("blazepose"))?c=this.config.body.enabled?await Ab(i.tensor,p):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("efficientpose"))?c=this.config.body.enabled?await Cb(i.tensor,p):[]:((I=this.config.body.modelPath)==null?void 0:I.includes("movenet"))&&(c=this.config.body.enabled?await t5(i.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ie()-r):Math.trunc(ie()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?En(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?(((R=(E=this.config.hand.detector)==null?void 0:E.modelPath)==null?void 0:R.includes("handdetect"))?u=this.config.hand.enabled?Vb(i.tensor,h):[]:(($=(P=this.config.hand.detector)==null?void 0:P.modelPath)==null?void 0:$.includes("handtrack"))&&(u=this.config.hand.enabled?jb(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 Vb(i.tensor,h):[]:((V=(O=this.config.hand.detector)==null?void 0:O.modelPath)==null?void 0:V.includes("handtrack"))&&(u=this.config.hand.enabled?await jb(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?s5(i.tensor,this.config):[]:((z=this.config.object.modelPath)==null?void 0:z.includes("centernet"))&&(d=this.config.object.enabled?vb(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 s5(i.tensor,this.config):[]:((W=this.config.object.modelPath)==null?void 0:W.includes("centernet"))&&(d=this.config.object.enabled?await vb(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=[...RN(l),...EN(c),...$N(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 MN(l,c,u,f,m)}},te(i.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};Bc=new WeakMap,Xp=new WeakMap,Kp=new WeakMap,W0=new WeakMap;return Xye;})();
|
|
/**
|
|
* @license
|
|
* Copyright 2017 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google Inc. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* 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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/**
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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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/**
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* @license
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* Copyright 2018 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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/** @license See the LICENSE file. */
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