mirror of https://github.com/vladmandic/human
8047 lines
1.5 MiB
8047 lines
1.5 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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var Human=(()=>{var Yg=Object.defineProperty;var qN=(e,t,n)=>t in e?Yg(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var XN=e=>Yg(e,"__esModule",{value:!0});var wa=(e=>typeof require!="undefined"?require:typeof Proxy!="undefined"?new Proxy(e,{get:(t,n)=>(typeof require!="undefined"?require:t)[n]}):e)(function(e){if(typeof require!="undefined")return require.apply(this,arguments);throw new Error('Dynamic require of "'+e+'" is not supported')});var jc=(e,t)=>{XN(e);for(var n in t)Yg(e,n,{get:t[n],enumerable:!0})};var de=(e,t,n)=>(qN(e,typeof t!="symbol"?t+"":t,n),n),x5=(e,t,n)=>{if(!t.has(e))throw TypeError("Cannot "+n)};var qc=(e,t,n)=>(x5(e,t,"read from private field"),n?n.call(e):t.get(e)),Xc=(e,t,n)=>{if(t.has(e))throw TypeError("Cannot add the same private member more than once");t instanceof WeakSet?t.add(e):t.set(e,n)},Kc=(e,t,n,s)=>(x5(e,t,"write to private field"),s?s.call(e,n):t.set(e,n),n);var 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s={},r={};for(let l=0;l<t.length;l++)s[t[l].id]=!0;for(let l=0;l<e.length;l++){let c=e[l],u=c.inputs;for(let d in u){let p=u[d],h=!1;for(let f=0;f<t.length;f++)if(s[p.id]){c.outputs.forEach(m=>s[m.id]=!0),h=!0,r[c.id]=!0;break}if(h)break}}let a={};a[n.id]=!0;let o={};for(let l=e.length-1;l>=0;l--){let c=e[l],u=c.inputs;for(let d=0;d<c.outputs.length;d++)if(a[c.outputs[d].id]){for(let p in u)a[u[p].id]=!0,o[c.id]=!0;break}}let i=[];for(let l=0;l<e.length;l++){let c=e[l];if(r[c.id]&&o[c.id]){let u={};for(let p in c.inputs){let h=c.inputs[p];s[h.id]&&(u[p]=h)}let d=Object.assign({},c);d.inputs=u,d.outputs=c.outputs,i.push(d)}}return i}function QE(e,t,n,s){for(let r=t.length-1;r>=0;r--){let a=t[r],o=[];if(a.outputs.forEach(l=>{let c=e[l.id];c!=null?o.push(c):o.push(null)}),a.gradient==null)throw new Error(`Cannot compute gradient: gradient function not found for ${a.kernelName}.`);let i=a.gradient(o);for(let l in a.inputs){if(!(l in i))throw new Error(`Cannot backprop through input ${l}. 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`;return p[p.length-1]=" "+p[p.length-1]+"]"+(a?"":f),p}function bd(e){let t=[];for(let n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var tn=class{constructor(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=Ut(e),n!=null){let s=n.length;O(s===this.size,()=>`Length of values '${s}' does not match the size inferred by the shape '${this.size}'.`)}if(t==="complex64")throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||I5(t,this.size),this.strides=tu(e)}set(e,...t){t.length===0&&(t=[0]),O(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let n=this.locToIndex(t);this.values[n]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let s of e){if(s<0||s>=this.shape[t]){let r=`Requested out of range element at ${e}. 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this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=o():(p=this.profiler.profileKernel(l,c,()=>o()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(p),t=p.outputs)}),s&&this.addTapeNode(l,c,t,d,n,u),this.state.profiling&&this.state.activeProfile.kernels.push({name:l,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(h=>c[h]!=null?c[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:p.timeMs,extraInfo:p.extraInfo}),Array.isArray(i)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(n=>this.keep(this.clone(n)))}getTensorsForGradient(e,t,n){let s=a2(e);if(s!=null){let r=s.inputsToSave||[],a=s.outputsToSave||[],o;s.saveAllInputs?(O(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),o=Object.keys(t).map(l=>t[l])):o=r.map(l=>t[l]);let 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n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Qg(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof vd||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*Qg(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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this.state.activeProfile.kernels)s.kernelTimeMs=await s.kernelTimeMs,s.extraInfo=await s.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,s,r,a){let o={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},i=a2(e);i!=null&&(s=i.gradFunc),s!=null&&(o.gradient=l=>(l=l.map((c,u)=>{if(c==null){let d=n[u],p=mh(d.size,d.dtype);return this.makeTensor(p,d.shape,d.dtype)}return c}),s(l.length>1?l:l[0],r,a))),this.state.activeTape.push(o)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=f2(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let 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Actual: ${r}.
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Actual: ${r}.
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Got strides ${n} and dilations '${a}'`),O(r==="NDHWC",()=>`Error in conv3d: got dataFormat of ${r} but only NDHWC is currently supported.`);let u={x:l,filter:i},d={strides:n,pad:s,dataFormat:r,dilations:a},p=W.runKernel(nd,u,d);return c?G(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var Z2=V({conv3d_:U$});function G$(e,t,n,s,r){O(e.length===t.rank,()=>`Length of inShape (${e.length}) and rank of dy (${t.rank}) must match`);let a=e,o=t,i=!1;t.rank===4&&(i=!0,o=G(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];O(a.length===5,()=>`Error in conv3dDerInput: inShape must be length 5, but got length ${a.length}.`),O(o.rank===5,()=>`Error in conv3dDerInput: dy must be rank 5, but got rank ${o.rank}`),O(n.rank===5,()=>`Error in conv3dDerInput: filter must be rank 5, but got rank ${n.rank}`),O(l===n.shape[3],()=>`Error in conv3dDerInput: depth of input (${l}) must match input depth for filter ${n.shape[3]}.`),O(c===n.shape[4],()=>`Error in conv3dDerInput: depth of output (${c}) must match output depth for filter ${n.shape[4]}.`);let u={dy:o,filter:n},d={pad:r,strides:s,inputShape:a},p=W.runKernel(wh,u,d);return i?G(p,[p.shape[1],p.shape[2],p.shape[3],p.shape[4]]):p}var nv=V({conv3DBackpropInput_:G$});function H$(e,t,n,s,r){let a=D(e,"x","conv3dTranspose"),o=D(t,"filter","conv3dTranspose");return nv(n,a,o,s,r)}var sv=V({conv3dTranspose_:H$});function j$(e){let n={x:D(e,"x","cos","float32")};return W.runKernel(Fa,n)}var cf=V({cos_:j$});function q$(e){let n={x:D(e,"x","cosh","float32")};return W.runKernel(Oa,n)}var Y2=V({cosh_:q$});function X$(e,t=0,n=!1,s=!1){let a={x:D(e,"x","cumsum")},o={axis:t,exclusive:n,reverse:s};return W.runKernel(fi,a,o)}var J2=V({cumsum_:X$});function K$(e,t,n,s=!1){let r=D(e,"x","denseBincount"),a=D(t,"weights","denseBincount");O(r.dtype==="int32",()=>`Error in denseBincount: input dtype must be int32, but got ${r.dtype}`),O(r.rank<=2,()=>`Error in denseBincount: input must be at most rank 2, but got rank ${r.rank}.`),O(n>=0,()=>`size must be non-negative, but got ${n}.`),O(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: ${a.shape}.`);let o={x:r,weights:a},i={size:n,binaryOutput:s};return W.runKernel(kh,o,i)}var rv=V({denseBincount_:K$});function Z$(e,t,n="NHWC"){let s=D(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];O(t>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${t}`),O(r*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${r} and ${t} for depthToSpace with input shape
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${s.shape}`),O(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}`),O(o%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${o} for depthToSpace with input shape ${s.shape}`);let i={x:s},l={blockSize:t,dataFormat:n};return W.runKernel(gi,i,l)}var av=V({depthToSpace_:Z$});function Y$(e,t,n,s,r="NHWC",a=[1,1],o){let i=D(e,"x","depthwiseConv2d","float32"),l=D(t,"filter","depthwiseConv2d","float32"),c=i,u=!1;i.rank===3&&(u=!0,c=G(i,[1,i.shape[0],i.shape[1],i.shape[2]])),O(c.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${c.rank}.`),O(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),O(c.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${c.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),o!=null&&O(fn(s),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${o} but got pad ${s}.`);let d={x:c,filter:l},p={strides:n,pad:s,dataFormat:r,dilations:a,dimRoundingMode:o},h=W.runKernel(Ma,d,p);return u?G(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Nd=V({depthwiseConv2d_:Y$});function J$(e){let n={x:D(e,"x","diag")};return W.runKernel(Ch,n)}var Q$=V({diag_:J$});function e_(e,t,n,s,r=[1,1],a="NHWC"){let o=D(e,"x","dilation2d"),i=D(t,"filter","dilation2d");O(o.rank===3||o.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${o.rank}.`),O(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),O(a==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${a}`);let l=o,c=!1;o.rank===3&&(l=G(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=W.runKernel(sd,u,d);return c?G(p,[p.shape[1],p.shape[2],p.shape[3]]):p}var ov=V({dilation2d_:e_});function t_(e,t){let n=D(e,"a","equal","string_or_numeric"),s=D(t,"b","equal","string_or_numeric");[n,s]=Dt(n,s),mt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ai,r)}var Ts=V({equal_:t_});function n_(e,t,n){let s=D(t,"a","where"),r=D(n,"b","where"),a=D(e,"condition","where","bool"),o=mt(mt(a.shape,s.shape),r.shape),i=Td(a,o),l=Td(s,o),c=Td(r,o),u={condition:i,t:l,e:c};return W.runKernel(Bi,u)}var Wn=V({where_:n_});function s_(e){let n={x:D(e,"x","zerosLike")};return W.runKernel(Zi,n)}var et=V({zerosLike_:s_});function r_(e,t){let n=D(e,"a","div"),s=D(t,"b","div");[n,s]=Dt(n,s);let r=he(n,s),a=et(r),o=Ts(s,a);return Wn(o,a,r)}var iv=V({divNoNan_:r_});function a_(e,t){let n=D(e,"t1","dot"),s=D(t,"t2","dot");O((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(O(r===a,()=>`Error in dot: inner dimensions of inputs must match, but got ${r} and ${a}.`),n.rank===1&&s.rank===1){let o=G(n,[1,-1]),i=G(s,[-1,1]),l=Ue(o,i);return G(l,[])}else if(n.rank===1&&s.rank===2){let o=G(n,[1,-1]),i=G(s,[s.shape[0],s.shape[1]]),l=Ue(o,i);return G(l,[l.size])}else if(n.rank===2&&s.rank===1){let o=G(s,[-1,1]),i=Ue(n,o);return G(i,[i.size])}else{let o=G(s,[s.shape[0],s.shape[1]]);return Ue(n,o)}}var o_=V({dot_:a_});function i_(e,...t){let n=t.map((r,a)=>D(r,`tensors${a}`,"einsum")),s={equation:e};return W.runKernel(rd,n,s)}var lv=V({einsum_:i_});function l_(e){let n={x:D(e,"x","elu","float32")};return W.runKernel(La,n)}var Ed=V({elu_:l_});function u_(e){let t=D(e,"x","erf");O(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=pe(t,"float32"));let n={x:t};return W.runKernel(hu,n)}var uv=V({erf_:u_});function c_(e){let n={x:D(e,"x","exp")};return W.runKernel(Ba,n)}var Ns=V({exp_:c_});function d_(e,t=0){let n=D(e,"x","expandDims","string_or_numeric");O(t<=n.rank,()=>"Axis must be <= rank of the tensor");let s={input:n},r={dim:t};return W.runKernel(yi,s,r)}var Xt=V({expandDims_:d_});function p_(e){let n={x:D(e,"x","expm1")};return W.runKernel(xi,n)}var cv=V({expm1_:p_});function h_(e,t){let n=D(e,"x","tile","string_or_numeric");O(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 W.runKernel(qr,s,r)}var qs=V({tile_:h_});function f_(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=G(r.toTensor(),[e,t]);if(n==null)return o;if(n.length===1)return qs(Xt(o,0),[n[0],1,1]);if(n.length===2)return qs(Xt(Xt(o,0),0),[n[0],n[1],1,1]);if(n.length===3)return qs(Xt(Xt(Xt(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 Q2=V({eye_:f_});function zu(e,t,n){let s={shape:e,value:t,dtype:n};return W.runKernel(fu,{},s)}function m_(e){let n={x:D(e,"x","floor","float32")};return W.runKernel(Wa,n)}var Rd=V({floor_:m_});function g_(e,t,n=0,s=0){let r=D(e,"x","gather"),a=D(t,"indices","gather","int32"),o={x:r,indices:a},i={axis:n,batchDims:s};return W.runKernel(vi,o,i)}var Lu=V({gather_:g_});function A_(e,t){let n=D(e,"a","greater","string_or_numeric"),s=D(t,"b","greater","string_or_numeric");[n,s]=Dt(n,s),mt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(ki,r)}var hs=V({greater_:A_});function y_(e,t){let n=D(e,"a","greaterEqual","string_or_numeric"),s=D(t,"b","greaterEqual","string_or_numeric");[n,s]=Dt(n,s),mt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ga,r)}var ll=V({greaterEqual_:y_});function x_(e){let n={input:D(e,"input","imag")};return W.runKernel(ad,n)}var df=V({imag_:x_});function b_(e){let n={x:D(e,"x","isFinite")};return W.runKernel(mu,n)}var v_=V({isFinite_:b_});function w_(e){let n={x:D(e,"x","isInf")};return W.runKernel(gu,n)}var k_=V({isInf_:w_});function S_(e){let n={x:D(e,"x","isNaN")};return W.runKernel(Au,n)}var dv=V({isNaN_:S_});function I_(e,t=.2){let s={x:D(e,"x","leakyRelu")},r={alpha:t};return W.runKernel(Si,s,r)}var pf=V({leakyRelu_:I_});function C_(e,t){let n=D(e,"a","less","string_or_numeric"),s=D(t,"b","less","string_or_numeric");[n,s]=Dt(n,s),mt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ii,r)}var e1=V({less_:C_});function T_(e,t){let n=D(e,"a","lessEqual","string_or_numeric"),s=D(t,"b","lessEqual","string_or_numeric");[n,s]=Dt(n,s),mt(n.shape,s.shape);let r={a:n,b:s};return W.runKernel(Ci,r)}var ul=V({lessEqual_:T_});function pv(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 W.runKernel(_h,{},s)}function N_(e,t=5,n=1,s=1,r=.5){let a=D(e,"x","localResponseNormalization");O(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}.`),O(fn(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let o=a,i=!1;a.rank===3&&(i=!0,o=G(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=W.runKernel(id,l,c);return i?G(u,[u.shape[1],u.shape[2],u.shape[3]]):u}var hv=V({localResponseNormalization_:N_});function E_(e){let n={x:D(e,"x","log","float32")};return W.runKernel(ja,n)}var Es=V({log_:E_});function R_(e){let n={x:D(e,"x","log1p")};return W.runKernel(yu,n)}var hf=V({log1p_:R_});function $_(e){return O(Ca(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let s=D(t,"x","tf.grad","string_or_numeric"),r=n!=null?D(n,"dy","tf.grad"):null;return W.tidy(()=>{let{value:a,grads:o}=W.gradients(()=>e(s),[s],r);return r!=null&&Mn(a.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),ff(o),o[0]})}}function __(e){return O(Ca(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{O(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let s=kd(t,"args","tf.grads","string_or_numeric"),r=n!=null?D(n,"dy","tf.grads"):null;return W.tidy(()=>{let{value:a,grads:o}=W.gradients(()=>e(...s),s,r);return r!=null&&Mn(a.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),ff(o),o})}}function D_(e){return O(Ca(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{O(t instanceof Je,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),O(n==null||n instanceof Je,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:s,value:r}=W.gradients(()=>e(t),[t],n);return ff(s),{grad:s[0],value:r}}}function P_(e){return O(Ca(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{O(Array.isArray(t)&&t.every(r=>r instanceof Je),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),O(n==null||n instanceof Je,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let s=W.gradients(()=>e(...t),t,n);return n!=null&&Mn(s.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),ff(s.grads),s}}function fv(e,t){O(Ca(e),()=>"The f passed in variableGrads(f) must be a function"),O(t==null||Array.isArray(t)&&t.every(c=>c instanceof vd),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let c in W.registeredVariables)t.push(W.registeredVariables[c])}let s=n?t.filter(c=>!c.trainable):null,r=t.length;t=t.filter(c=>c.trainable),O(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let a=!0,{value:o,grads:i}=W.gradients(e,t,null,a);O(i.some(c=>c!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),O(o.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${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 Rr(e){return W.customGrad(e)}function ff(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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g=0;g<t.length;g++)t[g]>r&&c.push({score:t[g],boxIndex:g,suppressBeginIndex:0});c.sort(Hv);let u=a>0?-.5/a:0,d=[],p=[];for(;d.length<n&&c.length>0;){let g=c.pop(),{score:A,boxIndex:x,suppressBeginIndex:y}=g;if(A<r)break;let b=!1;for(let w=d.length-1;w>=y;--w){let k=wF(e,x,d[w]);if(k>=s){b=!0;break}if(g.score=g.score*kF(s,u,k),g.score<=r)break}g.suppressBeginIndex=d.length,b||(g.score===A?(d.push(x),p.push(g.score)):g.score>r&&yF(c,g,Hv))}let h=d.length,f=n-h;i&&f>0&&(d.push(...new Array(f).fill(0)),p.push(...new Array(f).fill(0)));let m={selectedIndices:d};return o&&(m.selectedScores=p),l&&(m.validOutputs=h),m}function wF(e,t,n){let s=e.subarray(t*4,t*4+4),r=e.subarray(n*4,n*4+4),a=Math.min(s[0],s[2]),o=Math.min(s[1],s[3]),i=Math.max(s[0],s[2]),l=Math.max(s[1],s[3]),c=Math.min(r[0],r[2]),u=Math.min(r[1],r[3]),d=Math.max(r[0],r[2]),p=Math.max(r[1],r[3]),h=(i-a)*(l-o),f=(d-c)*(p-u);if(h<=0||f<=0)return 0;let m=Math.max(a,c),g=Math.max(o,u),A=Math.min(i,d),x=Math.min(l,p),y=Math.max(A-m,0)*Math.max(x-g,0);return y/(h+f-y)}function kF(e,t,n){let s=Math.exp(t*n*n);return n<=e?s:0}function Hv(e,t){return e.score-t.score||e.score===t.score&&t.boxIndex-e.boxIndex}async function SF(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY){let a=D(e,"boxes","nonMaxSuppressionAsync"),o=D(t,"scores","nonMaxSuppressionAsync"),i=Hu(a,o,n,s,r);n=i.maxOutputSize,s=i.iouThreshold,r=i.scoreThreshold;let l=await Promise.all([a.data(),o.data()]),c=l[0],u=l[1],{selectedIndices:d}=Vv(c,u,n,s,r);return a!==e&&a.dispose(),o!==t&&o.dispose(),Kt(d,"int32")}var IF=SF;function CF(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=0){let o=D(e,"boxes","nonMaxSuppression"),i=D(t,"scores","nonMaxSuppression"),l=Hu(o,i,n,s,r,a);n=l.maxOutputSize,s=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma;let c={boxes:o,scores:i},u={maxOutputSize:n,iouThreshold:s,scoreThreshold:r,softNmsSigma:a},d=W.runKernel($i,c,u);return{selectedIndices:d[0],selectedScores:d[1]}}var TF=V({nonMaxSuppressionWithScore_:CF});async function NF(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=0){let o=D(e,"boxes","nonMaxSuppressionAsync"),i=D(t,"scores","nonMaxSuppressionAsync"),l=Hu(o,i,n,s,r,a);n=l.maxOutputSize,s=l.iouThreshold,r=l.scoreThreshold,a=l.softNmsSigma;let c=await Promise.all([o.data(),i.data()]),u=c[0],d=c[1],{selectedIndices:p,selectedScores:h}=Gv(u,d,n,s,r,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:Kt(p,"int32"),selectedScores:Kt(h)}}var EF=NF;function RF(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=!1){let o=D(e,"boxes","nonMaxSuppression"),i=D(t,"scores","nonMaxSuppression"),l=Hu(o,i,n,s,r,null),c=l.maxOutputSize,u=l.iouThreshold,d=l.scoreThreshold,p={boxes:o,scores:i},h={maxOutputSize:c,iouThreshold:u,scoreThreshold:d,padToMaxOutputSize:a},f=W.runKernel(vu,p,h);return{selectedIndices:f[0],validOutputs:f[1]}}var $F=V({nonMaxSuppressionPadded_:RF});async function _F(e,t,n,s=.5,r=Number.NEGATIVE_INFINITY,a=!1){let o=D(e,"boxes","nonMaxSuppressionAsync"),i=D(t,"scores","nonMaxSuppressionAsync"),l=Hu(o,i,n,s,r,null),c=l.maxOutputSize,u=l.iouThreshold,d=l.scoreThreshold,[p,h]=await Promise.all([o.data(),i.data()]),{selectedIndices:f,validOutputs:m}=Uv(p,h,c,u,d,a);return o!==e&&o.dispose(),i!==t&&i.dispose(),{selectedIndices:Kt(f,"int32"),validOutputs:Re(m,"int32")}}var DF=_F;function PF(e,t,n=!1,s=!1){let r=D(e,"images","resizeBilinear");O(r.rank===3||r.rank===4,()=>`Error in resizeBilinear: x must be rank 3 or 4, but got rank ${r.rank}.`),O(t.length===2,()=>`Error in resizeBilinear: new 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a=r,o=!1;r.rank===3&&(o=!0,a=G(r,[1,r.shape[0],r.shape[1],r.shape[2]]));let[]=t,i={images:a},l={alignCorners:n,halfPixelCenters:s,size:t},c=W.runKernel(Su,i,l);return o?G(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var MF=V({resizeNearestNeighbor_:OF});function zF(e,t="binary",n=!1,s=.5){let r=D(e,"image","threshold"),a=.2989,o=.587,i=.114,l=r.shape[0]*r.shape[1],c=L(Kt([s]),255),u,d,p,h;if(O(r.rank===3,()=>`Error in threshold: image must be rank 3,but got rank ${r.rank}.`),O(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]}.`),O(r.dtype==="int32"||r.dtype==="float32",()=>`Error in dtype: image dtype must be int32 or float32,but got dtype ${r.dtype}.`),O(t==="otsu"||t==="binary",()=>`Method must be binary or otsu, but was ${t}`),r.shape[2]===3){[u,d,p]=sn(r,[1,1,1],-1);let g=L(u,a),A=L(d,o),x=L(p,i);h=le(le(g,A),x)}else h=e;if(t==="otsu"){let g=j2(pe(c1(h),"int32"),Pt([]),256);c=LF(g,l)}let f=n?ul(h,c):hs(h,c);return 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VF=V({transform_:WF});function UF(e,t,n){O(t%1==0,()=>`bandPart(): numLower must be an integer, got ${t}.`),O(n%1==0,()=>`bandPart(): numUpper must be an integer, got ${n}.`);let s=D(e,"a","bandPart");O(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=G(Uu(0,a,1,"int32"),[-1,1]),l=Uu(0,o,1,"int32"),c=me(i,l),u=lr(ul(c,Re(+t,"int32")),ll(c,Re(-n,"int32"))),d=Gt([a,o],s.dtype);return G(yn(rs(G(s,[-1,a,o])).map(p=>Wn(u,p,d))),r)}var GF=V({bandPart_:UF});function HF(e){let t;if(Array.isArray(e)){t=!1,O(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)O(e[a].shape[0]===r,()=>`Gram-Schmidt: 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n=e.shape[0],s=e.shape[1],r=Q2(n),a=Bn(e),o=ur([[1]],[1,1]),i=Bn(o),l=n>=s?s:n;for(let c=0;c<l;++c){let u=a,d=i,p=r;[i,a,r]=W.tidy(()=>{let h=De(a,[c,c],[n-c,1]),f=b1(h),m=De(a,[c,c],[1,1]),g=Wn(hs(m,0),ur([[-1]]),ur([[1]])),A=me(m,L(g,f)),x=he(h,A);x.shape[0]===1?i=Bn(o):i=vt([o,De(x,[1,0],[x.shape[0]-1,x.shape[1]])],0);let y=Ot(he(Ue(g,A),f)),b=De(a,[c,0],[n-c,s]),w=L(y,i),k=Qe(i);if(c===0)a=me(b,Ue(w,Ue(k,b)));else{let R=me(b,Ue(w,Ue(k,b)));a=vt([De(a,[0,0],[c,s]),R],0)}let C=Qe(w),N=De(r,[0,c],[n,r.shape[1]-c]);if(c===0)r=me(N,Ue(Ue(N,i),C));else{let R=me(N,Ue(Ue(N,i),C));r=vt([De(r,[0,0],[n,c]),R],1)}return[i,a,r]}),ne([u,d,p])}return!t&&n>s&&(r=De(r,[0,0],[n,s]),a=De(a,[0,0],[s,s])),[r,a]})}var XF=V({qr_:qF}),Vn;(function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"})(Vn||(Vn={}));function KF(e,t,n=Vn.SUM_BY_NONZERO_WEIGHTS){let s=D(e,"losses","computeWeightedLoss"),r=null;t!=null&&(r=D(t,"weights","computeWeightedLoss"));let a=r==null?s:L(s,r);if(n===Vn.NONE)return a;if(n===Vn.SUM)return Se(a);if(n===Vn.MEAN){if(r==null)return Wt(a);{let o=s.size/r.size,i=he(Se(a),Se(r));return o>1?he(i,Re(o)):i}}if(n===Vn.SUM_BY_NONZERO_WEIGHTS){if(r==null)return he(Se(a),Re(s.size));{let o=L(r,fs(s.shape)),i=pe(Se(Wu(o,Re(0))),"float32");return he(Se(a),i)}}throw Error(`Unknown reduction: ${n}`)}var Yr=V({computeWeightedLoss_:KF});function ZF(e,t,n,s=Vn.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","absoluteDifference"),a=D(t,"predictions","absoluteDifference"),o=null;n!=null&&(o=D(n,"weights","absoluteDifference")),Mn(r.shape,a.shape,"Error in absoluteDifference: ");let i=nn(me(r,a));return Yr(i,o,s)}var YF=V({absoluteDifference_:ZF});function JF(e,t,n,s,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","cosineDistance"),o=D(t,"predictions","cosineDistance"),i=null;s!=null&&(i=D(s,"weights","cosineDistance")),Mn(a.shape,o.shape,"Error in cosineDistance: ");let l=Re(1),c=me(l,Se(L(a,o),n,!0));return Yr(c,i,r)}var QF=V({cosineDistance_:JF});function eO(e,t,n,s=Vn.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","hingeLoss"),a=D(t,"predictions","hingeLoss"),o=null;n!=null&&(o=D(n,"weights","hingeLoss")),Mn(r.shape,a.shape,"Error in hingeLoss: ");let i=Re(1);r=me(L(Re(2),r),i);let l=$r(me(i,L(r,a)));return Yr(l,o,s)}var tO=V({hingeLoss_:eO});function nO(e,t,n,s=1,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","huberLoss"),o=D(t,"predictions","huberLoss"),i=null;n!=null&&(i=D(n,"weights","huberLoss")),Mn(a.shape,o.shape,"Error in huberLoss: ");let l=Re(s),c=nn(me(o,a)),u=$d(c,l),d=me(c,u),p=le(L(Re(.5),gt(u)),L(l,d));return Yr(p,i,r)}var sO=V({huberLoss_:nO});function rO(e,t,n,s=1e-7,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"labels","logLoss"),o=D(t,"predictions","logLoss"),i=null;n!=null&&(i=D(n,"weights","logLoss")),Mn(a.shape,o.shape,"Error in logLoss: ");let l=Re(1),c=Re(s),u=Ot(L(a,Es(le(o,c)))),d=L(me(l,a),Es(le(me(l,o),c))),p=me(u,d);return Yr(p,i,r)}var aO=V({logLoss_:rO});function oO(e,t,n,s=Vn.SUM_BY_NONZERO_WEIGHTS){let r=D(e,"labels","meanSquaredError"),a=D(t,"predictions","meanSquaredError"),o=null;n!=null&&(o=D(n,"weights","meanSquaredError")),Mn(r.shape,a.shape,"Error in meanSquaredError: ");let i=A1(r,a);return Yr(i,o,s)}var iO=V({meanSquaredError_:oO});function lO(e,t){let n=D(e,"labels","sigmoidCrossEntropyWithLogits"),s=D(t,"logits","sigmoidCrossEntropyWithLogits");Mn(n.shape,s.shape,"Error in sigmoidCrossEntropyWithLogits: ");let r=$r(s),a=L(s,n),o=hf(Ns(Ot(nn(s))));return le(me(r,a),o)}function uO(e,t,n,s=0,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"multiClassLabels","sigmoidCrossEntropy"),o=D(t,"logits","sigmoidCrossEntropy"),i=null;if(n!=null&&(i=D(n,"weights","sigmoidCrossEntropy")),Mn(a.shape,o.shape,"Error in sigmoidCrossEntropy: "),s>0){let c=Re(s),u=Re(1),d=Re(.5);a=le(L(a,me(u,c)),L(d,c))}let l=lO(a,o);return Yr(l,i,r)}var cO=V({sigmoidCrossEntropy_:uO});function dO(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. Labels / logits was rank ${t.rank} and dim was ${n}`);return Rr((r,a,o)=>{let l=yv(a,[n],!0),c=me(pe(a,"float32"),l);o([r,c]);let u=Ot(L(c,r));return{value:Se(u,[n]),gradFunc:(h,f)=>{let[m,g]=f,A=cl(h.shape,[n]);return[L(G(h,A),me(pe(m,"float32"),Ns(g))),L(G(h,A),me(Ns(g),pe(m,"float32")))]}}})(e,t)}function pO(e,t,n,s=0,r=Vn.SUM_BY_NONZERO_WEIGHTS){let a=D(e,"onehotLabels","softmaxCrossEntropy"),o=D(t,"logits","softmaxCrossEntropy"),i=null;if(n!=null&&(i=D(n,"weights","softmaxCrossEntropy")),Mn(a.shape,o.shape,"Error in softmaxCrossEntropy: "),s>0){let c=Re(s),u=Re(1),d=Re(a.shape[1]);a=le(L(a,me(u,c)),he(c,d))}let l=dO(a,o);return Yr(l,i,r)}var hO=V({softmaxCrossEntropy_:pO});function fO(e,t,n,s){let r=D(e,"indices","sparseFillEmptyRows"),a=D(t,"values","sparseFillEmptyRows"),o=D(n,"denseShape","sparseFillEmptyRows"),i=D(s,"defaultValue","sparseFillEmptyRows",a.dtype);if(r.rank!==2)throw new Error(`Indices should be Tensor2D but received shape
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${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 ${i.shape}`);let l={indices:r,values:a,denseShape:o,defaultValue:i},c=W.runKernel(Bh,l);return{outputIndices:c[0],outputValues:c[1],emptyRowIndicator:c[2],reverseIndexMap:c[3]}}var mO=V({sparseFillEmptyRows_:fO});function gO(e,t,n){let s=D(e,"inputIndices","sparseReshape"),r=D(t,"inputShape","sparseReshape"),a=D(n,"newShape","sparseReshape");if(s.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
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${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=W.runKernel(Wh,o);return{outputIndices:i[0],outputShape:i[1]}}var AO=V({sparseReshape_:gO});function yO(e,t,n){let s=D(e,"data","sparseSegmentMean"),r=D(t,"indices","sparseSegmentMean"),a=D(n,"segmentIds","sparseSegmentMean");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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|
${a.shape}`);let o={data:s,indices:r,segmentIds:a};return W.runKernel(Vh,o)}var xO=V({sparseSegmentMean_:yO});function bO(e,t,n){let s=D(e,"data","sparseSegmentSum"),r=D(t,"indices","sparseSegmentSum"),a=D(n,"segmentIds","sparseSegmentSum");if(s.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${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 W.runKernel(Uh,o)}var vO=V({sparseSegmentSum_:bO});function wO(e,t,n,s,r,a,o,i){let l=D(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=D(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=W.runKernel(dd,d,u);return{nGrams:p[0],nGramsSplits:p[1]}}var kO=V({stringNGrams_:wO});function SO(e,t,n=!0){let s=D(e,"input","stringSplit","string"),r=D(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 ${r.shape}`);let a={skipEmpty:n},o={input:s,delimiter:r},i=W.runKernel(Gh,o,a);return{indices:i[0],values:i[1],shape:i[2]}}var IO=V({stringSplit_:SO});function CO(e,t){let n=D(e,"input","stringToHashBucketFast","string"),s={numBuckets:t};if(t<=0)throw new Error("Number of buckets must be at least 1");let r={input:n};return W.runKernel(Hh,r,s)}var TO=V({stringToHashBucketFast_:CO}),NO={fft:vf,ifft:Pd,rfft:wf,irfft:g1},EO={hammingWindow:sF,hannWindow:Bv,frame:Wv,stft:iF},$e={flipLeftRight:dF,grayscaleToRGB:hF,resizeNearestNeighbor:MF,resizeBilinear:FF,rotateWithOffset:mF,cropAndResize:uF,nonMaxSuppression:AF,nonMaxSuppressionAsync:IF,nonMaxSuppressionWithScore:TF,nonMaxSuppressionWithScoreAsync:EF,nonMaxSuppressionPadded:$F,nonMaxSuppressionPaddedAsync:DF,threshold:BF,transform:VF},qv={bandPart:GF,gramSchmidt:jF,qr:XF},RO={absoluteDifference:YF,computeWeightedLoss:Yr,cosineDistance:QF,hingeLoss:tO,huberLoss:sO,logLoss:aO,meanSquaredError:iO,sigmoidCrossEntropy:cO,softmaxCrossEntropy:hO},Od={sparseFillEmptyRows:mO,sparseReshape:AO,sparseSegmentMean:xO,sparseSegmentSum:vO},Nf={stringNGrams:kO,stringSplit:IO,stringToHashBucketFast:TO},Jr=class extends $3{minimize(e,t=!1,n){let{value:s,grads:r}=this.computeGradients(e,n);if(n!=null){let a=n.map(o=>({name:o.name,tensor:r[o.name]}));this.applyGradients(a)}else 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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)}};Rf.className="Adagrad";To(Rf);var $f=class extends Jr{constructor(e,t,n,s=null){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],q(()=>{this.accBeta1=Re(t).variable(),this.accBeta2=Re(n).variable()}),s==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);q(()=>{let n=me(1,this.accBeta1),s=me(1,this.accBeta2);t.forEach((r,a)=>{let 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m=le(L(he(h,le(Dn(f),this.epsilon)),-this.learningRate),o);o.assign(m)}),this.accBeta1.assign(L(this.accBeta1,this.beta1)),this.accBeta2.assign(L(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&ne(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),q(()=>{this.accBeta1.assign($o(this.beta1,this.iterations_+1)),this.accBeta2.assign($o(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};$f.className="Adam";To($f);var _f=class extends Jr{constructor(e,t,n,s=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=s,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],q(()=>{this.iteration=Re(0).variable(),this.accBeta1=Re(t).variable()}),s==null&&(this.epsilon=W.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);q(()=>{let n=me(1,this.accBeta1),s=he(-this.learningRate,le(L(this.iteration,this.decay),1));t.forEach((r,a)=>{let o=W.registeredVariables[r],i=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${r}/m`,variable:et(o).variable(i)}),this.accumulatedWeightedInfNorm[a]==null&&(this.accumulatedWeightedInfNorm[a]={originalName:`${r}/v`,variable:et(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.accumulatedWeightedInfNorm[a].variable,d=le(L(c,this.beta1),L(l,1-this.beta1)),p=L(u,this.beta2),h=nn(l),f=Zr(p,h);c.assign(d),u.assign(f);let m=le(L(he(s,n),he(d,le(f,this.epsilon))),o);o.assign(m)}),this.iteration.assign(le(this.iteration,1)),this.accBeta1.assign(L(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&ne(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&ne(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new 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)}};_f.className="Adamax";To(_f);var Md=class extends Jr{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=W.registeredVariables[n];q(()=>{let o=le(L(this.c,r),a);a.assign(o)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=gn(Re(-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 not have settable weights.")}getConfig(){return{learningRate:this.learningRate}}static fromConfig(e,t){return new e(t.learningRate)}};Md.className="SGD";To(Md);var Df=class extends Md{constructor(e,t,n=!1){super(e);this.learningRate=e,this.momentum=t,this.useNesterov=n,this.accumulations=[],this.m=Re(this.momentum)}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n];if(this.accumulations[s]==null){let i=!1;this.accumulations[s]={originalName:`${n}/momentum`,variable:q(()=>et(r).variable(i))}}let a=this.accumulations[s].variable,o=Array.isArray(e)?e[s].tensor:e[n];o!=null&&q(()=>{let i,l=le(L(this.m,a),o);this.useNesterov?i=le(L(this.c,le(o,L(l,this.m))),r):i=le(L(this.c,l),r),a.assign(l),r.assign(i)})}),this.incrementIterations()}dispose(){this.m.dispose(),this.accumulations!=null&&ne(this.accumulations.map(e=>e.variable))}setMomentum(e){this.momentum=e}async getWeights(){return[await this.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Df.className="Momentum";To(Df);var Pf=class extends Jr{constructor(e,t=.9,n=0,s=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=s,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,s==null&&(this.epsilon=W.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(n=>n.name):Object.keys(e)).forEach((n,s)=>{let r=W.registeredVariables[n],a=!1;this.accumulatedMeanSquares[s]==null&&(this.accumulatedMeanSquares[s]={originalName:`${n}/rms`,variable:q(()=>et(r).variable(a))}),this.accumulatedMoments[s]==null&&(this.accumulatedMoments[s]={originalName:`${n}/momentum`,variable:q(()=>et(r).variable(a))}),this.accumulatedMeanGrads[s]==null&&this.centered&&(this.accumulatedMeanGrads[s]={originalName:`${n}/mg`,variable:q(()=>et(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;q(()=>{let c=le(L(i,this.decay),L(gt(o),1-this.decay));if(this.centered){let u=this.accumulatedMeanGrads[s].variable,d=le(L(u,this.decay),L(o,1-this.decay)),p=he(L(o,this.learningRate),Dn(me(c,le(gt(d),this.epsilon)))),h=le(L(l,this.momentum),p);i.assign(c),u.assign(d),l.assign(h);let f=me(r,h);r.assign(f)}else{let u=le(L(i,this.decay),L(gt(o),1-this.decay)),d=le(L(l,this.momentum),he(L(o,this.learningRate),Dn(le(u,this.epsilon))));i.assign(u),l.assign(d);let p=me(r,d);r.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&ne(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&ne(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&ne(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 t=this.centered?e.length/3:e.length/2,n=!1;this.accumulatedMeanSquares=e.slice(0,t).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.accumulatedMoments=e.slice(t,t*2).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})),this.centered&&(this.accumulatedMeanGrads=e.slice(t*2,t*3).map(s=>({originalName:s.name,variable:s.tensor.variable(n)})))}getConfig(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}}static fromConfig(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)}};Pf.className="RMSProp";To(Pf);var Do=class{static sgd(e){return new Md(e)}static momentum(e,t,n=!1){return new Df(e,t,n)}static rmsprop(e,t=.9,n=0,s=null,r=!1){return new Pf(e,t,n,s,r)}static adam(e=.001,t=.9,n=.999,s=null){return new $f(e,t,n,s)}static adadelta(e=.001,t=.95,n=null){return new Ef(e,t,n)}static adamax(e=.002,t=.9,n=.999,s=null,r=0){return new _f(e,t,n,s,r)}static 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this.assertNotDisposed(),rB(this.val,e),this.val.id!==e.id&&(this.val.assign(e),this.constraint!=null&&this.val.assign(this.constraint.apply(this.val))),this}dispose(){this.assertNotDisposed(),this.val.dispose()}assertNotDisposed(){if(this.val.isDisposed)throw new Error(`LayersVariable ${this.name} is already disposed.`)}get trainable(){return this.trainable_}set trainable(e){this.trainable_=e,this.val.trainable=e}};function rB(e,t){if(e.shape.toString()!==t.shape.toString())throw new Error("Shape mismatch: "+JSON.stringify(e.shape)+" vs. "+JSON.stringify(t.shape))}function X1(e){return e.map(t=>t.read())}function K1(e){e.forEach(t=>{t[0].write(t[1])})}var Zt=class{constructor(e){this.dtype=e.dtype,this.shape=e.shape,e.shape!=null?this.ndim=e.shape.length:this.ndim=e.ndim,this.maxNDim=e.maxNDim,this.minNDim=e.minNDim,this.axes=e.axes||{}}},fr=class{constructor(e,t,n,s,r,a,o){this.dtype=e,this.shape=t,this.sourceLayer=n,this.inputs=s,this.callArgs=r,this.outputTensorIndex=o,this.id=Tw(),a!=null&&(this.originalName=yw(a),this.name=xw(this.originalName)),this.rank=t.length}},aB=0,Zf=class{constructor(e,t){this.callArgs=t,this.id=aB++,this.outboundLayer=e.outboundLayer,this.inboundLayers=e.inboundLayers,this.nodeIndices=e.nodeIndices,this.tensorIndices=e.tensorIndices,this.inputTensors=e.inputTensors,this.outputTensors=e.outputTensors,this.inputMasks=e.inputMasks,this.outputMasks=e.outputMasks,this.inputShapes=e.inputShapes,this.outputShapes=e.outputShapes;for(let n of e.inboundLayers)n!=null&&n.outboundNodes.push(this);e.outboundLayer.inboundNodes.push(this)}getConfig(){let e=[];for(let t of this.inboundLayers)t!=null?e.push(t.name):e.push(null);return{outboundLayer:this.outboundLayer?this.outboundLayer.name:null,inboundLayers:e,nodeIndices:this.nodeIndices,tensorIndices:this.tensorIndices}}},oB=0,tt=class extends ue.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=oB++,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=ea(n)+"_"+qf(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 dr(`The layer has never been called and thus has no defined ${t}.`);if(this.inboundNodes.length<=e)throw new H(`Asked to get ${t} at node ${e}, but the layer has only ${this.inboundNodes.length} inbound nodes.`);return this.inboundNodes[e]}getInputAt(e){return as(this.getNodeAtIndex(e,"input").inputTensors)}getOutputAt(e){return as(this.getNodeAtIndex(e,"output").outputTensors)}get input(){if(this.inboundNodes.length>1)throw new Qr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use \`getInputAt(nodeIndex)\` instead.`);if(this.inboundNodes.length===0)throw new Qr(`Layer ${this.name} is not connected, no input to return.`);return as(this.getNodeAtIndex(0,"input").inputTensors)}get output(){if(this.inboundNodes.length===0)throw new Qr(`Layer ${this.name} has no inbound nodes.`);if(this.inboundNodes.length>1)throw new Qr(`Layer ${this.name} has multiple inbound nodes, hence the notion of "layer output" is ill-defined. Use \`getOutputAt(nodeIndex)\` instead.`);return as(this.getNodeAtIndex(0,"output").outputTensors)}get losses(){return this._losses}calculateLosses(){return this.losses.map(e=>e())}get updates(){return this._updates}get built(){return this._built}set built(e){this._built=e}get trainable(){return this.trainable_}set trainable(e){this._trainableWeights.forEach(t=>t.trainable=e),this.trainable_=e}get trainableWeights(){return this.trainable_?this._trainableWeights.filter(e=>e.trainable):[]}set trainableWeights(e){this._trainableWeights=e}get nonTrainableWeights(){return this.trainable?this._trainableWeights.filter(e=>!e.trainable).concat(this._nonTrainableWeights):this._trainableWeights.concat(this._nonTrainableWeights)}set nonTrainableWeights(e){this._nonTrainableWeights=e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}get stateful(){return this._stateful}resetStates(){if(!this.stateful)throw new Error("Cannot call the resetStates() method of a non-stateful Layer object.")}assertInputCompatibility(e){if(e=kt(e),this.inputSpec==null||this.inputSpec.length===0)return;let t=kt(this.inputSpec);if(e.length!==t.length)throw new H(`Layer ${this.name} expects ${t.length} inputs, but it received ${e.length} input tensors. 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 H(`Input ${n} is incompatible with layer ${this.name}: expected ndim=${r.ndim}, found ndim=${a}`);if(r.maxNDim!=null&&a>r.maxNDim)throw new H(`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 H(`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 H(`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 H(`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 H(`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=kt(e),s=!0;for(let a of n)if(!(a instanceof fr)){s=!1;break}let r=!0;for(let a of n)if(a instanceof fr){r=!1;break}if(s===r)throw new H("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 kt(e))a.push(o.shape);this.build(as(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=kt(a),i=[];for(let l of o)n.indexOf(l)!==-1&&(l=l.clone()),i.push(l);if(a=as(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=iB(e),o=this.computeOutputShape(a),i,l=lB(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 fr(l,c,this,kt(e),t,this.name,u)):i=new fr(l,o,this,kt(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 Qr(`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 Qr(`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 dr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return Kf(this.weights)}build(e){this.built=!0}getWeights(e=!1){return X1(e?this.trainableWeights:this.weights)}setWeights(e){q(()=>{let t=this.weights;if(t.length!==e.length)throw new H(`You called setWeights(weights) on layer "${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=X1(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 H(`Layer weight shape ${a.shape} not compatible with provided weight shape ${i.shape}`);n.push([o,i])}K1(n)})}addWeight(e,t,n,s,r,a,o,i){if(this._addedWeightNames.indexOf(e)!==-1)throw new H(`Duplicate weight name ${e} for layer ${this.name}`);this._addedWeightNames.push(e),n==null&&(n="float32"),this.fastWeightInitDuringBuild&&(s=i!=null?i():Nt("zeros"));let l=s.apply(t,n),c=new Ew(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=kt(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 ${this.name} does not support masking, but was passed an inputMask.`);return null}return t}addInboundNode(e,t,n,s,r,a,o=null){let i=kt(e);t=kt(t),n=kt(n),s=kt(s),r=Xf(r),a=Xf(a);let l=[],c=[],u=[];for(let d of i)l.push(d.sourceLayer),c.push(d.nodeIndex),u.push(d.tensorIndex);new Zf({outboundLayer:this,inboundLayers:l,nodeIndices:c,tensorIndices:u,inputTensors:i,outputTensors:t,inputMasks:n,outputMasks:s,inputShapes:r,outputShapes:a},o);for(let d=0;d<t.length;d++)t[d].sourceLayer=this,t[d].nodeIndex=this.inboundNodes.length-1,t[d].tensorIndex=d}getConfig(){let e={name:this.name,trainable:this.trainable};return this.batchInputShape!=null&&(e.batchInputShape=this.batchInputShape),this.dtype!=null&&(e.dtype=this.dtype),e}disposeWeights(){return this.weights.forEach(e=>e.dispose()),this.weights.length}assertNotDisposed(){if(this._refCount===0)throw new Error(`Layer '${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 iB(e){e=kt(e);let t=[];for(let n of e)t.push(n.shape);return as(t)}function lB(e){return"float32"}function Rw(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=Rw(o,i,l);for(let u of c)r.indexOf(u)===-1&&r.push(u)}return r}}}var Xu=class extends tt{constructor(e){super({dtype:e.dtype,name:e.name!=null?e.name:qf("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 H("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 H("An InputLayer should be passed either a `batchInputShape` or an `inputShape`.");t=[e.batchSize].concat(e.inputShape)}else if(e.batchSize!=null)throw new H("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 fr(this.dtype,this.batchInputShape,this,[],{},this.name);s.nodeIndex=0,s.tensorIndex=0,new Zf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:[s],outputTensors:[s],inputMasks:[null],outputMasks:[null],inputShapes:[t],outputShapes:[t]})}apply(e,t){throw new H(`Cannot pass any input to an InputLayer's apply() method. InputLayer name: ${this.name}`)}dispose(){return{refCountAfterDispose:this._refCount,numDisposedVariables:0}}getConfig(){return{batchInputShape:this.batchInputShape,dtype:this.dtype,sparse:this.sparse,name:this.name}}};Xu.className="InputLayer";ue.registerClass(Xu);function $w(e){if(e.batchShape==null&&e.shape==null)throw new Error("Please provide to Input either a `shape` or a `batchShape` argument. Note that `shape` does not include the batch dimension.");if(e.batchShape!=null&&e.shape!=null)throw new H("Please provide either a `shape` or `batchShape` argument to Input, but not both.");let t=e.batchShape;e.shape!=null&&t==null&&(t=[null].concat(e.shape));let n=e.dtype;return n==null&&(n="float32"),new Xu({batchInputShape:t,name:e.name,dtype:n,sparse:e.sparse}).inboundNodes[0].outputTensors[0]}async function Mo(e){if(e==null)return;let t=[],n=[],s=[];for(let r in e){let a=e[r];if(typeof a!="number"){let o=a;t.push(o.data()),n.push(r),s.push(o)}}if(t.length>0){let r=await Promise.all(t);for(let a=0;a<r.length;++a)e[n[a]]=r[a][0];ne(s)}}function _w(e){if(e!=null)for(let t in e){let n=e[t];typeof n!="number"&&n.dispose()}}var Dw;(function(e){e[e.SILENT=0]="SILENT",e[e.VERBOSE=1]="VERBOSE"})(Dw||(Dw={}));var uB=125,Ku=class{constructor(){this.validationData=null}setParams(e){this.params=e}async onEpochBegin(e,t){}async onEpochEnd(e,t){}async onBatchBegin(e,t){}async onBatchEnd(e,t){}async onTrainBegin(e){}async onTrainEnd(e){}setModel(e){}},Pw=class{constructor(e,t=10){e==null&&(e=[]),this.callbacks=e,this.queueLength=t}append(e){this.callbacks.push(e)}setParams(e){for(let t of this.callbacks)t.setParams(e)}setModel(e){for(let t of this.callbacks)t.setModel(e)}async onEpochBegin(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onEpochBegin(e,t)}async onEpochEnd(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onEpochEnd(e,t)}async onBatchBegin(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onBatchBegin(e,t)}async onBatchEnd(e,t){t==null&&(t={});for(let n of this.callbacks)await n.onBatchEnd(e,t)}async onTrainBegin(e){e==null&&(e={});for(let t of this.callbacks)await t.onTrainBegin(e)}async onTrainEnd(e){e==null&&(e={});for(let t of this.callbacks)await t.onTrainEnd(e)}},cB=class extends Ku{constructor(){super()}async onEpochBegin(e){this.seen=0,this.totals={}}async onBatchEnd(e,t){t==null&&(t={});let n=t.size==null?0:t.size;this.seen+=n;for(let s in t){let r=t[s];if(typeof r=="number")this.totals.hasOwnProperty(s)||(this.totals[s]=0),this.totals[s]=this.totals[s]+r*n;else{let a;s in this.totals?a=this.totals[s]:this.totals[s]=0;let o=q(()=>le(this.totals[s],L(r,n)));this.totals[s]=o,a!=null&&a.dispose()}}}async onEpochEnd(e,t){if(t!=null)for(let n of this.params.metrics)this.totals[n]!=null&&(typeof this.totals[n]=="number"?t[n]=this.totals[n]/this.seen:q(()=>{let s=L(he(1,this.seen),this.totals[n]);t[n]=s,this.totals[n].dispose(),gn(t[n])}))}},Fw=class extends Ku{async onTrainBegin(e){this.epoch=[],this.history={}}async onEpochEnd(e,t){t==null&&(t={}),this.epoch.push(e);for(let n in t)this.history[n]==null&&(this.history[n]=[]),this.history[n].push(t[n])}async syncData(){let e=[],t=[],n=[];for(let r in this.history){let a=this.history[r];for(let o=0;o<a.length;++o)if(typeof a[o]!="number"){let i=a[o];e.push(i.data()),t.push(r),n.push(o)}}let s=await Promise.all(e);for(let r=0;r<s.length;++r)this.history[t[r]][n[r]].dispose(),this.history[t[r]][n[r]]=s[r][0]}},Ow=class extends Ku{constructor(e,t){super();if(this.currentEpoch=0,this.nowFunc=e.nowFunc,this.nextFrameFunc=e.nextFrameFunc||Xv,this.yieldEvery=t||"auto",this.yieldEvery==="auto"&&(this.yieldEvery=uB),this.yieldEvery==="never"&&e.onYield!=null)throw new Error("yieldEvery is `never` but you provided an `onYield` callback. 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tt{constructor(e){super({});if(this.containerNodes=new Set,this.name=e.name,this.name==null){let A=this.getClassName().toLowerCase();this.name=qf(A)}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],Po(this.inputs).length!==this.inputs.length)throw new H(`The list of inputs passed to the model is redundant. All inputs should only appear once. Found: ${this.inputs.map(A=>A.name)}`);Po(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(A=>A.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let A of this.outputs){let x=A.sourceLayer,y=A.nodeIndex,b=A.tensorIndex;this.outputLayers.push(x),this.outputLayersNodeIndices.push(y),this.outputLayersTensorIndices.push(b)}for(let A of this.inputs){let x=A.sourceLayer,y=A.nodeIndex,b=A.tensorIndex;_r(y===0,"input layer has >1 nodes"),_r(b===0,"input layer has >1 tensors"),this.inputLayers.push(x),this.inputLayersNodeIndices.push(y),this.inputLayersTensorIndices.push(b)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let A=0;A<this.inputLayers.length;A++){let x=this.inputLayers[A];if(!(x instanceof Xu))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${A} (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 A of this.outputLayers)this.outputNames.push(A.name);this.internalInputShapes=this.inputs.map(A=>A.shape),this.internalOutputShapes=this.outputs.map(A=>A.shape);let t={},n={},s={},r={},a={},o=[],i=(A,x,y,b,w,k)=>{(b==null||w==null||k==null)&&(b=A.sourceLayer,w=A.nodeIndex,k=A.tensorIndex);let C=b.inboundNodes[w];if(y.indexOf(C)!==-1)throw new dr(`The tensor ${A.name} at layer "${b.name}" is part of a cycle.`);if(x.indexOf(C)!==-1)return;this.containerNodes.add(Fr.nodeKey(b,w)),b.id in a||(a[b.id]=Object.keys(a).length),y.indexOf(C)===-1&&y.push(C);let N=C.inboundLayers.length;for(let R=0;R<N;R++){let F=C.inputTensors[R],_=C.inboundLayers[R],P=C.nodeIndices[R],T=C.tensorIndices[R];i(F,x,y,_,P,T)}for(x.push(C);y.indexOf(C)>=0;)y.splice(y.indexOf(C),1);o.push(C)},l=[],c=[];for(let A of this.outputs)i(A,l,c);let u=o.slice().reverse();for(let A of u){n[A.id]=A,A.id in t||(t[A.id]=0);let x=t[A.id],y=s[A.outboundLayer.id]==null?0:s[A.outboundLayer.id];x=Math.max(x,y),s[A.outboundLayer.id]=x,r[A.outboundLayer.id]=A.outboundLayer,t[A.id]=x;for(let b=0;b<A.inboundLayers.length;b++){let w=A.inboundLayers[b],k=A.nodeIndices[b],C=w.inboundNodes[k],N=t[C.id]==null?0:t[C.id];t[C.id]=Math.max(x+1,N),n[C.id]=C}}let d={};for(let A in t){let x=t[A];x in d||(d[x]=[]),d[x].push(n[A])}let p={};for(let A in s){let x=s[A];x in p||(p[x]=[]),p[x].push(r[A])}let h=Object.keys(p).map(A=>parseInt(A,10)).sort(Ff);this.layers=[];for(let A of h){let x=p[A];x.sort((y,b)=>{let w=a[y.id],k=a[b.id];return w<k?-1:w>k?1:0});for(let y of x)y instanceof Fr&&this.internalContainerRefs.push(y),this.layers.push(y)}this.layersByDepth=p,h=Object.keys(d).map(A=>parseInt(A,10)).sort(Ff);let f=this.inputs.slice(),m=[];for(let A of h)for(let x of d[A]){let y=x.outboundLayer;if(y!=null){for(let b of x.inputTensors)if(f.indexOf(b)===-1)throw new dr(`Graph disconnected: cannot obtain value for tensor ${b} at layer "${y.name}". The following previous layers were accessed without issue: ${m}`);for(let b of x.outputTensors)f.push(b);m.push(y.name)}}this.nodesByDepth=d;let g=this.layers.map(A=>A.name);for(let A of g){let x=g.filter(y=>y===A).length;if(x!==1)throw new dr(`The name "${A}" is used ${x} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(g))}this.outboundNodes=[],this.inboundNodes=[],new Zf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(A=>null),outputMasks:this.outputs.map(A=>null),inputShapes:this.inputs.map(A=>A.shape),outputShapes:this.outputs.map(A=>A.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${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 H("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 H(`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 H(`Provided weight data has no target variable: ${a}`);delete n[o]}if(t){let a=[];for(let o in n)a.push(o);if(a.length>0)throw new H(`${a.length} of ${s} weights are not set: ${a}`)}K1(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${rA}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=sA(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return q(()=>{e=kt(e);let n=new bl;for(let s=0;s<this.inputs.length;++s)n.add(this.inputs[s],e[s]);return jd(this.outputs,n,t)})}computeMask(e,t){return q(()=>{e=kt(e);let n;return t==null?n=fl(null,e.length):n=kt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=Xf(e);if(t.length!==this.inputLayers.length)throw new H(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let o=0;o<t.length;o++){let i=this.inputLayers[o],l=t[o],c=i.name+"_0_0";n[c]=l}let s=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(Ff);if(s.length>1)for(let o of s){let i=this.nodesByDepth[o];for(let l of i){let c=l.outboundLayer;if(this.inputLayers.map(f=>f.id).indexOf(c.id)!==-1)continue;let u=[];for(let f=0;f<l.inboundLayers.length;f++){let m=l.inboundLayers[f],g=l.nodeIndices[f],A=l.tensorIndices[f],x=`${m.name}_${g}_${A}`,y=n[x];u.push(y)}let d=c.computeOutputShape(as(u)),p=Xf(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];_r(i in n),r.push(n[i])}return as(r)}runInternalGraph(e,t){t==null&&(t=fl(null,e.length));let n={};for(let i=0;i<this.inputs.length;++i){let l=this.inputs[i],c=e[i],u=t[i];n[l.id]=[c,u]}let s=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(Ff);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,A,x;if(c.callArgs!=null&&(f=c.callArgs),h.length===1){let[y,b]=h[0];f.mask==null&&(f.mask=b),A=kt(u.call(y,f)),x=kt(u.computeMask(y,b)),m=[y],g=[b]}else m=h.map(y=>y[0]),g=h.map(y=>y[1]),f.mask==null&&(f.mask=g),A=kt(u.call(m,f)),x=kt(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 y=0;y<p.length;++y){let b=p[y],w=A[y],k=x[y];n[b.id]=[w,k]}}}}let r=[],a=[],o=[];for(let i of this.outputs){_r(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 Fr?1:0;for(let r=0;r<s.inboundNodes.length;r++){let a=Fr.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 H(`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 H("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new H(`No such layer: ${e}`)}calculateLosses(){return q(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let s=Fr.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=Fr.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],A=d.nodeIndices[m],x=d.tensorIndices[m],y=Fr.nodeKey(g,A),b=t[y];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=Fr.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=Fr.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 A=[],x;for(let y of g){let b=y[0],w=y[1],k=y[2];if(x=y[3]==null?{}:y[3],!(b in r)){o(m,g);return}let C=r[b];if(C.inboundNodes.length<=w){o(m,g);return}let N=C.inboundNodes[w];A.push(N.outputTensors[k])}A.length>0&&m.apply(as(A),x)}function l(m){let g=m.name,A=mr(m,t.customObjects!=null?t.customObjects:{});A.setFastWeightInitDuringBuild(s),r[g]=A,m.inboundNodes.forEach(y=>{if(!(y instanceof Array))throw new H(`Corrupted configuration, expected array for nodeData: ${y}`);o(A,y)})}let c=t.name,u=t.layers;for(let m of u)l(m);for(;!gL(a);)for(let m of u){let g=r[m.name];if(g.name in a){let A=a[g.name];delete a[g.name];for(let x of A)i(g,x)}}let d=[],p=[],h=t.inputLayers;for(let m of h){let g=m[0],A=m[1],x=m[2];_r(g in r);let b=r[g].inboundNodes[A].outputTensors;d.push(b[x])}let f=t.outputLayers;for(let m of f){let g=m[0],A=m[1],x=m[2];_r(g in r);let b=r[g].inboundNodes[A].outputTensors;p.push(b[x])}return new e({inputs:d,outputs:p,name:c})}get stateful(){if(this._stateful)throw new H("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(){q(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function BB(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: ${JSON.stringify(e)}`)}function Kw(e,t){return BB(e,t,"classWeight")}async function Zw(e,t,n,s){if(t!=null||s!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=q(()=>{if(e.shape.length===1)return Bn(e);if(e.shape.length===2){if(e.shape[1]>1)return js(e,1);if(e.shape[1]===1)return G(e,[e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${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());ne(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])}),Kt(o,"float32")}else return null}function WB(e,t){return L(e,t)}var VB=32;function Yw(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=Jw("input",e.inputNames,n),o=Jw("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 Jw(e,t,n){if(n instanceof Je)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 H(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);s.push(n[r])}return s}}function UB(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 GB(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(Qw(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=UB(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=Mw(n.callbacks,n.yieldEvery),d=n.verbose==null?1:n.verbose,{callbackList:p,history:h}=zw(u,d,n.epochs,null,null,HB(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 A=0,x=0;for(s||(m=await t.iterator());s?A<n.batchesPerEpoch:!0;){let y=await m.next();if(s&&y.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${A} batches; interrupting training. 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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 bl(u),p=jd(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=WB(g,r[f]));let A=Wt(g);t.push(A),f===0?h=g:h=le(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],A=this.metricsTensors[f][1];m=Wt(g(s[A],p[A]))}gn(m),a.push(m)}return h=Wt(h),this.calculateLosses().forEach(f=>{h=le(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=>q(()=>{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 bl(a),i=jd(this.outputs,o);for(let l=0;l<this.lossFunctions.length;++l){let c=this.lossFunctions[l],u=Wt(c(r[l],i[l]));l===0?n=u:n=le(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=Wt(c(r[u],i[u]));t.push(d)}return t})}async fit(e,t,n={}){return KB(this,e,t,n)}async fitDataset(e,t){return GB(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 ne(o),as(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=tf().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-tf().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=ea(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=>ea(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]=ea(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[ea(sm(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>ea(sm(e)));{let e={};for(let t in this.metrics)e[t]=ea(sm(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=Hd(e.optimizer_config),n=mr(t),s;if(typeof e.loss=="string")s=ml(e.loss);else if(Array.isArray(e.loss))s=e.loss.map(a=>ml(a));else if(e.loss!=null){s={};for(let a in e.loss)s[a]=ml(e.loss[a])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(a=>ml(a));else if(e.metrics!=null){r={};for(let a in e.metrics)r[a]=ml(e.metrics[a])}this.compile({loss:s,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let l=ns.getSaveHandlers(e);if(l.length===0)throw new H(`Cannot find any save handlers for URL '${e}'`);if(l.length>1)throw new H(`Found more than one (${l.length}) save handlers for URL '${e}'`);e=l[0]}if(e.save==null)throw new H("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await ns.encodeWeights(this.getNamedWeights(t)),s=!1,r=null,o={modelTopology:this.toJSON(r,s),format:eW,generatedBy:`TensorFlow.js tfjs-layers v${rA}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){o.trainingConfig=this.getTrainingConfig();let l="optimizer",{data:c,specs:u}=await ns.encodeWeights(await this.optimizer.getWeights(),l);n.specs.push(...u),n.data=ns.concatenateArrayBuffers([n.data,c])}if(this.userDefinedMetadata!=null){let l=!0;Hw(this.userDefinedMetadata,this.name,l),o.userDefinedMetadata=this.userDefinedMetadata}return o.weightData=n.data,o.weightSpecs=n.specs,e.save(o)}setUserDefinedMetadata(e){Hw(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};ta.className="Model";ue.registerClass(ta);var rk=class extends ta{};rk.className="Functional";ue.registerClass(rk);async function tW(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let s=Hd(n),r=mr(s,t);if(e.weightsManifest!=null){let a=await ns.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(i=>i.originalName)),o={};for(let i of r.weights)o[i.originalName]=a[i.originalName];r.loadWeights(o),ne(a)}return r}async function nW(e,t){if(t==null&&(t={}),typeof e=="string"){let n=ns.getLoadHandlers(e,t);if(n.length===0)n.push(ns.browserHTTPRequest(e,t));else if(n.length>1)throw new H(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return sW(e,void 0,t)}async function sW(e,t,n){if(n==null&&(n={}),e.load==null)throw new H("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=mr(Hd(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 H("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:c,optimizerWeights:u}=rW(s.weightData,s.weightSpecs);i.loadWeights(c,a),i.optimizer!=null&&u.length>0&&await i.optimizer.setWeights(u),ne(c),ne(u.map(d=>d.tensor))}return i}function rW(e,t){let n=ns.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 cA=class extends ta{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:qf("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 H(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof cA||e instanceof ta,n;if(t){if(n=e,n.outputs.length!==1)throw new H("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 H("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 H("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let s=$w({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 H(`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 H("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=Rw(this.outputs[0])}this.inboundNodes=[],new Zf({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:fl(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(pt(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 ta({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 dr("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 dr("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 dr("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 dr("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 H("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 cA))throw new Le(`Sequential.fromConfig called on non-Sequential input: ${o}`);for(let i of r){let c=mr(i,void 0,s);s&&c.setFastWeightInitDuringBuild(!0),o.add(c)}return o}set stopTraining(e){if(this.model==null)throw new H("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 H("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}}},am=cA;am.className="Sequential";ue.registerClass(am);function aW(e){return new ta(e)}function oW(e){return new am(e)}function iW(e,t){return t==null&&(t={}),nW(e,t)}function ak(e){return $w(e)}function lW(e,t){Z1.registerCallbackConstructor(e,t)}var is=class extends ue.Serializable{getConfig(){return{}}},ok=class extends is{apply(e,t=1){return PL(e,t)}};ok.className="elu";ue.registerClass(ok);var ik=class extends is{apply(e){return p1(e)}};ik.className="selu";ue.registerClass(ik);var lk=class extends is{apply(e){return $r(e)}};lk.className="relu";ue.registerClass(lk);var uk=class extends is{apply(e){return q(()=>$d(6,$r(e)))}};uk.className="relu6";ue.registerClass(uk);var ck=class extends is{apply(e){return e}};ck.className="linear";ue.registerClass(ck);var dk=class extends is{apply(e){return ds(e)}};dk.className="sigmoid";ue.registerClass(dk);var pk=class extends is{apply(e){return OL(e)}};pk.className="hardSigmoid";ue.registerClass(pk);var hk=class extends is{apply(e){return Bu(e)}};hk.className="softplus";ue.registerClass(hk);var fk=class extends is{apply(e){return FL(e)}};fk.className="softsign";ue.registerClass(fk);var mk=class extends is{apply(e){return Fu(e)}};mk.className="tanh";ue.registerClass(mk);var dA=class extends is{apply(e,t=-1){return Gu(e,t)}};dA.className="softmax";ue.registerClass(dA);var gk=class extends is{apply(e,t=-1){return t1(e,t)}};gk.className="logSoftmax";ue.registerClass(gk);var Ak=class extends is{apply(e,t=1){return q(()=>L(ds(L(e,t)),e))}};Ak.className="swish";ue.registerClass(Ak);var yk=class extends is{apply(e){return q(()=>L(e,Fu(Bu(e))))}};yk.className="mish";ue.registerClass(yk);function zo(e){return e.getClassName()}function pA(e,t={}){return zd(e,ue.SerializationMap.getMap().classNameMap,t,"activation")}function Lo(e){if(e==null){let t={};return t.className="linear",t.config={},pA(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},pA(t)}else return e instanceof is?e:pA(e)}function hA(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var xk=class extends ue.Serializable{},Xd=class extends xk{constructor(e){super();hA(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return q(()=>{let t=Gt([1]);return this.hasL1&&(t=le(t,Se(L(this.l1,nn(e))))),this.hasL2&&(t=le(t,Se(L(this.l2,Vd(e))))),G(t,[])})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Xd.className="L1L2";ue.registerClass(Xd);function uW(e){return hA(e),new Xd({l1:e!=null?e.l1:null,l2:0})}function cW(e){return hA(e),new Xd({l2:e!=null?e.l2:null,l1:0})}var bk={l1l2:"L1L2"};function At(e){return N1(e)}function vk(e,t={}){return zd(e,ue.SerializationMap.getMap().classNameMap,t,"regularizer")}function Et(e){if(e==null)return null;if(typeof e=="string"){let n={className:e in bk?bk[e]:e,config:{}};return vk(n)}else return e instanceof xk?e:vk(e)}var fA=class extends tt{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Be(e);let n=$r(e);return this.maxValue!=null&&(n=ps(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};fA.className="ReLU";ue.registerClass(fA);var mA=class extends tt{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=Be(e);return pf(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};mA.className="LeakyReLU";ue.registerClass(mA);var gA=class extends tt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=Nt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=Et(e.alphaRegularizer),this.alphaConstraint=on(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 H(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=pt(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let s of this.sharedAxes)t[s-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let s=1;s<e.length;++s)n[s]=e[s];this.inputSpec=[new Zt({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Be(e),xf(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Mt(this.alphaInitializer),alphaRegularizer:At(this.alphaRegularizer),alphaConstraint:an(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};gA.className="PReLU";ue.registerClass(gA);var AA=class extends tt{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Le(`Non-default alpha value (${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=Be(e);return Ed(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};AA.className="ELU";ue.registerClass(AA);var yA=class extends tt{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=Be(e);return L(n,pe(hs(n,this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};yA.className="ThresholdedReLU";ue.registerClass(yA);var xA=class extends tt{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new dA().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Be(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}};xA.className="Softmax";ue.registerClass(xA);function Yu(e,t,n){if(typeof e=="number")return fl(e,t);if(e.length!==t)throw new H(`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(!RL(r))throw new H(`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 gr(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 Or(e,t,n,s){if(e==null)return null;if(s==="valid")e=e*t+Oo([n-t,0]);else if(s==="same")e=e*t;else throw new H(`Unsupport padding mode: ${s}.`);return e}function bA(e,t){return q(()=>(Ht(t),t==="channelsFirst"?Qe(e,[0,2,3,1]):e))}function wk(e,t){return q(()=>(Ht(t),t==="channelsFirst"?Qe(e,[0,2,3,4,1]):e))}function dW(e,t,n,s=1,r="valid",a,o=1){return q(()=>{if(a==null&&(a=cr()),Ht(a),e.shape.length!==3)throw new H(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new H(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new H(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(a==="channelsFirst"&&(e=Qe(e,[0,2,1])),r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=q2(e,t,s,r==="same"?"same":"valid","NWC",o);return n!=null&&(i=hr(i,n)),i})}function kk(e,t,n,s=[1,1],r="valid",a,o,i=null){return q(()=>{if(a==null&&(a=cr()),Ht(a),e.rank!==3&&e.rank!==4)throw new H(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new H(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let l=bA(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=_o.conv2d({x:l,filter:t,strides:s,pad:r==="same"?"same":"valid",dilations:o,dataFormat:"NHWC",bias:n,activation:i}),a==="channelsFirst"&&(l=Qe(l,[0,3,1,2])),l})}function pW(e,t,n,s=[1,1,1],r="valid",a,o){return q(()=>{if(a==null&&(a=cr()),Ht(a),e.rank!==4&&e.rank!==5)throw new H(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new H(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let i=wk(e,a);if(r==="causal")throw new Le("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Z2(i,t,s,r==="same"?"same":"valid","NDHWC",o),n!=null&&(i=hr(i,n)),a==="channelsFirst"&&(i=Qe(i,[0,4,1,2,3])),i})}var vA=class extends tt{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",vA.verifyArgs(t),this.rank=e,xn(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Le(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=Yu(t.kernelSize,e,"kernelSize"),this.strides=Yu(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,_s(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ht(this.dataFormat),this.activation=Lo(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=Nt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=on(t.biasConstraint),this.biasRegularizer=Et(t.biasRegularizer),this.activityRegularizer=Et(t.activityRegularizer),this.dilationRate=Yu(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new H(`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 H(`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 H(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(_r("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!R1(e.kernelSize,"number",1,3))throw new H(`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:zo(this.activation),useBias:this.useBias,biasInitializer:Mt(this.biasInitializer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Kd=class extends vA{constructor(e,t){super(e,t);this.kernel=null,Kd.verifyArgs(t),this.filters=t.filters,xn(this.filters,"filters"),this.kernelInitializer=Nt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=on(t.kernelConstraint),this.kernelRegularizer=Et(t.kernelRegularizer)}build(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H(`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 q(()=>{e=Be(e);let n,s=this.bias==null?null:this.bias.read(),r=pw(this.activation.getClassName());if(r!=null&&this.rank===2)n=kk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=dW(e,this.kernel.read(),s,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=kk(e,this.kernel.read(),s,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=pW(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=pt(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=gr(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:Mt(this.kernelInitializer),kernelRegularizer:At(this.kernelRegularizer),kernelConstraint:an(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 H(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},Sk=class extends Kd{constructor(e){super(2,e);Sk.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!R1(e.kernelSize,"number",1,2))throw new H(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}},om=Sk;om.className="Conv2D";ue.registerClass(om);var Ik=class extends Kd{constructor(e){super(3,e);Ik.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 H(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}},im=Ik;im.className="Conv3D";ue.registerClass(im);var wA=class extends om{constructor(e){super(e);if(this.inputSpec=[new Zt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=pt(e),e.length!==4)throw new H("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Be(e);if(n.shape.length!==4)throw new H(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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=Or(i,d,c,this.padding),f=Or(l,p,u,this.padding),m=[r,h,f,this.filters];this.dataFormat!=="channelsLast"&&(n=Qe(n,[0,2,3,1]));let g=K2(n,this.kernel.read(),m,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(g=Qe(g,[0,3,1,2])),this.bias!=null&&(g=hr(g,this.bias.read(),this.dataFormat)),this.activation!=null&&(g=this.activation.apply(g)),g})}computeOutputShape(e){e=pt(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]=Or(t[s],i,a,this.padding),t[r]=Or(t[r],l,o,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};wA.className="Conv2DTranspose";ue.registerClass(wA);var kA=class extends im{constructor(e){super(e);if(this.inputSpec=[new Zt({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new H(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=pt(e),e.length!==5)throw new H("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new H("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],s=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",s,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Zt({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{let n=Be(e);if(n.shape.length!==5)throw new H(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${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],A=Or(l,f,d,this.padding),x=Or(c,m,p,this.padding),y=Or(u,g,h,this.padding),b=[r,A,x,y,this.filters];this.dataFormat!=="channelsLast"&&(n=Qe(n,[0,2,3,4,1]));let w=sv(n,this.kernel.read(),b,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(w=Qe(w,[0,4,1,2,3])),this.bias!==null&&(w=hr(w,this.bias.read(),this.dataFormat)),this.activation!==null&&(w=this.activation.apply(w)),w})}computeOutputShape(e){e=pt(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]=Or(t[s],c,o,this.padding),t[r]=Or(t[r],u,i,this.padding),t[a]=Or(t[a],d,l,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};kA.className="Conv3DTranspose";ue.registerClass(kA);var Ck=class extends Kd{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 H("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new H("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 H(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=Nt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=Et(t.depthwiseRegularizer),this.depthwiseConstraint=on(t.depthwiseConstraint),this.pointwiseInitializer=Nt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=Et(t.pointwiseRegularizer),this.pointwiseConstraint=on(t.pointwiseConstraint)}build(e){if(e=pt(e),e.length<this.rank+2)throw new H(`Inputs to SeparableConv${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 H(`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 Zt({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return q(()=>{e=Be(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=Qe(e,[0,2,3,1])),n=Sv(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=hr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Qe(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=Mt(this.depthwiseInitializer),e.pointwiseInitializer=Mt(this.pointwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.pointwiseRegularizer=At(this.pointwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseConstraint),e.pointwiseConstraint=an(this.pointwiseConstraint),e}};Ck.className="SeparableConv";var SA=class extends Ck{constructor(e){super(2,e)}};SA.className="SeparableConv2D";ue.registerClass(SA);var Tk=class extends Kd{constructor(e){super(1,e);Tk.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"&&!R1(e.kernelSize,"number",1,1))throw new H(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}},IA=Tk;IA.className="Conv1D";ue.registerClass(IA);var CA=class extends tt{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 q(()=>{if(e=Be(e),this.dataFormat==="channelsLast"){let n=Mf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return Mf(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=Mf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return Mf(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}};CA.className="Cropping2D";ue.registerClass(CA);var TA=class extends tt{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,TL(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 q(()=>{let n=Be(e),s=n.shape;if(this.dataFormat==="channelsFirst"){n=Qe(n,[0,2,3,1]);let r=this.size[0]*s[2],a=this.size[1]*s[3],o=this.interpolation==="nearest"?$e.resizeNearestNeighbor(n,[r,a]):$e.resizeBilinear(n,[r,a]);return Qe(o,[0,3,1,2])}else{let r=this.size[0]*s[1],a=this.size[1]*s[2];return this.interpolation==="nearest"?$e.resizeNearestNeighbor(n,[r,a]):$e.resizeBilinear(n,[r,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};TA.className="UpSampling2D";ue.registerClass(TA);function hW(e,t,n=[1,1],s="valid",r,a){return q(()=>{r==null&&(r=cr()),Ht(r);let o=bA(e,r);if(e.rank!==4)throw new H(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new H(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return o=Nd(o,t,n,s==="same"?"same":"valid","NHWC",a),r==="channelsFirst"&&(o=Qe(o,[0,3,1,2])),o})}var NA=class extends vA{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=Nt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=on(e.depthwiseConstraint),this.depthwiseRegularizer=Et(e.depthwiseRegularizer)}build(e){if(e=pt(e),e.length<4)throw new H(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new H(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${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 q(()=>{e=Be(e);let n=hW(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=hr(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=pt(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=gr(t,this.kernelSize[0],this.padding,this.strides[0]),a=gr(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=Mt(this.depthwiseInitializer),e.depthwiseRegularizer=At(this.depthwiseRegularizer),e.depthwiseConstraint=an(this.depthwiseRegularizer),e}};NA.className="DepthwiseConv2D";ue.registerClass(NA);function Nk(e,t,n,s){if(Array.isArray(e)){if(t!=null||n!=null)throw new H("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 Ek(e,t,n,s=!1,r,a,o=!1,i=!1){return q(()=>{let l=t.shape.length;if(l<3)throw new H(`Input should be at least 3D, but is ${l}D.`);let c=[1,0].concat(pr(2,l));if(t=Qe(t,c),a!=null)throw new Le("The rnn() functoin of the deeplearn.js backend does not support constants yet.");o&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=pe(pe(r,"bool"),"float32"),r.rank===l-1&&(r=Xt(r,-1)),r=Qe(r,c)),s&&(t=$s(t,0),r!=null&&(r=$s(r,0)));let u=[],d,p=n,h=t.shape[0],f=rs(t),m;r!=null&&(m=rs(r));for(let A=0;A<h;++A){let x=f[A],y=q(()=>e(x,p));if(r==null)d=y[0],p=y[1];else{let b=q(()=>{let w=m[A],k=me(Rs(w),w),C=le(L(y[0],w),L(p[0],k)),N=p.map((R,F)=>le(L(y[1][F],w),L(R,k)));return{output:C,newStates:N}});d=b.output,p=b.newStates}i&&u.push(d)}let g;return i&&(g=yn(u,1)),[d,g,p]})}var Rk=class extends tt{constructor(e){super(e);let t;if(e.cell==null)throw new H("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new cm({cells:e.cell}):t=e.cell,t.stateSize==null)throw new H("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 Zt({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 pr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){q1(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 q(()=>{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.");q1(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,s=e.slice(2);this.inputSpec[0]=new Zt({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 H(`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 Zt({shape:[null,o]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){q(()=>{if(!this.stateful)throw new Qr("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new H("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Gt([n,s])):this.states_=[Gt([n,this.cell.stateSize])];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(s=>Gt([n,s])):this.states_[0]=Gt([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):ne(this.states_);for(let s=0;s<this.states_.length;++s){let r=e[s],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[s]:this.cell.stateSize,o=[n,a];if(!v.arraysEqual(r.shape,o))throw new H(`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=>gn(s.clone()))})}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=Nk(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 Zt({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 fr){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 q(()=>{let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;e=Be(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 H(`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=Ek((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 q(()=>{let t=Gt(e.shape);return t=Se(t,[1,2]),t=Wd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?z1(t,[1,n]):t):this.cell.stateSize>1?[z1(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()===Rk.className&&(t.cell={className:this.cell.getClassName(),config:n}),{...n,...e,...t}}static fromConfig(e,t,n={}){let s=t.cell,r=mr(s,n);return new e(Object.assign(t,{cell:r}))}},na=Rk;na.className="RNN";ue.registerClass(na);var Zd=class extends tt{},lm=class extends Zd{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=Lo(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Nt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Nt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Nt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Et(e.kernelRegularizer),this.recurrentRegularizer=Et(e.recurrentRegularizer),this.biasRegularizer=Et(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=qu([1,Oo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=qu([1,Oo([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.dropoutFunc=e.dropoutFunc,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=pt(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 q(()=>{if(e=e,e.length!==2)throw new H(`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=Bo({ones:()=>Rs(e),rate:this.dropout,training:s,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Bo({ones:()=>Rs(n),rate:this.recurrentDropout,training:s,dropoutFunc:this.dropoutFunc}));let r,a=this.dropoutMask,o=this.recurrentDropoutMask;a!=null?r=Dr(L(e,a),this.kernel.read()):r=Dr(e,this.kernel.read()),this.bias!=null&&(r=hr(r,this.bias.read())),o!=null&&(n=L(n,o));let i=le(r,Dr(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:zo(this.activation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),recurrentInitializer:Mt(this.recurrentInitializer),biasInitializer:Mt(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return{...e,...t}}};lm.className="SimpleRNNCell";ue.registerClass(lm);var EA=class extends na{constructor(e){e.cell=new lm(e);super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return new e(t)}};EA.className="SimpleRNN";ue.registerClass(EA);var um=class extends Zd{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 H("GRUCell does not support reset_after parameter set to true.");this.units=e.units,xn(this.units,"units"),this.activation=Lo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Lo(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Nt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Nt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Nt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=Et(e.kernelRegularizer),this.recurrentRegularizer=Et(e.recurrentRegularizer),this.biasRegularizer=Et(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=qu([1,Oo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=qu([1,Oo([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=pt(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 q(()=>{if(e=e,e.length!==2)throw new H(`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=Bo({ones:()=>Rs(e),rate:this.dropout,training:n,count:3,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Bo({ones:()=>Rs(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=Dr(e,this.kernel.read());this.useBias&&(c=hr(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,a[0]));let u=this.recurrentKernel.read(),[d,p]=sn(u,[2*this.units,this.units],u.rank-1),h=Dr(s,d),[f,m,g]=sn(c,3,c.rank-1),[A,x]=sn(h,2,h.rank-1);o=this.recurrentActivation.apply(le(f,A)),i=this.recurrentActivation.apply(le(m,x));let y=Dr(L(i,s),p);l=this.activation.apply(le(g,y));let b=le(L(o,s),L(le(1,Ot(o)),l));return[b,b]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:zo(this.activation),recurrentActivation:zo(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),recurrentInitializer:Mt(this.recurrentInitializer),biasInitializer:Mt(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return{...e,...t}}};um.className="GRUCell";ue.registerClass(um);var RA=class extends na{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 um(e);super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};RA.className="GRU";ue.registerClass(RA);var Yd=class extends Zd{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=Lo(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Lo(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=Nt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=Nt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=Nt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=Et(e.kernelRegularizer),this.recurrentRegularizer=Et(e.recurrentRegularizer),this.biasRegularizer=Et(e.biasRegularizer),this.kernelConstraint=on(e.kernelConstraint),this.recurrentConstraint=on(e.recurrentConstraint),this.biasConstraint=on(e.biasConstraint),this.dropout=qu([1,Oo([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=qu([1,Oo([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=pt(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 Ys{apply(o,i){let l=r.apply([a]),c=new Lf().apply([a]),u=r.apply([a*2]);return vw(vw(l,c),u)}},t.className="CustomInit",t)}else s=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,s,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return q(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new H(`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=Bo({ones:()=>Rs(e),rate:this.dropout,training:n,count:4,dropoutFunc:this.dropoutFunc})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Bo({ones:()=>Rs(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=Dr(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(s=L(s,o[0])),d=le(d,Dr(s,this.recurrentKernel.read())),this.useBias&&(d=hr(d,this.bias.read()));let[p,h,f,m]=sn(d,4,d.rank-1);i=this.recurrentActivation.apply(p),l=this.recurrentActivation.apply(h),c=le(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:zo(this.activation),recurrentActivation:zo(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),recurrentInitializer:Mt(this.recurrentInitializer),biasInitializer:Mt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:At(this.kernelRegularizer),recurrentRegularizer:At(this.recurrentRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),recurrentConstraint:an(this.recurrentConstraint),biasConstraint:an(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return{...e,...t}}};Yd.className="LSTMCell";ue.registerClass(Yd);var $A=class extends na{constructor(e){e.implementation===0&&console.warn("`implementation=0` has been deprecated, and now defaults to `implementation=1`. Please update your layer call."),e.cell=new Yd(e);super(e)}call(e,t){return q(()=>{this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,s=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:s,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};$A.className="LSTM";ue.registerClass($A);var cm=class extends Zd{constructor(e){super(e);this.cells=e.cells}get stateSize(){let e=[];for(let t of this.cells.slice().reverse())Array.isArray(t.stateSize)?e.push(...t.stateSize):e.push(t.stateSize);return e}call(e,t){return q(()=>{e=e;let n=e.slice(1),s=[];for(let o of this.cells.slice().reverse())Array.isArray(o.stateSize)?s.push(n.splice(0,o.stateSize.length)):s.push(n.splice(0,1));s.reverse();let r=[],a;for(let o=0;o<this.cells.length;++o){let i=this.cells[o];n=s[o],o===0?a=[e[0]].concat(n):a=[a[0]].concat(n),a=i.call(a,t),r.push(a.slice(1))}n=[];for(let o of r.slice().reverse())n.push(...o);return[a[0]].concat(n)})}build(e){q1(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,s)=>{Al(`RNNCell_${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(mr(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 X1(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]])}K1(t)}};cm.className="StackedRNNCells";ue.registerClass(cm);function Bo(e){let{ones:t,rate:n,training:s=!1,count:r=1,dropoutFunc:a}=e,o=()=>a!=null?a(t(),n):kw(t(),n),i=()=>Ud(o,t,s);return!r||r<=1?gn(i().clone()):Array(r).fill(void 0).map(i).map(c=>gn(c.clone()))}var $k=class extends na{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 Zt({ndim:5})]}call(e,t){return q(()=>{if(this.cell.dropoutMask!=null&&(ne(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(ne(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new H("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 q(()=>{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){q(()=>{if(!this.stateful)throw new Qr("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 H("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.getStates()==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Gt(r)):this.states_=[Gt(r)];else if(e==null)ne(this.states_),this.keptStates!=null&&(ne(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>Gt(r)):this.states_[0]=Gt(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new H(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):ne(this.states_);for(let o=0;o<this.states_.length;++o){let i=e[o],l=r;if(!v.arraysEqual(i.shape,l))throw new H(`State ${o} is incompatible with layer ${this.name}: expected shape=${l}, received shape=${i.shape}`);this.states_[o]=i}}this.states_=this.states_.map(o=>gn(o.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:s,padding:r,strides:a,dilationRate:o}=this.cell,i=t==="channelsFirst",l=e[i?3:2],c=e[i?4:3],u=gr(l,s[0],r,a[0],o[0]),d=gr(c,s[1],r,a[1],o[1]);return[...e.slice(0,2),...i?[n,u,d]:[u,d,n]]}};$k.className="ConvRNN2D";var dm=class extends Yd{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=Yu(n,2,"kernelSize"),this.kernelSize.forEach(i=>xn(i,"kernelSize")),this.strides=Yu(s||1,2,"strides"),this.strides.forEach(i=>xn(i,"strides")),this.padding=r||"valid",_s(this.padding),this.dataFormat=a||"channelsLast",Ht(this.dataFormat),this.dilationRate=Yu(o||1,2,"dilationRate"),this.dilationRate.forEach(i=>xn(i,"dilationRate"))}build(e){var t;e=pt(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new H(`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 Ys{apply(u,d){let p=l.apply([c]),h=fs([c]),f=l.apply([c*2]);return M1([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 q(()=>{if(e.length!==3)throw new H(`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=Bo({ones:()=>Rs(s),rate:this.dropout,training:n,count:o,dropoutFunc:this.dropoutFunc}));let i=this.dropoutMask,l=(Z,J,ee)=>!J||!J[ee]?Z:L(J[ee],Z),c=l(s,i,0),u=l(s,i,1),d=l(s,i,2),p=l(s,i,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Bo({ones:()=>Rs(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),A=l(r,h,3),x=3,[y,b,w,k]=sn(this.kernel.read(),o,x),[C,N,R,F]=this.useBias?sn(this.bias.read(),o):[null,null,null,null];c=this.inputConv(c,y,C,this.padding),u=this.inputConv(u,b,N,this.padding),d=this.inputConv(d,w,R,this.padding),p=this.inputConv(p,k,F,this.padding);let[_,P,T,M]=sn(this.recurrentKernel.read(),o,x);f=this.recurrentConv(f,_),m=this.recurrentConv(m,P),g=this.recurrentConv(g,T),A=this.recurrentConv(A,M);let U=this.recurrentActivation.apply(le(c,f)),j=this.recurrentActivation.apply(le(u,m)),z=le(L(j,a),L(U,this.activation.apply(le(d,g)))),X=L(this.recurrentActivation.apply(le(p,A)),this.activation.apply(z));return[X,X,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=Eo(e,t,this.strides,s||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?hr(r,n,this.dataFormat):r}recurrentConv(e,t){return Eo(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};dm.className="ConvLSTM2DCell";ue.registerClass(dm);var _A=class extends $k{constructor(e){let t=new dm(e);super({...e,cell:t})}static fromConfig(e,t){return new e(t)}};_A.className="ConvLSTM2D";ue.registerClass(_A);var pm=class extends tt{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 q(()=>{this.invokeCallHook(e,t);let n=Be(e);if(0<this.rate&&this.rate<1){let s=t.training==null?!1:t.training,r=this.getNoiseShape(n);return Ud(()=>kw(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()}};pm.className="Dropout";ue.registerClass(pm);var DA=class extends pm{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};DA.className="SpatialDropout1D";ue.registerClass(DA);var PA=class extends tt{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=Lo(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=Nt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=Nt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=on(e.kernelConstraint),this.biasConstraint=on(e.biasConstraint),this.kernelRegularizer=Et(e.kernelRegularizer),this.biasRegularizer=Et(e.biasRegularizer),this.activityRegularizer=Et(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=pt(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=pt(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Be(e),s=pw(this.activation.getClassName()),r;return s!=null?r=Dr(n,this.kernel.read(),s,this.bias?this.bias.read():null):(r=Dr(n,this.kernel.read()),this.bias!=null&&(r=hr(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:zo(this.activation),useBias:this.useBias,kernelInitializer:Mt(this.kernelInitializer),biasInitializer:Mt(this.biasInitializer),kernelRegularizer:At(this.kernelRegularizer),biasRegularizer:At(this.biasRegularizer),activityRegularizer:At(this.activityRegularizer),kernelConstraint:an(this.kernelConstraint),biasConstraint:an(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};PA.className="Dense";ue.registerClass(PA);var FA=class extends tt{constructor(e){e=e||{};super(e);this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=pt(e);for(let t of e.slice(1))if(t==null)throw new H(`The shape of the input to "Flatten" is not fully defined (got ${e.slice(1)}). Make sure to pass a complete "input_shape" or "batch_input_shape" argument to the first layer in your model.`);return[e[0],Fo(e,1)]}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Be(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let s=[0];for(let r=2;r<n.rank;++r)s.push(r);s.push(1),n=Qe(n,s)}return DL(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};FA.className="Flatten";ue.registerClass(FA);var OA=class extends tt{constructor(e){super(e);this.supportsMasking=!0,this.activation=Lo(e.activation)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Be(e);return this.activation.apply(n)})}getConfig(){let e={activation:zo(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};OA.className="Activation";ue.registerClass(OA);var MA=class extends tt{constructor(e){super(e);this.n=e.n,this.inputSpec=[{ndim:2}]}computeOutputShape(e){return[e[0],this.n,e[1]]}call(e,t){return q(()=>(e=Be(e),$L(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};MA.className="RepeatVector";ue.registerClass(MA);var zA=class extends tt{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 H("Can only specifiy one unknown dimension.");else r*=l}let o=Fo(e);if(a!==null){if(r===0||o%r!=0)throw new H(n);s[a]=o/r}else if(o!==r)throw new H(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 q(()=>{this.invokeCallHook(e,t);let n=Be(e),s=n.shape,r=s.slice(0,1).concat(this.fixUnknownDimension(s.slice(1),this.targetShape));return G(n,r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};zA.className="Reshape";ue.registerClass(zA);var LA=class extends tt{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=pr(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 Zt({ndim:this.dims.length+1})]}computeOutputShape(e){e=pt(e);let t=e.slice();return this.dims.forEach((n,s)=>{t[s+1]=e[n]}),t}call(e,t){return Qe(Be(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};LA.className="Permute";ue.registerClass(LA);var BA=class extends tt{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=Be(e),s=-1;return af(Wu(n,this.maskValue),s)}call(e,t){return q(()=>{this.invokeCallHook(e,t);let n=Be(e),s=-1,r=!0,a=af(Wu(n,this.maskValue),s,r);return L(n,pe(a,n.dtype))})}};BA.className="Masking";ue.registerClass(BA);var WA=class extends tt{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(kt(e.inputLength))}this.inputDim=e.inputDim,xn(this.inputDim,"inputDim"),this.outputDim=e.outputDim,xn(this.outputDim,"outputDim"),this.embeddingsInitializer=Nt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=Et(e.embeddingsRegularizer),this.activityRegularizer=Et(e.activityRegularizer),this.embeddingsConstraint=on(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 q(()=>this.maskZero?(e=Be(e),Wu(e,et(e))):null)}computeOutputShape(e){if(e=pt(e),this.inputLength==null)return[...e,this.outputDim];let t=kt(this.inputLength);if(t.length!==e.length-1)throw new H(`"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 H(`"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 q(()=>{this.invokeCallHook(e,t);let n=Be(e);n.dtype!=="int32"&&(n=Of(n,"int32"));let s=ww(this.embeddings.read(),G(n,[n.size]));return G(s,pt(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Mt(this.embeddingsInitializer),embeddingsRegularizer:At(this.embeddingsRegularizer),activityRegularizer:At(this.activityRegularizer),embeddingsConstraint:an(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};WA.className="Embedding";ue.registerClass(WA);var wl=class extends tt{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 H("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=[pt(e)]),e=e,e.length<2)throw new H(`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=Po(t),t.length>1)throw new H(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let a=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,a)}let s=e.map(r=>r.length);e.indexOf(null)===-1&&Po(s).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return q(()=>{if(e=e,this.reshapeRequired){let n=[],s=e.map(r=>r.rank);if(s.indexOf(null)===-1){let r=Oo(s);for(let a of e){let o=a.rank;for(let i=0;i<r-o;++i)a=Wd(a,1);n.push(a)}return this.mergeFunction(n)}else{let r=!1;for(let i of e){let l=i.rank;if(l==null){let c=i.shape,u=c[0],d=c.slice(1).concat([u]),p=G(i,[u].concat(Fo(c.slice(1))));p=Qe(p,[1,0]),p=G(p,d),n.push(p),r=!0}else if(l>1){let c=pr(1,l).concat([0]);n.push(Qe(i,c)),r=!0}else n.push(i)}let a=this.mergeFunction(n),o=a.rank;if(r){if(o==null){let i=a.shape,l=i.length,c=i[l-1],u=[c].concat(i.slice(0,i.length-1));a=G(Qe(G(a,[-1,c]),[1,0]),u)}else if(o>1){let i=[o-1].concat(pr(0,o-1));a=Qe(a,i)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let s=1;s<e.length;++s){let r=e[s]==null?null:e[s].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let s of e)s!=null&&s[0]!==null&&n.push(s[0]);return n=Po(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return q(()=>{if(t==null)return null;if(!Array.isArray(t))throw new H("`mask` should be an Array");if(!Array.isArray(e))throw new H("`inputs` should be an Array");if(t.length!==e.length)throw new H(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(s=>s==null))return null;t=t.map(s=>s==null?s:Xt(s,0));let n=t[0];for(let s=1;s<t.length-1;++s)n=lr(n,t[s]);return n})}},VA=class extends wl{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=le(t,e[n]);return t})}};VA.className="Add";ue.registerClass(VA);var UA=class extends wl{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=L(t,e[n]);return t})}};UA.className="Multiply";ue.registerClass(UA);var GA=class extends wl{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=le(t,e[n]);return L(1/e.length,t)})}};GA.className="Average";ue.registerClass(GA);var HA=class extends wl{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Zr(t,e[n]);return t})}};HA.className="Maximum";ue.registerClass(HA);var jA=class extends wl{constructor(e){super(e)}mergeFunction(e){return q(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=$d(t,e[n]);return t})}};jA.className="Minimum";ue.registerClass(jA);var qA=class extends wl{constructor(e){super(e);this.DEFAULT_AXIS=-1,e==null&&(e={}),this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){if(!(Array.isArray(e)&&Array.isArray(e[0]))||e.length===1)throw new H("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 H("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return q(()=>M1(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new H("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 H("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new H("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new H(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return q(()=>{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(pe(Rs(e[a]),"bool")):t[a].rank<e[a].rank?s.push(Xt(t[a],-1)):s.push(t[a]);let r=vt(s,this.axis);return V2(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};qA.className="Concatenate";ue.registerClass(qA);function Jd(e,t){for(;e<0;)e+=t;return e}function fW(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 q(()=>{let o;if(s>r){o=s-r;let l=[];for(let c=0;c<o;++c)l.push(1);t=G(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=G(e,e.shape.concat(l))}else o=0;let i;if(e.shape.length===2&&t.shape.length===2)a[0]===a[1]?i=Se(L(e,t),a[0]):i=Se(L(Qe(e,[1,0]),t),a[1]);else{let l=a[0]!==e.shape.length-1,c=a[1]===t.shape.length-1;i=Ue(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=ot(i,c)}return i.shape.length===1&&(i=Xt(i,1)),i})}var XA=class extends wl{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){v.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Le("Dot layer does not support tensors of 4D or higher rank yet.");let s=this.interpretAxes(t,n);if(t[s[0]]!==n[s[1]])throw new H(`Dimension incompatibility: ${t[s[0]]} !== ${n[s[1]]}`)}mergeFunction(e){if(e.length!==2)throw new H(`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)=>Jd(r,e[a].shape.length)):s=[Jd(this.axes,t.shape.length),Jd(this.axes,n.shape.length)],this.normalize&&(t=Yf(t,s[0]),n=Yf(n,s[1])),fW(t,n,s)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[Jd(this.axes,e.length),Jd(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}};XA.className="Dot";ue.registerClass(XA);var KA=class extends tt{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 q(()=>{this.invokeCallHook(e,t);let n=Be(e);return Ud(()=>le(zf(n.shape,0,this.stddev),n),()=>n,t.training||!1)})}};KA.className="GaussianNoise";ue.registerClass(KA);var ZA=class extends tt{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 q(()=>{this.invokeCallHook(e,t);let n=Be(e);return this.rate>0&&this.rate<1?Ud(()=>{let r=Math.sqrt(this.rate/(1-this.rate));return L(n,zf(n.shape,1,r))},()=>n,t.training||!1):n})}};ZA.className="GaussianDropout";ue.registerClass(ZA);var YA=class extends tt{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Be(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 q(()=>{if(this.rate<1&&this.rate>0){let n=this._getNoiseShape(e);return Ud(()=>{let r=Be(e),a=1.6732632423543772,o=1.0507009873554805,i=-a*o,l=ll(Vu(n),this.rate);l=Of(l,"float32");let c=((1-this.rate)*(1+this.rate*i**2))**-.5,u=-c*i*this.rate,d=le(L(r,l),L(le(l,-1),i));return le(L(d,c),u)},()=>Be(e),t.training||!1)}return e})}};YA.className="AlphaDropout";ue.registerClass(YA);function Qd(e,t,n,s,r,a=.001){let o;if(e.rank===2)o=X3(e,t,n,s,r,a);else if(e.rank===3)o=K3(e,t,n,s,r,a);else if(e.rank===4)o=Z3(e,t,n,s,r,a);else throw new Le(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return o}function mW(e,t,n,s,r=.001){return q(()=>{let a=Af(e,s),o=a.mean,i=a.variance;return[Qd(e,o,i,n,t,r),o,i]})}function gW(e,t,n,s,r=.001){return q(()=>{let a=Af(e,s),o=a.mean,i=a.variance,l=[];for(let f of pr(0,e.rank))s.indexOf(f)!==-1?l.push(1):l.push(e.shape[f]);let c=G(o,l),u=G(i,l),d=t==null?null:G(t,l),p=n==null?null:G(n,l);return[Qd(e,c,u,p,d,r),o,i]})}function AW(e,t,n,s,r=.001){return v.arraysEqual(s.slice().sort(),pr(0,e.rank-1))?mW(e,t,n,s,r):gW(e,t,n,s,r)}var JA=class extends tt{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=Nt(e.betaInitializer||"zeros"),this.gammaInitializer=Nt(e.gammaInitializer||"ones"),this.movingMeanInitializer=Nt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=Nt(e.movingVarianceInitializer||"ones"),this.betaConstraint=on(e.betaConstraint),this.gammaConstraint=on(e.gammaConstraint),this.betaRegularizer=Et(e.betaRegularizer),this.gammaRegularizer=Et(e.gammaRegularizer)}build(e){e=pt(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new H(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Zt({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 q(()=>{let n=t.training==null?!1:t.training,s=Be(e),r=s.shape,a=r.length,o=pr(0,a),i=this.axis>=0?this.axis:this.axis+a;o.splice(i,1);let l=fl(1,a);l[i]=r[i];let c=o.slice();c.sort();let u=!v.arraysEqual(c,pr(0,a).slice(0,a-1)),d=()=>{if(u){let A=G(this.movingMean.read(),l),x=G(this.movingVariance.read(),l),y=this.center?G(this.beta.read(),l):null,b=this.scale?G(this.gamma.read(),l):null;return Qd(s,A,x,y,b,this.epsilon)}else return Qd(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]=AW(s,this.gamma.read(),this.beta.read(),o,this.epsilon),m=(A,x,y)=>{q(()=>{let b=1-y,w=A.read(),k=L(me(w,x),b);A.write(me(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:Mt(this.betaInitializer),gammaInitializer:Mt(this.gammaInitializer),movingMeanInitializer:Mt(this.movingMeanInitializer),movingVarianceInitializer:Mt(this.movingVarianceInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer),betaConstraint:an(this.betaConstraint),gammaConstraint:an(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};JA.className="BatchNormalization";ue.registerClass(JA);var QA=class extends tt{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=Nt(e.betaInitializer||"zeros"),this.gammaInitializer=Nt(e.gammaInitializer||"ones"),this.betaRegularizer=Et(e.betaRegularizer),this.gammaRegularizer=Et(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=pt(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!==Po(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=Be(e),s=n.shape,r=s.length;return q(()=>{let a=!0,{mean:o,variance:i}=Af(n,this.axis,a),l=fl(1,r);for(let f of this.axis)l[f]=s[f];let c=f=>f!=null&&f.shape.length!==r?G(f,l):f,u=c(this.gamma.read()),d=c(this.beta.read()),p=[],h=[];for(let f=0;f<r;++f)this.axis.indexOf(f)!==-1?(p.push(s[f]),h.push(1)):(p.push(1),h.push(s[f]));return o=qs(o,p),i=qs(i,p),u=qs(u,h),d=qs(d,h),Qd(n,o,i,d,u,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Mt(this.betaInitializer),gammaInitializer:Mt(this.gammaInitializer),betaRegularizer:At(this.betaRegularizer),gammaRegularizer:At(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};QA.className="LayerNormalization";ue.registerClass(QA);function yW(e,t,n){return q(()=>{if(e.rank!==4)throw new H(`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 H("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=cr()),n!=="channelsLast"&&n!=="channelsFirst")throw new H(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let s;return n==="channelsFirst"?s=[[0,0],[0,0],t[0],t[1]]:s=[[0,0],t[0],t[1],[0,0]],Xs(e,s)})}var ey=class extends tt{constructor(e){e==null&&(e={});super(e);if(this.dataFormat=e.dataFormat==null?cr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new H(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${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 H(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new H(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){e=pt(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 q(()=>yW(Be(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};ey.className="ZeroPadding2D";ue.registerClass(ey);function hm(e,t,n,s,r,a){return q(()=>{Ht(r),gw(a),_s(s),n==null&&(n=[1,1]),s==null&&(s="valid"),r==null&&(r=cr()),a==null&&(a="max"),e=bA(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=gf(e,t,n,i):o=lf(e,t,n,i),r==="channelsFirst"&&(o=Qe(o,[0,3,1,2])),o})}function _k(e,t,n,s,r,a){return q(()=>{Ht(r),gw(a),_s(s),n==null&&(n=[1,1,1]),s==null&&(s="valid"),r==null&&(r=cr()),a==null&&(a="max"),e=wk(e,r);let o,i=s==="same"?"same":"valid";return a==="max"?o=a1(e,t,n,i):o=H2(e,t,n,i),r==="channelsFirst"&&(o=Qe(o,[0,4,1,2,3])),o})}var Dk=class extends tt{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 H(`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 H(`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,_s(this.padding),this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){e=pt(e);let t=gr(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return q(()=>{this.invokeCallHook(e,t),e=Wd(Be(e),2);let n=this.poolingFunction(Be(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return ot(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},ty=class extends Dk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),_s(s),hm(e,t,n,s,r,"max")}};ty.className="MaxPooling1D";ue.registerClass(ty);var ny=class extends Dk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),_s(s),hm(e,t,n,s,r,"avg")}};ny.className="AveragePooling1D";ue.registerClass(ny);var Pk=class extends tt{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 H(`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),_s(this.padding),this.inputSpec=[new Zt({ndim:4})]}computeOutputShape(e){e=pt(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=gr(t,this.poolSize[0],this.padding,this.strides[0]),n=gr(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 q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Be(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}},sy=class extends Pk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),_s(s),hm(e,t,n,s,r,"max")}};sy.className="MaxPooling2D";ue.registerClass(sy);var ry=class extends Pk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),_s(s),hm(e,t,n,s,r,"avg")}};ry.className="AveragePooling2D";ue.registerClass(ry);var Fk=class extends tt{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 H(`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),_s(this.padding),this.inputSpec=[new Zt({ndim:5})]}computeOutputShape(e){e=pt(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=gr(t,this.poolSize[0],this.padding,this.strides[0]),n=gr(n,this.poolSize[1],this.padding,this.strides[1]),s=gr(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 q(()=>(this.invokeCallHook(e,t),this.poolingFunction(Be(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}},ay=class extends Fk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),_s(s),_k(e,t,n,s,r,"max")}};ay.className="MaxPooling3D";ue.registerClass(ay);var oy=class extends Fk{constructor(e){super(e)}poolingFunction(e,t,n,s,r){return Ht(r),_s(s),_k(e,t,n,s,r,"avg")}};oy.className="AveragePooling3D";ue.registerClass(oy);var Ok=class extends tt{constructor(e){super(e);this.inputSpec=[new Zt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Le}},iy=class extends Ok{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Be(e);return Wt(n,1)})}};iy.className="GlobalAveragePooling1D";ue.registerClass(iy);var ly=class extends Ok{constructor(e){super(e||{})}call(e,t){return q(()=>{let n=Be(e);return An(n,1)})}};ly.className="GlobalMaxPooling1D";ue.registerClass(ly);var Mk=class extends tt{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ht(this.dataFormat),this.inputSpec=[new Zt({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}},uy=class extends Mk{call(e,t){return q(()=>{let n=Be(e);return this.dataFormat==="channelsLast"?Wt(n,[1,2]):Wt(n,[2,3])})}};uy.className="GlobalAveragePooling2D";ue.registerClass(uy);var cy=class extends Mk{call(e,t){return q(()=>{let n=Be(e);return this.dataFormat==="channelsLast"?An(n,[1,2]):An(n,[2,3])})}};cy.className="GlobalMaxPooling2D";ue.registerClass(cy);var zk=class extends tt{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=mr(s,n);delete t.layer;let a={layer:r};return Object.assign(a,t),new e(a)}},dy=class extends zk{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=pt(e),e.length<3)throw new H(`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=pt(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 q(()=>(e=Be(e),Ek((a,o)=>[Be(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};dy.className="TimeDistributed";ue.registerClass(dy);function xW(e){gl(CL,"BidirectionalMergeMode",e)}var bW="concat",py=class extends zk{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=mr(n),t.goBackwards=t.goBackwards!==!0;let s={};if(s.className=e.layer.getClassName(),s.config=t,this.backwardLayer=mr(s),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?bW:e.mergeMode,xW(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()):as(s)}apply(e,t){let n=t==null?null:t.initialState,s=t==null?null:t.constants;t==null&&(t={});let r=Nk(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 H("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 Zt({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 fr;for(let l of a)if(l instanceof fr!==i)throw new H("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 q(()=>{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=$s(r,1));let o;return this.mergeMode==="concat"?o=M1([s,r]):this.mergeMode==="sum"?o=le(s,r):this.mergeMode==="ave"?o=L(.5,le(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 this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=mr(t.layer);if(delete t.layer,t.numConstants!=null)throw new Le("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let s=t;return s.layer=n,new e(s)}};py.className="Bidirectional";ue.registerClass(py);function vW(e){return new Xu(e)}function wW(e){return new AA(e)}function kW(e){return new fA(e)}function SW(e){return new mA(e)}function IW(e){return new gA(e)}function CW(e){return new xA(e)}function TW(e){return new yA(e)}function NW(e){return new IA(e)}function EW(e){return new om(e)}function RW(e){return new wA(e)}function 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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),Js(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,gn(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 Js(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),yn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return Pt([],[0].concat(this.elementShape));let t=[];for(let s=0;s<this.size();s++)t.push(s);let n=this.readMany(t);return Js(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),vt(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,rs(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=[];q(()=>{t=G(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]=G(De(t,c,u),this.elementShape)}return a});let o=[];for(let i=0;i<e.length;i++)o[i]=i;this.writeMany(o,a)}},tp=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}`);Js(t,r.shape,"TensorList shape mismatch: "),gn(r)}),this.idTensor=Re(0),this.maxNumElements=s,gn(this.idTensor)}get id(){return this.idTensor.id}copy(){return new tp([...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.`);Js(e,this.elementShape,"TensorList shape mismatch: ");let s=ep(this.elementShape,this.tensors,e);return q(()=>{let r=this.tensors.map(a=>G(a,s));return yn(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=ep(this.elementShape,this.tensors,e),s=this.tensors.pop();return Js(s.shape,e,"TensorList shape mismatch: "),G(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(Js(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");gn(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.`);Js(this.tensors[e].shape,t,"TensorList shape mismatch: ");let s=ep(this.elementShape,this.tensors,t);return G(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.`);Js(this.elementShape,t.shape,"TensorList shape mismatch: "),gn(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}`);Js(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let s=ep(this.elementShape,this.tensors,n);return e.length===0?Pt([],[0].concat(s)):q(()=>{let r=e.map(a=>G(this.tensors[a],s));return yn(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);Js(this.elementShape,t,"TensorList shape mismatch: ");let n=ep(this.elementShape,this.tensors,t);return this.size()===0?Pt([],[0].concat(n)):q(()=>{let s=this.tensors.map(r=>G(r,n));return vt(s,0)})}};function IU(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);Js(r,t,"TensorList shape mismatch: ");let a=rs(e);return new tp(a,t,s)}function CU(e,t,n){return new tp([],e,t,n)}function TU(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. ${s})`);let a=new tp([],n,e.dtype,s),o=rs(e,0);return t.forEach((i,l)=>{a.setItem(i,o[l])}),a}function NU(e,t,n){let s=0,r=t.map(u=>(s+=u,s));if(s!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
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tensor.shape[0], but sum of lengths is
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${s}, and tensor's shape is: ${e.shape}`);let a=e.shape.slice(1),o=Ny(a,n),i=s===0?0:e.size/s,l=q(()=>{let u=[];e=G(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]=G(De(e,h,f),o)}return e.dispose(),u}),c=new tp([],n,e.dtype,t.length);for(let u=0;u<l.length;u++)c.setItem(u,l[u]);return c}var EU=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`)}},HU=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:s,nGramsSplits:r}=Nf.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}=Nf.stringSplit(S("input",e,t,n),S("delimiter",e,t,n),S("skipEmpty",e,t,n));return[s,r,a]}case"StringToHashBucketFast":return[Nf.stringToHashBucketFast(S("input",e,t,n),S("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},jU=(e,t,n)=>{switch(e.op){case"Cast":return[pe(S("x",e,t,n),S("dtype",e,t,n))];case"ExpandDims":{let s=S("axis",e,t,n);return[Xt(S("x",e,t,n),s)]}case"Squeeze":{let s=S("axis",e,t,n);return[ot(S("x",e,t,n),s)]}case"Reshape":return[G(S("x",e,t,n),S("shape",e,t,n))];case"MirrorPad":return[bv(S("x",e,t,n),S("padding",e,t,n),S("mode",e,t,n))];case"PadV2":case"Pad":return[Xs(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[yf(S("x",e,t,n),s,r)]}case"BatchToSpaceND":{let s=S("blockShape",e,t,n),r=S("crops",e,t,n);return[uf(S("x",e,t,n),s,r)]}case"DepthToSpace":{let s=S("blockSize",e,t,n),r=S("dataFormat",e,t,n).toUpperCase();return[av(S("x",e,t,n),s,r)]}case"BroadcastTo":return[Td(S("x",e,t,n),S("shape",e,t,n))];case"BroadcastArgs":return[Y3(S("s0",e,t,n),S("s1",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function w7(e,t,n,s){let r=((a,o,i)=>{switch(a.category){case"arithmetic":return q(()=>wU(a,o,i));case"basic_math":return q(()=>kU(a,o,i));case"control":return EU(a,o,i);case"convolution":return q(()=>RU(a,o,i));case"creation":return q(()=>$U(a,o,i));case"dynamic":return _U(a,o,i);case"evaluation":return q(()=>DU(a,o,i));case"image":return q(()=>MU(a,o,i));case"graph":return q(()=>PU(a,o,i));case"logical":return q(()=>zU(a,o,i));case"matrices":return q(()=>LU(a,o,i));case"normalization":return q(()=>BU(a,o,i));case"reduction":return q(()=>WU(a,o,i));case"slice_join":return q(()=>VU(a,o,i));case"sparse":return q(()=>UU(a,o,i));case"spectral":return q(()=>GU(a,o,i));case"string":return q(()=>HU(a,o,i));case"transformation":return q(()=>jU(a,o,i));case"hash_table":return OU(a,o,i,s);case"custom":let l=Zk(a.op);if(l&&l.customExecutor)return l.customExecutor(new vU(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 k7=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 S7(e,t,n,s){let r=new Set,a=[],o=null,i=null,l=new Set,c=Object.keys(e).map(p=>ms(p)[0]),u=[];s!=null&&(u=s.map(p=>ms(p.name)[0]));let d=[...t];for(;d.length>0;){let p=d.pop();if((I7(p)||YU(p)||JU(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 qU(e,t,n){let{usedNodes:s,inputs:r}=n,a=[],o=Object.keys(r).map(u=>ms(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 XU=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],KU=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],ZU=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function I7(e){return XU.indexOf(e.op)>=0}function YU(e){return KU.indexOf(e.op)>=0}function JU(e){return ZU.indexOf(e.op)>=0}var Ry=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 Ry(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=S7(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 qU(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[ms(u)[0]]),r=t.map(u=>ms(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 q(()=>{let u=new k7(this.weightMap,l,c,this.functionExecutorMap),d={...this.weightMap};Object.keys(e).forEach(f=>{let[m,g]=ms(f),A=[];A[g]=e[f],d[m]=A});let p=this.getFrozenTensorIds(d),h={};for(let f=0;f<i.length;f++){let m=i[f];if(!d[m.name]){let g=w7(m,d,u,this._resourceManager);if(v.isPromise(g))throw new Error(`The execution of the op '${m.op}' returned a promise. Please use model.executeAsync() instead.`);d[m.name]=g,this.checkTensorForDisposal(m.name,m,d,u,p,r,h)}}return this.parent==null&&u.dispose(p),t.map(f=>Un(f,d,u))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(s=>s.id)));return new Set(t)}checkTensorForDisposal(e,t,n,s,r,a,o){t.category==="control"||a.indexOf(e)!==-1||(n[e].forEach(i=>{i!=null&&(o[i.id]=(o[i.id]||0)+t.children.length)}),t.inputs.forEach(i=>{if(i.category!=="control"){let l=eU(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]=Mr(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=K().getBool("KEEP_INTERMEDIATE_TENSORS")}catch(c){console.warn(c.message)}this.resetIntermediateTensors();let a=new k7(this.weightMap,s,r,this.functionExecutorMap);this.tensorsMap=await this.executeWithControlFlow(e,a,t,n);let o=t.map(c=>Un(c,this.tensorsMap,a)),i=o.map(c=>c.id),l=Object.keys(e).map(c=>e[c].id);return this.keepIds=new Set([...i,...l,...this.weightIds]),this.keepTensorForDebug||this.disposeTensorsMap(),this.parent==null&&a.dispose(this.keepIds),o}async executeFunctionAsync(e,t,n){let s=e.reduce((r,a,o)=>(r[this.inputs[o].name]=a,r),{});return this._executeAsync(s,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,s){let r=Object.keys(e),a=r.map(x=>this.graph.nodes[ms(x)[0]]),o=n.map(x=>ms(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}=S7(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[y,b]=ms(x),w=[];w[b]=e[x],h[y]=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 A=i.filter(x=>!I7(x)&&!Un(x.name,h,t)).map(x=>x.name);if(A.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 [${A}] from the provided inputs [${r}]. Consider providing the following inputs: [${c}]. ${x}`)}return h}processStack(e,t,n,s,r,a,o,i,l){let c=[];for(;t.length>0;){let u=t.pop();n.currentContext=u.contexts;let d="";if(u.node.op==="Enter"&&S("isConstant",u.node,s,n)&&([d]=Mr(u.node.name,n)),s[u.node.name]==null){let p=w7(u.node,s,n,this._resourceManager);d||([d]=Mr(u.node.name,n));let h=n.currentContext;v.isPromise(p)?c.push(p.then(f=>(s[d]=f,n.currentContext=h,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l),f))):(s[d]=p,this.checkTensorForDisposal(d,u.node,s,n,a,o,i),this.processChildNodes(u.node,t,n,s,r,l))}else this.processChildNodes(u.node,t,n,s,r,l)}return c}processChildNodes(e,t,n,s,r,a){e.children.forEach(o=>{let[i]=Mr(o.name,n);r[i]||!a.has(o.name)||(o.op==="Merge"?o.inputNames.some(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})):o.inputNames.every(l=>!!Un(l,s,n))&&(r[i]=!0,t.push({contexts:n.currentContext,node:o})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let n=e[t],[s]=ms(t),r=this.graph.nodes[s];if(r.attrParams.shape&&r.attrParams.shape.value){let a=r.attrParams.shape.value,o=a.length===n.shape.length&&n.shape.every((i,l)=>a[l]===-1||a[l]===i);v.assert(o,()=>`The shape of dict['${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 ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let s=this._signature.inputs[n];t[s.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[s]=ms(n);return this.graph.nodes[s]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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]=ms(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},QU=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},eG="?tfjs-format=file",tG="model.json",C7=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new QU}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=ns.browserHTTPRequest(e,this.loadOptions);else{let t=ns.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(ns.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${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}.${t.versions.minConsumer}`;let s=ns.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Ry(g7.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(s),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=g7.Instance.transformGraph(e.modelInitializer);this.initializer=new Ry(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=ns.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${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 Je)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,s)=>(t[n]=e[s],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}getIntermediateTensors(){return this.executor.getIntermediateTensors()}disposeIntermediateTensors(){this.executor.disposeIntermediateTensors()}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function Xe(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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s.delete(e),e.__proto__&&(a.__proto__=e.__proto__),a}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function oG(e,t=E7){return N7(e,t)}function N7(e,t,n=new Set){let s=e[0];if(n.has(s))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(Ju(s)){let a=Array.isArray(s)?[]:{};n.add(s);for(let o in s){let i=e.map(c=>c[o]),l=N7(i,t,n);a[o]=l}return n.delete(s),a}else throw new Error(`Can't recurse into non-iterable type: ${s}`);else return r.value}function E7(e){return e===null?null:Ju(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function R7(e,t){let n=new Map;Am(e,t,n);for(let r of Array.from(n.keys())){let a=n.get(r);if(v.isPromise(a)){let o=await a;n.set(r,o)}}return Am(e,t,n)}function Ju(e){let t=!1;if(K().get("IS_BROWSER"))t=e instanceof TextDecoder;else{let{StringDecoder:n}=v5();t=e instanceof n}return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof Je)&&!(e instanceof Promise)&&!t)}function iG(e){return e==null||lG(e)||Array.isArray(e)||typeof e=="object"&&e instanceof Je||v.isTypedArray(e)}function lG(e){return e===null||typeof e!="object"&&typeof e!="function"}function uG(e){return aG(e,cG)}function cG(e){return e instanceof Je?{value:e.clone(),recurse:!1}:Ju(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var $7=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},_7=class extends $7{constructor(){super(_7.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let s=0;s<n;s++)t[s]=this.get(this.wrap(this.begin+s));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}},D7=_7;D7.INITIAL_CAPACITY=32;function P7(e){return new hG(e)}function $y(e){return new fG(e)}function dG(e,t){return new O7(e,t)}function pG(e,t=ym.FAIL){return new kG(e,t)}var bn=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new vG(this,e)}filter(e){return new xG(this,e)}map(e){return new bG(this,e)}mapAsync(e){return new F7(this,e)}serialMapAsync(e){return new F7(this,e).serial()}flatmap(e){return new wG(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 yG(this,e,t)}columnMajorBatch(e,t=!0,n=E7){return this.rowMajorBatch(e,t).map(r=>oG(r,n))}concatenate(e,t){return new O7(P7([this,e]),t)}take(e){return e<0||e==null?this:new AG(this,e)}skip(e){return e<0||e==null?this:new gG(this,e)}prefetch(e){return new M7(this,e)}shuffle(e,t){return new SG(this,e,t)}serial(){return new mG(this)}},hG=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 e=this.items[this.trav];return this.trav++,{value:uG(e),done:!1}}},fG=class extends bn{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: ${e.message}`,e}}},mG=class extends bn{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},gG=class extends bn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;ne(e.value)}return this.upstream.next()}},AG=class extends bn{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},yG=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`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},xG=class extends bn{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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;ne(e.value)}}},bG=class extends bn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=or.getTensorsInContainer(e.value),n=this.transform(e.value),s=or.getTensorsInContainer(n);for(let r of t)or.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},vG=class extends bn{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},F7=class extends bn{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=or.getTensorsInContainer(e.value),n=await this.transform(e.value),s=or.getTensorsInContainer(n);for(let r of t)or.isTensorInList(r,s)||r.dispose();return{value:n,done:!1}}},_y=class extends bn{constructor(){super();this.outputQueue=new D7,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},wG=class extends _y{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=or.getTensorsInContainer(e.value),n=this.transform(e.value),s=or.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)or.isTensorInList(r,s)||r.dispose();return!0}},O7=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}},ym;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(ym||(ym={}));var kG=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 R7(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}},M7=class extends bn{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new $7(t)}summary(){return`${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()}},SG=class extends M7{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=rG.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}}},Qu=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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${e}`);let s;return this.size===1/0||this.size==null?s=this.size:t?s=Math.ceil(this.size/e):s=Math.floor(this.size/e),gs(async()=>(await n.iterator()).columnMajorBatch(e,t,TG),s)}concatenate(e){let t=this,n;return this.size===1/0||e.size===1/0?n=1/0:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,gs(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===1/0?n=1/0:n=null,gs(async()=>(await t.iterator()).filter(s=>q(()=>e(s))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return gs(async()=>(await t.iterator()).map(n=>q(()=>e(n))),this.size)}mapAsync(e){let t=this;return gs(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return gs(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=1/0:n=null,gs(async()=>{let s=$y(async()=>({value:await t.iterator(),done:!1}));return dG(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,gs(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=sG.alea(t||v.now().toString());return gs(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,gs(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()}};Qu.MAX_BUFFER_SIZE=1e4;function gs(e,t=null){return new class extends Qu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function IG(e){return gs(async()=>P7(e),e.length)}function CG(e){if(!Ju(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 gs(async()=>{let n=await R7(e,s=>{if(s instanceof Qu)return{value:s.iterator(),recurse:!1};if(Ju(s))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return pG(n,ym.SHORTEST)},t)}function TG(e){if(e===null)return null;let t=e[0];return iG(t)?{value:NG(e),recurse:!1}:{value:null,recurse:!0}}function NG(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof Je?yn(e):Pt(e)}var z7=class extends Qu{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))}},xm='"',np=Symbol("out"),L7=Symbol("field"),bm=Symbol("quote"),Dy=Symbol("quoteafterquote"),B7=Symbol("quoteinquote"),W7=class extends Qu{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 z7(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=np;for(let o=0;o<r;o++)switch(a){case np:switch(e.charAt(o)){case xm:s=o+1,a=bm;break;case this.delimiter:if(s=o+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),a=np;break;default:a=L7,s=o;break}break;case L7:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o)),a=np,s=o+1;break;default:}break;case bm:switch(e.charAt(o)){case xm:a=Dy;break;default:}break;case Dy:switch(e.charAt(o)){case this.delimiter:n.push(e.substring(s,o-1)),a=np,s=o+1;break;case xm:a=bm;break;default:a=B7;break}break;case B7:switch(e.charAt(o)){case xm:a=bm;break;default:}break;default:}if(a===Dy?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}},V7=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(K().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new V7(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let s=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(s,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let s=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(s,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(s=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-1/0&&s({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),s({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((s,r)=>n.set(s,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(v.sizeFromShape(t));return n.set(e,n.length-e.length),Pt(n,t)}},U7=class extends bn{constructor(e,t){super();if(this.webcamVideoElement=e,this.webcamConfig=t,this.isClosed=!0,this.resize=!1,this.needToResize())if(this.resize=!0,this.cropSize=[this.webcamConfig.resizeHeight,this.webcamConfig.resizeWidth],this.cropBoxInd=Kt([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=ur([a,r,i,o],[1,4])}else this.cropBox=ur([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(K().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new U7(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&v.assert(this.webcamConfig.facingMode==="user"||this.webcamConfig.facingMode==="environment",()=>`Invalid webcam facing mode: ${this.webcamConfig.facingMode}. Please provide 'user' or 'environment'`);try{this.stream=await navigator.mediaDevices.getUserMedia({video:{deviceId:this.webcamConfig.deviceId,facingMode:this.webcamConfig.facingMode?this.webcamConfig.facingMode:"user",width:this.webcamVideoElement.width,height:this.webcamVideoElement.height}})}catch(e){throw e.message=`Error thrown while initializing video stream: ${e.message}`,e}if(!this.stream)throw new Error("Could not obtain video from webcam.");try{this.webcamVideoElement.srcObject=this.stream}catch(e){console.log(e),this.webcamVideoElement.src=window.URL.createObjectURL(this.stream)}return this.webcamVideoElement.play(),this.isClosed=!1,new Promise(e=>{this.webcamVideoElement.onloadedmetadata=()=>{e()}})}async next(){if(this.isClosed)return{value:null,done:!0};let e;try{e=Hs.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return q(()=>{let t=Xt(pe(e,"float32"),0),n;n=$e.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let s=n.shape;return G(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.")}},G7=class{},H7=class extends bn{split(e){return new EG(this,e)}},EG=class extends H7{constructor(e,t){super();this.upstream=e,this.impl=new RG(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},RG=class extends _y{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}},$G=class extends bn{decodeUTF8(){return new _G(this)}},_G=class extends H7{constructor(e){super();this.upstream=e,this.impl=new DG(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},DG=class extends _y{constructor(e){super();if(this.upstream=e,K().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=v5();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return K().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},j7=class extends $G{constructor(e,t={}){super();this.file=e,this.options=t,v.assert(e instanceof Uint8Array||(K().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 ${this.file}`}async next(){return this.offset>=(this.file instanceof Uint8Array?this.file.byteLength:this.file.size)?{value:null,done:!0}:{value:await new Promise((t,n)=>{let s=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,s)));else{let r=new FileReader;r.onload=o=>{let i=r.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return n(new TypeError("FileReader returned unknown type."));t(i)},r.onabort=o=>n(new Error("Aborted")),r.onerror=o=>n(new Error(o.type));let a=this.file.slice(this.offset,s);r.readAsArrayBuffer(a)}this.offset=s}),done:!1}}};async function PG(e,t={},n){let s,r;typeof e=="string"?s=e:(s=e.url,r=FG(e));let a=await(n||v.fetch)(s,r);if(a.ok){let o=new Uint8Array(await a.arrayBuffer());return new j7(o,t)}else throw new Error(a.statusText)}var FG=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function q7(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var X7=class extends G7{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(q7(this.input)&&K().get("IS_NODE")){let e=Vs("fs");this.input=e.readFileSync(this.input.substr(7))}return new j7(this.input,this.options)}},K7=class extends G7{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return q7(this.url)?new X7(this.url,this.fileOptions).iterator():PG(this.url,this.fileOptions)}};function OG(e,t={}){return new W7(new K7(e),t)}function MG(e){let t=$y(e);return gs(async()=>t)}function zG(e){return gs(async()=>{let t=await e();return $y(()=>t.next())})}async function LG(e,t){return U7.create(e,t)}async function BG(e){return V7.create(e)}var WG="0.0.0";function Ne(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&v.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var VG=Ks.whereImpl,Z7=class extends eu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Zc(this,ss())}nextDataId(){return Z7.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,K().get("IS_NODE")&&E.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 E.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 ss().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){Ne([e],"where");let t=this.readSync(e.dataId);return VG(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}},Py=Z7;Py.nextDataId=0;var vm={};Oe(vm,{addImpl:()=>J7,bincountImpl:()=>Oy,bincountReduceImpl:()=>Q7,ceilImpl:()=>eS,concatImpl:()=>My,equalImpl:()=>tS,expImpl:()=>sS,expm1Impl:()=>aS,floorImpl:()=>oS,gatherNdImpl:()=>iS,gatherV2Impl:()=>lS,greaterEqualImpl:()=>cS,greaterImpl:()=>uS,lessEqualImpl:()=>pS,lessImpl:()=>dS,linSpaceImpl:()=>hS,logImpl:()=>fS,maxImpl:()=>mS,maximumImpl:()=>gS,minimumImpl:()=>AS,multiplyImpl:()=>zy,negImpl:()=>yS,notEqualImpl:()=>xS,prodImpl:()=>bS,rangeImpl:()=>By,rsqrtImpl:()=>vS,sigmoidImpl:()=>RH,simpleAbsImpl:()=>Y7,sliceImpl:()=>Sm,sparseFillEmptyRowsImpl:()=>kS,sparseReshapeImpl:()=>SS,sparseSegmentReductionImpl:()=>Wy,sqrtImpl:()=>DH,squaredDifferenceImpl:()=>IS,stridedSliceImpl:()=>CS,stringNGramsImpl:()=>TS,stringSplitImpl:()=>NS,stringToHashBucketFastImpl:()=>ES,subImpl:()=>RS,tileImpl:()=>$S,topKImpl:()=>DS,transposeImpl:()=>Ly,uniqueImpl:()=>PS});function Y7(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var UG=e=>{let{x:t}=e.inputs,n=e.backend;Ne(t,"abs");let s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return s=Y7(r),n.makeOutput(s,t.shape,t.dtype)},GG={kernelName:di,backendName:"cpu",kernelFunc:UG};function Yt(e){return(t,n,s,r,a)=>{let o=E.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=E.getBroadcastDims(t,o),g=E.getBroadcastDims(n,o);if(m.length+g.length===0)for(let A=0;A<u.length;++A)u[A]=e(s[A%s.length],r[A%r.length]);else for(let A=0;A<u.length;++A){let x=v.indexToLoc(A,i,l),y=x.slice(-d);m.forEach(C=>y[C]=0);let b=v.locToIndex(y,d,h),w=x.slice(-p);g.forEach(C=>w[C]=0);let k=v.locToIndex(w,p,f);u[A]=e(s[b],r[k])}return[u,o]}}function As(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 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indices.shape[0] = ${i}`);let g=v.getArrayFromDType(n,0),A=v.getArrayFromDType(r,0);return[g,[0,d],A,c,u]}let p=!0,h=0,f=new Array(l).fill(0);for(let g=0;g<i;++g){let A=e[g*d];if(A<0)throw new Error(`indices(${g}, 0) is invalid: ${A} < 0`);if(A>=l)throw new Error(`indices(${g}, 0) is invalid: ${A} >= ${l}`);++f[A],p=p&&A>=h,h=A}let m=!0;for(let g=0;g<l;++g){let A=f[g]===0;c[g]=A,m=m&&!A,f[g]=Math.max(f[g],1),g>0&&(f[g]+=f[g-1])}if(m&&p){let g=e,A=s;for(let x=0;x<i;++x)u[x]=x;return[g,[i,d],A,c,u]}else{let g=f[l-1],A=v.getArrayFromDType(n,g*d),x=v.getArrayFromDType(r,g),y=new Array(l).fill(0);for(let b=0;b<i;++b){let w=e[b*d],k=y[w],C=(w===0?0:f[w-1])+k;y[w]++;for(let N=0;N<d;++N)A[C*d+N]=e[b*d+N];x[C]=s[b],u[b]=C}for(let b=0;b<l;++b)if(y[b]===0){let k=b===0?0:f[b-1];A[k*d+0]=b;for(let C=1;C<d;++C)A[k*d+C]=0;x[k]=o}return[A,[g,d],x,c,u]}}function SS(e,t,n,s,r){let a=v.sizeFromShape(s),o=t[0],i=r.length,l=[],c=1,u=-1;for(let g=0;g<i;++g){let A=r[g];if(A===-1){if(u!==-1)throw new Error(`only one output dimension may be -1, not both ${u} and ${g}`);u=g,l.push(1)}else{if(A<0)throw new Error(`size ${g} must be non-negative, not ${A}`);c*=A,l.push(A)}}if(u!==-1){if(c<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let g=Math.trunc(a/c);if(c*g!==a)throw new Error(`Input to reshape is a SparseTensor with ${a}
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dense values, but the requested shape requires a multiple of ${c}. inputShape=${s} outputShape= ${l}`);l[u]=g}let d=v.sizeFromShape(l);if(d!==a)throw new Error(`Input to reshape is a tensor with ${a} dense values, but the requested shape has ${d}. inputShape=${s} outputShape=${l}`);let p=s.length,h=[];if(p>0){h[p-1]=1;for(let g=p-2;g>=0;--g)h[g]=h[g+1]*s[g+1]}let f=[];if(i>0){f[i-1]=1;for(let g=i-2;g>=0;--g)f[g]=f[g+1]*l[g+1]}let m=v.getArrayFromDType(n,o*i);for(let g=0;g<o;++g){let A=0;for(let x=0;x<p;++x)A+=e[g*p+x]*h[x];for(let x=0;x<i;++x)m[g*i+x]=Math.trunc(A/f[x]),A%=f[x]}return[m,[o,i],l]}function Wy(e,t,n,s,r,a=!1,o=0){let i=s.length;if(i!==r.length)throw new Error("segmentIds and indices should have same size.");let l=[t[0],e.length/t[0]],c=l[1],d=i>0?r[i-1]+1:0;if(d<0)throw new Error("segment ids must be >= 0");let p=t.slice();p[0]=d;let h=p.reduce((y,b)=>y*b,1),f=v.getArrayFromDType(n,h);if(i===0)return d>0&&f.fill(o),[f,p];if(d<=0)throw new Error("segment ids must be >= 0");let m=0,g=1,A=0,x=r[m];for(;;){let y=0;if(g<i){if(y=r[g],x===y){++g;continue}if(x>=y)throw new Error("segment ids are not increasing")}if(x<0||x>=d)throw new Error(`Segment id ${x} out of range [0, ${d}), possibly because segmentIds input is not sorted.`);x>A&&f.fill(o,A*c,x*c);for(let b=m;b<g;++b){let w=s[b];if(w<0||w>=l[0])throw new Error(`Bad: indices[${b}] == ${s[b]} out of range [0, ${l[0]})`);for(let k=0;k<c;k++)f[x*c+k]+=e[w*c+k]}if(a)for(let b=0;b<c;b++)f[x*c+b]/=g-m;if(m=g,++g,A=x+1,x=y,g>i)break}return A<d&&f.fill(o,A*c,d*c),[f,p]}var DH=Vo(e=>Math.sqrt(e)),PH=ht(co,e=>Math.sqrt(e)),FH={kernelName:co,backendName:"cpu",kernelFunc:PH},IS=Yt((e,t)=>{let n=e-t;return n*n}),OH=vn(fo,IS),MH={kernelName:fo,backendName:"cpu",kernelFunc:OH};function CS(e,t,n,s){let r=ze(e,t.dtype);for(let a=0;a<r.size;a++){let o=r.indexToLoc(a),i=new Array(o.length);for(let l=0;l<i.length;l++)i[l]=o[l]*n[l]+s[l];r.set(t.get(...i),...o)}return r}var zH=class{constructor(e,t,n,s,r,a){this.separator=v.encodeString(e),this.nGramWidths=t,this.leftPad=v.encodeString(n),this.rightPad=v.encodeString(s),this.padWidth=r,this.preserveShort=a}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,s,r,a){for(let o=0;o<r;++o){let i=this.getPadWidth(a),l=Math.max(0,i-o),c=Math.max(0,i-(r-(o+1))),u=a-(l+c),d=t+(l>0?0:o-i),p=0;p+=l*this.leftPad.length;for(let A=0;A<u;++A)p+=e[d+A].length;p+=c*this.rightPad.length,p+=(l+c+u-1)*this.separator.length,n[s+o]=new Uint8Array(p);let f=n[s+o],m=0,g=A=>A.forEach(x=>f[m++]=x);for(let A=0;A<l;++A)g(this.leftPad),g(this.separator);for(let A=0;A<u-1;++A)g(e[d+A]),g(this.separator);if(u>0){g(e[d+u-1]);for(let A=0;A<c;++A)g(this.separator),g(this.rightPad)}else{for(let A=0;A<c-1;++A)g(this.rightPad),g(this.separator);g(this.rightPad)}}}compute(e,t){let n=e.length,s=t.length;if(s>0){let i=t[0];if(i!==0)throw new Error(`First split value must be 0, got ${i}`);for(let l=1;l<s;++l){let c=t[l]>=i;if(c=c&&t[l]<=n,!c)throw new Error(`Invalid split value ${t[l]}, must be in [${i}, ${n}]`);i=t[l]}if(i!==n)throw new Error(`Last split value must be data size. 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ZH={kernelName:Ra,backendName:"cpu",kernelFunc:BS};function YH(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=BS({inputs:{a:r,b:a},attrs:{transposeA:l,transposeB:c},backend:n}),o&&(h=sp({inputs:{a:p,b:o},backend:n}),m.push(p),p=h),u&&(f=Uy(n,p,u,i,d),m.push(p),p=f);for(let A of m)n.disposeIntermediateTensorInfo(A);return p}var JH={kernelName:xo,backendName:"cpu",kernelFunc:YH},QH=ht(su,e=>Math.acos(e)),ej={kernelName:su,backendName:"cpu",kernelFunc:QH},tj=ht(ru,e=>Math.acosh(e)),nj={kernelName:ru,backendName:"cpu",kernelFunc:tj};function sj(e){let{inputs:t,backend:n}=e,s=t;Ne(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 rj={kernelName:Ta,backendName:"cpu",kernelFunc:sj};function 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pj={kernelName:iu,backendName:"cpu",kernelFunc:dj},hj=ht(lu,e=>Math.asin(e)),fj={kernelName:lu,backendName:"cpu",kernelFunc:hj},mj=ht(uu,e=>Math.asinh(e)),gj={kernelName:uu,backendName:"cpu",kernelFunc:mj},Aj=ht(cu,e=>Math.atan(e)),yj={kernelName:cu,backendName:"cpu",kernelFunc:Aj},xj=Yt((e,t)=>Math.atan2(e,t)),bj=vn(pu,xj),vj={kernelName:pu,backendName:"cpu",kernelFunc:bj},wj=ht(du,e=>Math.atanh(e)),kj={kernelName:du,backendName:"cpu",kernelFunc:wj};function Gy(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,A=r.outShape[1]*r.outShape[2]*r.outShape[3],x=r.outShape[2]*r.outShape[3],y=r.outShape[3];for(let b=0;b<r.batchSize;++b){let w=b*A,k=b*s[0];for(let C=0;C<r.inChannels;++C)for(let N=0;N<r.outHeight;++N){let 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n.makeTensorInfo(r.shape,r.dtype,m)}var Pj={kernelName:Ua,backendName:"cpu",kernelFunc:Dj};function Fj(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s;Ne([r],"batchToSpaceND");let i=a.reduce((A,x)=>A*x),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=Rt({inputs:{x:r},backend:n,attrs:{shape:l}}),f=Ds({inputs:{x:h},backend:n,attrs:{perm:c}}),m=Rt({inputs:{x:f},backend:n,attrs:{shape:u}}),g=Sl({inputs:{x:m},backend:n,attrs:{begin:d,size:p}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(m),g}var Oj={kernelName:pi,backendName:"cpu",kernelFunc:Fj};function Mj(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=Oy(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var zj={kernelName:yh,backendName:"cpu",kernelFunc:Mj};function Lj(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.data.get(s.dataId).values,o=n.data.get(r.dataId).values,i=E.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var Bj={kernelName:xh,backendName:"cpu",kernelFunc:Lj},Wj=ht(jr,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),Vj={kernelName:jr,backendName:"cpu",kernelFunc:Wj},Uj=e=>{let{x:t}=e.inputs,n=e.backend,s=new Float32Array(v.sizeFromShape(t.shape)),r=n.data.get(t.dataId),a=r.complexTensorInfos.real,o=r.complexTensorInfos.imag,i=n.data.get(a.dataId).values,l=n.data.get(o.dataId).values;for(let c=0;c<i.length;c++){let u=i[c],d=l[c];s[c]=Math.hypot(u,d)}return n.makeOutput(s,t.shape,"float32")},Gj={kernelName:td,backendName:"cpu",kernelFunc:Uj};function 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n.makeTensorInfo(x.shape,x.dtype,x.values)}var Kj={kernelName:bh,backendName:"cpu",kernelFunc:Xj};function Zj(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;Ne([r,a],"conv2dBackpropInput");let d=v.computeStrides(a.shape),p=v.computeStrides(r.shape),h=E.convertConv2DDataFormat(c),f=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,h),m=new tn(f.inShape,"float32"),g=m.values,A=n.data.get(r.dataId).values,x=n.data.get(a.dataId).values,[y,b,w]=d,{batchSize:k,filterHeight:C,filterWidth:N,inChannels:R,inHeight:F,inWidth:_,outChannels:P,outHeight:T,outWidth:M,strideHeight:U,strideWidth:j}=f;h=f.dataFormat;let z=C-1-f.padInfo.top,X=N-1-f.padInfo.left,Z=h==="channelsLast",J=m.strides[0],ee=Z?m.strides[1]:m.strides[2],re=Z?m.strides[2]:1,Q=Z?1:m.strides[1],te=p[0],oe=Z?p[1]:p[2],fe=Z?p[2]:1,be=Z?1:p[1];for(let we=0;we<k;++we)for(let Ce=0;Ce<R;++Ce)for(let Me=0;Me<F;++Me){let 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c=v.computeStrides(r.shape),u=v.computeStrides(a.shape),d=E.computeConv3DInfo(r.shape,l,o,1,i),p=d.strideDepth,h=d.strideHeight,f=d.strideWidth,m=d.filterDepth,g=d.filterHeight,A=d.filterWidth,x=new tn(d.filterShape,"float32"),y=x.values,[b,w,k,C]=x.strides,N=n.data.get(a.dataId).values,[R,F,_,P]=u,T=n.data.get(r.dataId).values,[M,U,j,z]=c,X=d.padInfo.front,Z=d.padInfo.left,J=d.padInfo.top;for(let ee=0;ee<m;++ee){let re=Math.max(0,Math.ceil((X-ee)/p)),Q=Math.min(d.outDepth,(d.inDepth+X-ee)/p),te=ee*b;for(let oe=0;oe<g;++oe){let fe=Math.max(0,Math.ceil((J-oe)/h)),be=Math.min(d.outHeight,(d.inHeight+J-oe)/h),we=oe*w+te;for(let Ce=0;Ce<A;++Ce){let Me=Math.max(0,Math.ceil((Z-Ce)/f)),We=Math.min(d.outWidth,(d.inWidth+Z-Ce)/f),He=Ce*k+we;for(let qe=0;qe<d.inChannels;++qe){let ct=qe*C+He;for(let dt=0;dt<d.outChannels;++dt){let rt=0;for(let wt=0;wt<d.batchSize;++wt){let ft=wt*M,Ct=wt*R;for(let _t=re;_t<Q;++_t){let kn=(ee+_t*p-X)*U+ft,sr=_t*F+Ct;for(let Fn=fe;Fn<be;++Fn){let Bs=(oe+Fn*h-J)*j+kn,Ss=Fn*_+sr;for(let Sn=Me;Sn<We;++Sn){let Rn=(Ce+Sn*f-Z)*z+Bs,kr=Sn*P+Ss;rt+=T[Rn+qe]*N[kr+dt]}}}}y[ct+dt]=rt}}}}}return n.makeTensorInfo(x.shape,x.dtype,x.values)}var tq={kernelName:vh,backendName:"cpu",kernelFunc:eq};function nq(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s;Ne([r],"conv3dBackpropInputV2");let c=v.computeStrides(r.shape),u=v.computeStrides(a.shape),d=E.computeConv3DInfo(l,a.shape,i,1,o),p=new tn(d.inShape,"float32"),h=p.values,[f,m,g,A]=p.strides,x=n.data.get(r.dataId).values,[y,b,w,k]=c,C=n.data.get(a.dataId).values,[N,R,F,_]=u,{batchSize:P,filterDepth:T,filterHeight:M,filterWidth:U,inChannels:j,inDepth:z,inHeight:X,inWidth:Z,outChannels:J,outDepth:ee,outHeight:re,outWidth:Q,strideDepth:te,strideHeight:oe,strideWidth:fe}=d,be=T-1-d.padInfo.front,we=M-1-d.padInfo.top,Ce=U-1-d.padInfo.left;for(let Me=0;Me<P;++Me)for(let We=0;We<j;++We)for(let He=0;He<z;++He){let 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lq(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,A=ze([f,m,g,h],"float32"),x=n.data.get(a.dataId).values,y=n.data.get(o.dataId).values,b=n.data.get(r.dataId).values,w=v.computeStrides(r.shape),k=v.computeStrides(A.shape);for(let C=0;C<f;C++){let N=C*4,R=x[N],F=x[N+1],_=x[N+2],P=x[N+3],T=y[C];if(T>=u)continue;let M=m>1?(_-R)*(d-1)/(m-1):0,U=g>1?(P-F)*(p-1)/(g-1):0;for(let j=0;j<m;j++){let z=m>1?R*(d-1)+j*M:.5*(R+_)*(d-1);if(z<0||z>d-1){for(let X=0;X<g;X++)for(let Z=0;Z<h;Z++){let J=Z+X*k[2]+j*k[1]+C*k[0];A.values[J]=c}continue}if(l==="bilinear"){let X=Math.floor(z),Z=Math.ceil(z),J=z-X;for(let ee=0;ee<g;ee++){let re=g>1?F*(p-1)+ee*U:.5*(F+P)*(p-1);if(re<0||re>p-1){for(let fe=0;fe<h;fe++){let be=fe+ee*k[2]+j*k[1]+C*k[0];A.values[be]=c}continue}let Q=Math.floor(re),te=Math.ceil(re),oe=re-Q;for(let fe=0;fe<h;fe++){let be=fe+Q*w[2]+X*w[1]+T*w[0],we=b[be];be=fe+te*w[2]+X*w[1]+T*w[0];let Ce=b[be];be=fe+Q*w[2]+Z*w[1]+T*w[0];let Me=b[be];be=fe+te*w[2]+Z*w[1]+T*w[0];let We=b[be],He=we+(Ce-we)*oe,qe=Me+(We-Me)*oe;be=fe+ee*k[2]+j*k[1]+C*k[0],A.values[be]=He+(qe-He)*J}}}else for(let X=0;X<g;++X){let Z=g>1?F*(p-1)+X*U:.5*(F+P)*(p-1);if(Z<0||Z>p-1){for(let re=0;re<h;re++){let Q=re+X*k[2]+j*k[1]+C*k[0];A.values[Q]=c}continue}let J=Math.round(Z),ee=Math.round(z);for(let re=0;re<h;re++){let Q=re+J*w[2]+ee*w[1]+T*w[0],te=re+X*k[2]+j*k[1]+C*k[0];A.values[te]=b[Q]}}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var uq={kernelName:mi,backendName:"cpu",kernelFunc:lq};function cq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s;Ne(r,"cumsum");let l=E.getAxesPermutation([a],r.shape.length),c=r;l!=null&&(c=Ds({inputs:{x:r},backend:n,attrs:{perm:l}}));let u=E.getInnerMostAxes(1,r.shape.length)[0];if(u!==c.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${c.shape.length-1} but got axis=${u}`);let d=Ln(c.dtype,"int32"),p=v.makeZerosTypedArray(v.sizeFromShape(c.shape),d),h=n.data.get(c.dataId).values,f=c.shape[c.shape.length-1],m=i?(A,x)=>A+f-x-1:(A,x)=>A+x;for(let A=0;A<h.length;A+=f)for(let x=0;x<f;x++){let y=m(A,x);if(x===0)p[y]=o?0:h[y];else{let b=m(A,x-1);p[y]=o?h[b]+p[b]:h[y]+p[b]}}let g=n.makeTensorInfo(c.shape,d,p);if(l!=null){let A=E.getUndoAxesPermutation(l),x=Ds({inputs:{x:g},backend:n,attrs:{perm:A}});return n.disposeIntermediateTensorInfo(g),n.disposeIntermediateTensorInfo(c),x}return g}var dq={kernelName:fi,backendName:"cpu",kernelFunc:cq};function pq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=Oy(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=Q7(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var hq={kernelName:kh,backendName:"cpu",kernelFunc:pq};function fq(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s;v.assert(o==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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Got strides ${o} and dilations '${p}'`);let h=E.computeConv2DInfo(r.shape,a.shape,o,p,i,c,!0),{filterHeight:f,filterWidth:m,dilationHeight:g,dilationWidth:A,padInfo:x}=h,y=x.left,b=x.top,w=h.outChannels/h.inChannels,k=new tn(h.outShape,r.dtype),C=n.data.get(r.dataId).values,N=n.data.get(a.dataId).values,R=k.values;for(let F=0;F<h.batchSize;++F){let _=F*u[0],P=F*k.strides[0];for(let T=0;T<h.outHeight;++T){let M=P+T*k.strides[1],U=T*h.strideHeight-b;for(let j=0;j<f;++j){let z=U+j*g;if(z<0||z>=h.inHeight)continue;let X=j*d[0],Z=_+z*u[1];for(let J=0;J<h.outWidth;++J){let ee=M+J*k.strides[2],re=J*h.strideWidth-y;for(let Q=0;Q<m;++Q){let te=re+Q*A;if(te<0||te>=h.inWidth)continue;let oe=X+Q*d[1],fe=Z+te*h.inChannels,be=ee,we=oe;for(let Ce=0;Ce<h.inChannels;++Ce){let Me=C[fe+Ce];for(let We=0;We<w;++We)R[be+We]+=Me*N[we+We];be+=w,we+=w}}}}}}return n.makeTensorInfo(k.shape,k.dtype,k.values)}var gq={kernelName:Ma,backendName:"cpu",kernelFunc:GS};function Aq(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;Ne([r,a],"depthwiseConv2dNativeBackpropFilter");let d=E.computeConv2DInfo(r.shape,u,o,i,l,c,!0),{strideHeight:p,strideWidth:h,filterHeight:f,filterWidth:m}=d,g=new tn(d.filterShape,"float32"),A=d.padInfo.left,x=d.padInfo.top,y=d.outChannels/d.inChannels,b=n.data.get(r.dataId).values,w=new tn(r.shape,r.dtype,b),k=n.data.get(a.dataId).values,C=new tn(a.shape,a.dtype,k);for(let N=0;N<f;++N){let R=Math.max(0,Math.ceil((x-N)/p)),F=Math.min(d.outHeight,(d.inHeight+x-N)/p);for(let _=0;_<m;++_){let P=Math.max(0,Math.ceil((A-_)/h)),T=Math.min(d.outWidth,(d.inWidth+A-_)/h);for(let M=0;M<d.outChannels;++M){let U=Math.trunc(M/y),j=M%y,z=0;for(let X=0;X<d.batchSize;++X)for(let Z=R;Z<F;++Z){let J=N+Z*p-x;for(let ee=P;ee<T;++ee){let re=_+ee*h-A;z+=w.get(X,J,re,U)*C.get(X,Z,ee,M)}}g.set(z,N,_,U,j)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var yq={kernelName:Sh,backendName:"cpu",kernelFunc:Aq};function xq(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;Ne([r,a],"depthwiseConv2DNativeBackpropInput");let d=v.computeStrides(r.shape),p=v.computeStrides(a.shape),h=E.computeConv2DInfo(u,a.shape,o,i,l,c,!0),f=new tn(h.inShape,"float32"),m=f.values,[g,A,x]=f.strides,y=n.data.get(r.dataId).values,[b,w,k]=d,C=n.data.get(a.dataId).values,[N,R,F]=p,{batchSize:_,filterHeight:P,filterWidth:T,inChannels:M,inHeight:U,inWidth:j,outChannels:z,outHeight:X,outWidth:Z,strideHeight:J,strideWidth:ee}=h,re=P-1-h.padInfo.top,Q=T-1-h.padInfo.left,te=z/M;for(let oe=0;oe<_;++oe)for(let fe=0;fe<M;++fe)for(let be=0;be<U;++be){let we=be-re,Ce=Math.max(0,Math.ceil(we/J)),Me=Math.min(X,(P+we)/J);for(let We=0;We<j;++We){let He=We-Q,qe=Math.max(0,Math.ceil(He/ee)),ct=Math.min(Z,(T+He)/ee),dt=0;for(let rt=Ce;rt<Me;++rt){let wt=rt*J-we;for(let ft=qe;ft<ct;++ft){let Ct=ft*ee-He,_t=b*oe+w*rt+k*ft,ks=N*(P-1-wt)+R*(T-1-Ct)+F*fe;for(let kn=0;kn<te;++kn){let sr=fe*te+kn,Fn=y[_t+sr],us=C[ks+kn];dt+=Fn*us}}}m[g*oe+A*be+x*We+fe]=dt}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var bq={kernelName:Ih,backendName:"cpu",kernelFunc:xq};function vq(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 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MK(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t;Ne([s,r,a],"select");let o=s.shape.length,i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=Ln(r.dtype,a.dtype),d=v.makeZerosTypedArray(v.sizeFromShape(r.shape),u),p=0,h=o===0||o>1||r.shape.length===1?1:v.sizeFromShape(r.shape.slice(1));for(let f=0;f<i.length;f++)for(let m=0;m<h;m++)i[f]===1?d[p++]=l[f]:d[p++]=c[f];return n.makeTensorInfo(r.shape,u,d)}var zK={kernelName:Bi,backendName:"cpu",kernelFunc:MK},LK=E.SELU_SCALEALPHA,BK=E.SELU_SCALE,WK=ht(Iu,e=>e>=0?BK*e:LK*(Math.exp(e)-1)),VK={kernelName:Iu,backendName:"cpu",kernelFunc:WK},UK=ht(Cu,e=>e<0?-1:e>0?1:0),GK={kernelName:Cu,backendName:"cpu",kernelFunc:UK},HK=ht(lo,e=>Math.sin(e)),jK={kernelName:lo,backendName:"cpu",kernelFunc:HK},qK=ht(Vi,e=>Math.sinh(e)),XK={kernelName:Vi,backendName:"cpu",kernelFunc:qK},KK=11920928955078125e-23,JS=Math.log(KK)+2,ZK=ht(Tu,e=>{let t=e>-JS,n=e<JS,s=Math.exp(e),r;return 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|
${a.shape}`);if(s.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
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|
${s.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
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|
${r.shape}`);if(o.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
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|
${o.shape}`);let i=n.data.get(s.dataId).values,l=n.data.get(r.dataId).values,c=n.data.get(a.dataId).values,u=n.data.get(o.dataId).values[0],[d,p,h,f,m]=kS(i,s.shape,s.dtype,l,r.dtype,c,u);return[n.makeTensorInfo(p,s.dtype,d),n.makeTensorInfo([p[0]],r.dtype,h),n.makeTensorInfo([f.length],"bool",new Uint8Array(f.map(g=>Number(g)))),n.makeTensorInfo([m.length],s.dtype,new Int32Array(m))]}var tZ={kernelName:Bh,backendName:"cpu",kernelFunc:eZ};function nZ(e){let{inputs:t,backend:n}=e,{inputIndices:s,inputShape:r,newShape:a}=t;if(s.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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${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]=SS(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var sZ={kernelName:Wh,backendName:"cpu",kernelFunc:nZ};function rZ(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}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[c,u]=Wy(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var aZ={kernelName:Vh,backendName:"cpu",kernelFunc:rZ};function oZ(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}`);let o=n.data.get(s.dataId).values,i=n.data.get(r.dataId).values,l=n.data.get(a.dataId).values,[c,u]=Wy(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var iZ={kernelName:Uh,backendName:"cpu",kernelFunc:oZ};function lZ(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}=E.calculateShapes(a,r,i),h=!1,f=n.bufferSync(r),m=n.bufferSync(a),g=n.data.get(o.dataId).values[0],A=YS(f,m,i,p,u,c,l,d,g,h);return n.makeTensorInfo(i,A.dtype,A.values)}var uZ={kernelName:cd,backendName:"cpu",kernelFunc:lZ};function cZ(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=E.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 h=Sl({inputs:{x:r},backend:n,attrs:{begin:c,size:p}});return c[i]+=d,h})}var 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s=1,r=1;e.texImage2D(e.TEXTURE_2D,0,t.internalFormatFloat,s,r,0,t.textureFormatFloat,t.textureTypeFloat,null);let a=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,a),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,n,0);let o=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(n),e.deleteFramebuffer(a),o}function oY(e,t){let n=Zy(e,t),s=e.createTexture();e.bindTexture(e.TEXTURE_2D,s);let r=1,a=1;e.texImage2D(e.TEXTURE_2D,0,n.internalFormatHalfFloat,r,a,0,n.textureFormatFloat,n.textureTypeHalfFloat,null);let o=e.createFramebuffer();e.bindFramebuffer(e.FRAMEBUFFER,o),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,s,0);let i=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(s),e.deleteFramebuffer(o),i}function SI(e){return e!==2?!1:Lr(e).fenceSync!=null}function rc(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 WebGL backend.`)})}var _e=K();_e.registerFlag("HAS_WEBGL",()=>_e.getNumber("WEBGL_VERSION")>0);_e.registerFlag("WEBGL_VERSION",()=>Qy(2)?2:Qy(1)?1:0);_e.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);_e.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>_e.get("WEBGL_VERSION")===2);_e.registerFlag("WEBGL_CPU_FORWARD",()=>!0);_e.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);_e.registerFlag("WEBGL_PACK",()=>_e.getBool("HAS_WEBGL"));_e.registerFlag("WEBGL_PACK_NORMALIZATION",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_CLIP",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_PACK_REDUCE",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_LAZILY_UNPACK",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_CONV_IM2COL",()=>_e.getBool("WEBGL_PACK"));_e.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>xI(_e.getNumber("WEBGL_VERSION")));_e.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>bI(_e.getNumber("WEBGL_VERSION")));_e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=_e.getNumber("WEBGL_VERSION");return e===0?0:vI(e)});_e.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>_e.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!$u.isMobile());_e.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>wI(_e.getNumber("WEBGL_VERSION")));_e.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>_e.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:_e.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));_e.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>kI(_e.getNumber("WEBGL_VERSION")));_e.registerFlag("WEBGL_FENCE_API_ENABLED",()=>SI(_e.getNumber("WEBGL_VERSION")));_e.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>_e.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);_e.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});_e.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>$u.isMobile()?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});_e.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);_e.registerFlag("WEBGL_USE_SHAPES_UNIFORMS",()=>!1);_e.registerFlag("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e5);_e.registerFlag("TOPK_K_CPU_HANDOFF_THRESHOLD",()=>128);function Gn(){let e,t,n,s,r,a,o,i,l,c;return K().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",s="in",r="texture",a="outputColor",o="out vec4 outputColor;",i=`
|
|
bool isnan_custom(float val) {
|
|
return (val > 0.0 || val < 0.0) ? false : val != 0.0;
|
|
}
|
|
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan_custom(val.x),
|
|
isnan_custom(val.y), isnan_custom(val.z), isnan_custom(val.w));
|
|
}
|
|
|
|
#define isnan(value) isnan_custom(value)
|
|
`,l="",c=`
|
|
#define round(value) newRound(value)
|
|
int newRound(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 newRound(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`):(e="",t="attribute",n="varying",s="varying",r="texture2D",a="gl_FragColor",o="",i=`
|
|
#define isnan(value) isnan_custom(value)
|
|
bool isnan_custom(float val) {
|
|
return (val > 0. || val < 1. || val == 0.) ? false : true;
|
|
}
|
|
bvec4 isnan_custom(vec4 val) {
|
|
return bvec4(isnan(val.x), isnan(val.y), isnan(val.z), isnan(val.w));
|
|
}
|
|
`,l=`
|
|
uniform float INFINITY;
|
|
|
|
bool isinf(float val) {
|
|
return abs(val) == INFINITY;
|
|
}
|
|
bvec4 isinf(vec4 val) {
|
|
return equal(abs(val), vec4(INFINITY));
|
|
}
|
|
`,c=`
|
|
int round(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 round(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`),{version:e,attribute:t,varyingVs:n,varyingFs:s,texture2D:r,output:a,defineOutput:o,defineSpecialNaN:i,defineSpecialInf:l,defineRound:c}}function Nl(e,t,n="index"){let s=v.computeStrides(t);return s.map((r,a)=>{let o=`int ${e[a]} = ${n} / ${r}`,i=a===s.length-1?`int ${e[a+1]} = ${n} - ${e[a]} * ${r}`:`index -= ${e[a]} * ${r}`;return`${o}; ${i};`}).join("")}function Fm(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}; ${i};`}).join("")}function iY(e,t){let n=e.length,s=e.map(a=>`${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 lY(e,t,n="index"){let s=e.map((a,o)=>o),r=iY(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 tx(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 nx(){return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * outShapeStrides[0] + coords.y * outShapeStrides[1] + coords.z;
|
|
}
|
|
`}var II=`
|
|
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:CI}=E;function uY(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}=sx(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?`[${h.arrayIndex}]`:""};`)});let r=s.join(`
|
|
`),a=e.map(h=>cY(h,t,n.packedInputs,n.enableShapeUniforms)).join(`
|
|
`),o=t.texShape,i=Gn(),l=hY(i),c,u,d=gY(i);return t.isPacked?(c=dY(t.logicalShape,o,n.enableShapeUniforms),u=mY(i)):(c=pY(t.logicalShape,o,n.enableShapeUniforms),u=fY(i)),n.packedInputs&&(d+=bY),[d,l,u,r,c,a,n.userCode].join(`
|
|
`)}function ac(e,t=!1){let n=e.shapeInfo.logicalShape;switch(n.length){case 0:return _Y(e,t);case 1:return PY(e,t);case 2:return OY(e,t);case 3:return zY(e,t);case 4:return BY(e,t);case 5:return WY(e);case 6:return VY(e);default:throw new Error(`${n.length}-D input sampling is not yet supported`)}}function TI(e,t){switch(e.shapeInfo.logicalShape.length){case 0:return $Y(e);case 1:return DY(e,t);case 2:return FY(e,t);case 3:return MY(e,t);default:return LY(e,t)}}function cY(e,t,n=!1,s){let r="";n?r+=TI(e,s):r+=ac(e,s);let a=e.shapeInfo.logicalShape,o=t.logicalShape;return a.length<=o.length&&(n?r+=UY(e,t):r+=GY(e,t)),r}function dY(e,t,n){switch(e.length){case 0:return NI();case 1:return vY(e,t,n);case 2:return EY(e,t,n);case 3:return kY(e,t,n);default:return IY(e,t,n)}}function pY(e,t,n){switch(e.length){case 0:return NI();case 1:return wY(e,t,n);case 2:return RY(e,t,n);case 3:return SY(e,t,n);case 4:return CY(e,t,n);case 5:return TY(e,t);case 6:return NY(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function hY(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function fY(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function mY(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function gY(e){return`${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);
|
|
}
|
|
|
|
${AY}
|
|
${yY}
|
|
${xY}
|
|
`}var AY=`
|
|
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);
|
|
}
|
|
`,yY=`
|
|
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);
|
|
}
|
|
`,xY=`
|
|
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);
|
|
}
|
|
`,bY=`
|
|
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 NI(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function vY(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 wY(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 * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function kY(e,t,n){if(n)return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
int texelsInLogicalRow = int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),a=r*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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 SY(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;
|
|
${Fm(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`;let s=Nl(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function IY(e,t,n){if(n)return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(packedTexShape[0], packedTexShape[1]));
|
|
int index = resTexRC.x * packedTexShape[1] + resTexRC.y;
|
|
|
|
int texelsInLogicalRow = int(ceil(float(outShape[3]) / 2.0));
|
|
int texelsInBatch = texelsInLogicalRow * int(ceil(float(outShape[2]) / 2.0));
|
|
int texelsInBatchN = texelsInBatch * outShape[1];
|
|
|
|
int b2 = index / texelsInBatchN;
|
|
index -= b2 * texelsInBatchN;
|
|
|
|
int b = index / texelsInBatch;
|
|
index -= b * texelsInBatch;
|
|
|
|
int r = 2 * (index / texelsInLogicalRow);
|
|
int c = imod(index, texelsInLogicalRow) * 2;
|
|
|
|
return ivec4(b2, b, r, c);
|
|
}
|
|
`;let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[e.length-1]/2),a=r*Math.ceil(e[e.length-2]/2),o=a,i="",l="b, r, c";for(let c=2;c<e.length-1;c++)o*=e[e.length-c-1],i=`
|
|
int b${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 CY(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;
|
|
${Fm(["r","c","d","d2"],e)}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`;let s=Nl(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${s}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function TY(e,t){let n=Nl(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function NY(e,t){let n=Nl(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function EY(e,t,n){let s=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(v.arraysEqual(e,t))return n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 packedTexShape = ivec2(ceil(float(outTexShape[0]) / 2.0), ceil(float(outTexShape[1]) / 2.0));
|
|
return 2 * ivec2(resultUV.yx * vec2(packedTexShape[0], packedTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${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, ${r}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function RY(e,t,n){return v.arraysEqual(e,t)?n?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(outTexShape[0], outTexShape[1]));
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:e[1]===1?n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:e[0]===1?n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:n?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(outTexShape[0], outTexShape[1]));
|
|
int index = resTexRC.x * outTexShape[1] + resTexRC.y;
|
|
int r = index / outShape[1];
|
|
int c = index - r * outShape[1];
|
|
return ivec2(r, c);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${e[1]};
|
|
int c = index - r * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function El(e){return`offset${e}`}function $Y(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),s=Gn();return`
|
|
vec4 ${n}() {
|
|
return ${s.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function _Y(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`float ${s}() {return ${n};}`;let[r,a]=e.shapeInfo.texShape;if(r===1&&a===1)return`
|
|
float ${s}() {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let o=El(n);if(t)return`
|
|
float ${s}() {
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], ${o});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let[i,l]=e.shapeInfo.texShape;return`
|
|
float ${s}() {
|
|
vec2 uv = uvFromFlat(${i}, ${l}, ${o});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function DY(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,a=Gn();if(t)return`
|
|
vec4 ${s}(int index) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom1D(
|
|
packedTexShape[0], packedTexShape[1], index);
|
|
return ${a.texture2D}(${n}, uv);
|
|
}
|
|
`;let o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return`
|
|
vec4 ${s}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${o[0]}, ${o[1]}, index);
|
|
return ${a.texture2D}(${n}, uv);
|
|
}
|
|
`}function PY(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${s}(int index) {
|
|
${oc(e)}
|
|
}
|
|
`;let r=e.shapeInfo.texShape,a=r[0],o=r[1];if(o===1&&a===1)return`
|
|
float ${s}(int index) {
|
|
return sampleTexture(${n}, halfCR);
|
|
}
|
|
`;let i=El(n);return o===1?t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / float(${n}TexShape[0]));
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${a}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:a===1?t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / float(${n}TexShape[1]), 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${o}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:t?`
|
|
float ${s}(int index) {
|
|
vec2 uv = uvFromFlat(${n}TexShape[0], ${n}TexShape[1], index + ${i});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${s}(int index) {
|
|
vec2 uv = uvFromFlat(${a}, ${o}, index + ${i});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function FY(e,t){let n=e.shapeInfo.logicalShape,s=e.name,r="get"+s.charAt(0).toUpperCase()+s.slice(1),a=e.shapeInfo.texShape,o=a[0],i=a[1],l=Gn();if(a!=null&&v.arraysEqual(n,a))return t?`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`:`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${o}.0);
|
|
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`;if(t)return`
|
|
vec4 ${r}(int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${s}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom2D(valuesPerRow, packedTexShape[0], packedTexShape[1], row, col);
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`;let c=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],u=Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${u}, ${c[0]}, ${c[1]}, row, col);
|
|
return ${l.texture2D}(${s}, uv);
|
|
}
|
|
`}function OY(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=ic(e,l),h=["row","col"];return`
|
|
${ac(p,t)}
|
|
float ${r}(int row, int col) {
|
|
return ${r}(${lc(h,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${r}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${n[1]}, 1)));
|
|
${oc(e)}
|
|
}
|
|
`;let c=a[0],u=a[1],d=El(s);return u===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / float(${s}TexShape[0]));
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${c}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:c===1?t?`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${s}Shape[1], 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / float(${s}TexShape[1]), 0.5);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${n[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:t?`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${s}Shape[1] + col + ${d};
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${n[1]} + col + ${d};
|
|
vec2 uv = uvFromFlat(${c}, ${u}, index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function MY(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=ic(e,p),m=["b","row","col"];return`
|
|
${TI(f,t)}
|
|
vec4 ${r}(int b, int row, int col) {
|
|
return ${r}(${lc(m,h)});
|
|
}
|
|
`}let i=Gn();if(t)return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
ivec2 packedTexShape = ivec2(ceil(float(${s}TexShape[0]) / 2.0), ceil(float(${s}TexShape[1]) / 2.0));
|
|
int valuesPerRow = int(ceil(float(${s}Shape[2]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${s}Shape[1]) / 2.0));
|
|
vec2 uv = packedUVfrom3D(
|
|
packedTexShape[0], packedTexShape[1], texelsInBatch, valuesPerRow, b, row, col);
|
|
return ${i.texture2D}(${s}, uv);
|
|
}
|
|
`;let l=o[0],c=o[1],u=Math.ceil(n[2]/2),d=u*Math.ceil(n[1]/2);return`
|
|
vec4 ${r}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${l}, ${c}, ${d}, ${u}, b, row, col);
|
|
return ${i.texture2D}(${s}, uv);
|
|
}
|
|
`}function zY(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=ic(e,c),g=["row","col","depth"];return`
|
|
${ac(m,t)}
|
|
float ${r}(int row, int col, int depth) {
|
|
return ${r}(${lc(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)));
|
|
${oc(e)}
|
|
}
|
|
`;let u=e.shapeInfo.texShape,d=u[0],p=u[1],h=e.shapeInfo.flatOffset;if(p===a&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
int stride1 = ${s}Shape[2];
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(stride1, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${o}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;if(p===o&&h==null)return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${s}Shape[1], 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}TexShape[1], ${s}TexShape[0]);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${n[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`;let f=El(s);return t?`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int stride0 = ${s}Shape[1] * ${s}Shape[2];
|
|
int stride1 = ${s}Shape[2];
|
|
int index = row * ${a} + col * ${o} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`:`
|
|
float ${r}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a} + col * ${o} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${d}, ${p}, index);
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function LY(e,t){let n=e.name,s="get"+n.charAt(0).toUpperCase()+n.slice(1),r=Gn();if(t)return`
|
|
vec4 ${s}(int b2, int b, int row, int col) {
|
|
int valuesPerRow = int(ceil(float(${n}Shape[3]) / 2.0));
|
|
int texelsInBatch = valuesPerRow * int(ceil(float(${n}Shape[2]) / 2.0));
|
|
int index = b * texelsInBatch + (row / 2) * valuesPerRow + (col / 2);
|
|
texelsInBatch *= ${n}Shape[1];
|
|
index = b2 * texelsInBatch + index;
|
|
ivec2 packedTexShape = ivec2(ceil(float(${n}TexShape[0]) / 2.0), ceil(float(${n}TexShape[1]) / 2.0));
|
|
int texR = index / packedTexShape[1];
|
|
int texC = index - texR * packedTexShape[1];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(packedTexShape[1], packedTexShape[0]); return ${r.texture2D}(${n}, uv);
|
|
}
|
|
`;let a=e.shapeInfo.logicalShape,o=a.length,i=e.shapeInfo.texShape,l=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],c=l[0],u=l[1],d=Math.ceil(a[o-1]/2),p=d*Math.ceil(a[o-2]/2),h="int b, int row, int col",f=`b * ${p} + (row / 2) * ${d} + (col / 2)`;for(let m=2;m<o-1;m++)h=`int b${m}, `+h,p*=a[o-m-1],f=`b${m} * ${p} + `+f;return`
|
|
vec4 ${s}(${h}) {
|
|
int index = ${f};
|
|
int texR = index / ${u};
|
|
int texC = index - texR * ${u};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${c});
|
|
return ${r.texture2D}(${n}, uv);
|
|
}
|
|
`}function BY(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=ic(e,l),y=["row","col","depth","depth2"];return`
|
|
${ac(x,t)}
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
return ${r}(${lc(y,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)));
|
|
${oc(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 A=El(s);return t?`
|
|
float ${r}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
${f}
|
|
${m}
|
|
${g}
|
|
int index = row * stride0 + col * stride1 +
|
|
depth * stride2 + depth2;
|
|
vec2 uv = uvFromFlat(${s}TexShape[0], ${s}TexShape[1], index + ${A});
|
|
return sampleTexture(${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 + ${A});
|
|
return sampleTexture(${s}, uv);
|
|
}
|
|
`}function WY(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=ic(e,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${ac(m)}
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${s}(${lc(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;
|
|
${oc(e)}
|
|
}
|
|
`;let u=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],h=d[1];if(h===i&&u==null)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${o}, ${a}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===r&&u==null)return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]},
|
|
${t[2]*t[3]}, ${t[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=El(n);return`
|
|
float ${s}(int row, int col, int depth, int depth2, int depth3) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${o} + depth * ${a} +
|
|
depth2 * ${r} + depth3 + ${f};
|
|
vec2 uv = uvFromFlat(${p}, ${h}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function VY(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=ic(e,r),A=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${ac(g)}
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${s}(${lc(A,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)));
|
|
${oc(e)}
|
|
}
|
|
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,h=p[0],f=p[1];if(f===u&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${c}, ${l}, ${i}, ${o})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(f===o&&d==null)return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=El(n);return`
|
|
float ${s}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${u} + col * ${c} + depth * ${l} +
|
|
depth2 * ${i} + depth3 * ${o} + depth4 + ${m};
|
|
vec2 uv = uvFromFlat(${h}, ${f}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function oc(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 ${t}[i];
|
|
}
|
|
}
|
|
`}function UY(e,t){let n=e.name,s=n.charAt(0).toUpperCase()+n.slice(1),r="get"+s+"AtOutCoords",a=e.shapeInfo.logicalShape.length,o=t.logicalShape.length,i=CI(e.shapeInfo.logicalShape,t.logicalShape),l=yt(o),c=o-a,u,d=["x","y","z","w","u","v"];a===0?u="":o<2&&i.length>=1?u="coords = 0;":u=i.map(x=>`coords.${d[x+c]} = 0;`).join(`
|
|
`);let p="";o<2&&a>0?p="coords":p=e.shapeInfo.logicalShape.map((x,y)=>`coords.${d[y+c]}`).join(", ");let h="return outputValue;",m=v.sizeFromShape(e.shapeInfo.logicalShape)===1,A=v.sizeFromShape(t.logicalShape)===1;if(a===1&&!m&&!A)h=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(m&&!A)o===1?h=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:h=`
|
|
return vec4(outputValue.x);
|
|
`;else if(i.length){let x=a-2,y=a-1;i.indexOf(x)>-1&&i.indexOf(y)>-1?h="return vec4(outputValue.x);":i.indexOf(x)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${r}() {
|
|
${l} coords = getOutputCoords();
|
|
${u}
|
|
vec4 outputValue = get${s}(${p});
|
|
${h}
|
|
}
|
|
`}function GY(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=yt(l),u=CI(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 yt(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 ${e} is not yet supported`)}function sx(e,t,n){let{newShape:s,keptDims:r}=v.squeezeShape(t),a=t.length,o=e&&a===3&&t[0]===1,i=o?t.slice(1):s,l=!e&&a>1&&!v.arraysEqual(t,n)&&s.length<a||o;return{useSqueezeShape:l,uniformShape:l?i:t,keptDims:r}}function ic(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function lc(e,t){return t.map(n=>e[n]).join(", ")}function HY(e,t,n,s){let r=n.map((b,w)=>{let k={logicalShape:b.shape,texShape:b.isUniform?null:b.texData.texShape,isUniform:b.isUniform,isPacked:b.isUniform?!1:b.texData.isPacked,flatOffset:null};return b.texData!=null&&b.texData.slice!=null&&b.texData.slice.flatOffset>0&&(k.flatOffset=b.texData.slice.flatOffset),{name:t.variableNames[w],shapeInfo:k}}),a=r.map(b=>b.shapeInfo),o={logicalShape:s.shape,texShape:s.texData.texShape,isUniform:!1,isPacked:s.texData.isPacked,flatOffset:null},i=uY(r,o,t),l=rI(e.gl,i),c=e.createProgram(l),u=null,d=e.getUniformLocation(c,"NAN",!1);K().getNumber("WEBGL_VERSION")===1&&(u=e.getUniformLocation(c,"INFINITY",!1));let p=!1,h={},f={},m={};for(let b=0;b<t.variableNames.length;b++){let w=t.variableNames[b];h[w]=e.getUniformLocation(c,w,p),h[`offset${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,A,x;t.enableShapeUniforms&&(g=e.getUniformLocation(c,"outShape",p),x=e.getUniformLocation(c,"outShapeStrides",p),A=e.getUniformLocation(c,"outTexShape",p));let y=[];return t.customUniforms&&t.customUniforms.forEach((b,w)=>{y[w]=e.getUniformLocation(c,b.name,p)}),{program:t,fragmentShader:l,source:i,webGLProgram:c,uniformLocations:h,customUniformLocations:y,inShapeInfos:a,outShapeInfo:o,infLoc:u,nanLoc:d,inShapesLocations:f,inTexShapesLocations:m,outShapeLocation:g,outShapeStridesLocation:x,outTexShapeLocation:A}}function EI(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 ${l} must match`)})}function jY(e,t,n,s,r){t.program.enableShapeUniforms||(EI(t.inShapeInfos,n),EI([t.outShapeInfo],[s]));let a=s.texData.texture,o=s.texData.texShape;s.texData.isPacked?e.setOutputPackedMatrixTexture(a,o[0],o[1]):e.setOutputMatrixTexture(a,o[0],o[1]),e.setProgram(t.webGLProgram),K().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,1/0),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((l,c)=>{let u=t.program.variableNames[c],d=t.uniformLocations[u],p=t.uniformLocations[`offset${u}`],h=t.inShapesLocations[`${u}Shape`],f=t.inTexShapesLocations[`${u}TexShape`];if(h){let{uniformShape:m}=sx(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 ${l.type} is not supported yet.`)}),e.executeProgram()}function qY(e,t,n){let s="";t.concat(n).forEach(o=>{let i=o.texData!=null&&o.texData.slice!=null&&o.texData.slice.flatOffset>0;if(e.enableShapeUniforms&&!o.isUniform){let l=o.texData.texShape,{useSqueezeShape:c,uniformShape:u,keptDims:d}=sx(e.packedInputs,o.shape,l),p="",h="",f="";if(u.length===1&&e.packedInputs){let w=[Math.ceil(l[0]/2),Math.ceil(l[1]/2)];p=`${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]}_${w[w.length-1]===l[1]}`}let m=o.shape.length,g=u.length===2&&v.arraysEqual(o.shape,l),A=v.sizeFromShape(o.shape)===1,x=E.getBroadcastDims(o.shape,n.shape),y=!e.packedInputs&&m===n.shape.length&&v.arraysEqual(l,n.texData.texShape),b=e.packedInputs||u.length>2?"":`${l[0]>1}_${l[1]>1}`;s+=`${m}_${y}_${c?d:""}_${u.length}_${A}_${x}_${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+`${K().getNumber("WEBGL_VERSION")}`,a}function Os(e){return K().getBool("WEBGL_USE_SHAPES_UNIFORMS")&&e<=4}var XY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=ip.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gn();this.outputShape=e,this.enableShapeUniforms=Os(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Fm(["r","c","d"],e):Nl(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getA(rc.x, rc.y, rc.z);
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},KY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=ip.DENSE,this.customUniforms=[{name:"texShape",type:"ivec2"}];let t=Gn();this.outputShape=e,this.enableShapeUniforms=Os(this.outputShape.length),this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${this.enableShapeUniforms?Fm(["r","c","d"],e):Nl(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(texShape[0], texShape[1]));
|
|
int index = 4 * (resTexRC.x * texShape[1] + resTexRC.y);
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for (int i=0; i<4; i++) {
|
|
int flatIndex = index + i;
|
|
ivec3 rc = outCoordsFromFlatIndex(flatIndex);
|
|
result[i] = getChannel(getA(rc.x, rc.y, rc.z), vec2(rc.y, rc.z));
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},ZY=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Ps.DOWNLOAD;let t=Gn();this.outputShape=e,this.userCode=`
|
|
${II}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},YY=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Ps.DOWNLOAD;let t=Gn();this.outputShape=e,this.userCode=`
|
|
${II}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},JY=class{constructor(e,t=!1){this.variableNames=["A"],this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Gn();this.outputShape=e,this.enableShapeUniforms=Os(this.outputShape.length);let s="result";t&&(s="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${this.enableShapeUniforms?nx():tx(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.);
|
|
}
|
|
`}},QY=class{constructor(e,t=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.customUniforms=[{name:"texShape",type:"ivec2"}];let n=Gn();this.outputShape=e,this.enableShapeUniforms=Os(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?nx():tx(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${s}
|
|
|
|
${n.output} = ${r};
|
|
}
|
|
`}},RI={};Oe(RI,{bindVertexProgramAttributeStreams:()=>LI,createBufferFromOutputTexture:()=>VI,createFloat16MatrixTexture:()=>FI,createFloat16PackedMatrixTexture:()=>zI,createFloat32MatrixTexture:()=>PI,createIndexBuffer:()=>DI,createPackedMatrixTexture:()=>MI,createUnsignedBytesMatrixTexture:()=>OI,createVertexBuffer:()=>_I,createVertexShader:()=>$I,downloadByteEncodedFloatMatrixFromOutputTexture:()=>GI,downloadFloat32MatrixFromBuffer:()=>UI,downloadMatrixFromPackedOutputTexture:()=>jI,downloadPackedMatrixFromBuffer:()=>HI,getInternalFormatForFloat16MatrixTexture:()=>ax,getInternalFormatForFloat16PackedMatrixTexture:()=>lx,getInternalFormatForFloat32MatrixTexture:()=>rx,getInternalFormatForPackedMatrixTexture:()=>ix,getInternalFormatForUnsignedBytesMatrixTexture:()=>ox,uploadDenseMatrixToTexture:()=>BI,uploadPixelDataToTexture:()=>WI});function $I(e){let t=Gn(),n=`${t.version}
|
|
precision highp float;
|
|
${t.attribute} vec3 clipSpacePos;
|
|
${t.attribute} vec2 uv;
|
|
${t.varyingVs} vec2 resultUV;
|
|
|
|
void main() {
|
|
gl_Position = vec4(clipSpacePos, 1);
|
|
resultUV = uv;
|
|
}`;return sI(e,n)}function _I(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 iI(e,t)}function DI(e){let t=new Uint16Array([0,1,2,2,1,3]);return lI(e,t)}function pp(e,t,n,s,r,a){cI(t,n);let o=uI(e),i=e.TEXTURE_2D;return Ie(e,()=>e.bindTexture(i,o)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MIN_FILTER,e.NEAREST)),Ie(e,()=>e.texParameteri(i,e.TEXTURE_MAG_FILTER,e.NEAREST)),Ie(e,()=>e.texImage2D(i,0,s,t,n,0,r,a,null)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null)),o}function rx(e){return e.internalFormatFloat}function PI(e,t,n,s){let[r,a]=lp(t,n);return pp(e,r,a,rx(s),s.textureFormatFloat,e.FLOAT)}function ax(e){return e.internalFormatHalfFloat}function FI(e,t,n,s){let[r,a]=lp(t,n);return pp(e,r,a,ax(s),s.textureFormatFloat,s.textureTypeHalfFloat)}function ox(e){return e.downloadTextureFormat}function OI(e,t,n,s){let[r,a]=lp(t,n);return pp(e,r,a,ox(s),e.RGBA,e.UNSIGNED_BYTE)}function ix(e){return e.internalFormatPackedFloat}function MI(e,t,n,s){let[r,a]=sc(t,n);return pp(e,r,a,ix(s),e.RGBA,e.FLOAT)}function lx(e){return e.internalFormatPackedHalfFloat}function zI(e,t,n,s){let[r,a]=sc(t,n);return pp(e,r,a,lx(s),e.RGBA,s.textureTypeHalfFloat)}function LI(e,t,n){let s=0,r=3*4,a=3*4+2*4;return Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),Yy(e,t,"clipSpacePos",n,3,a,s)&&Yy(e,t,"uv",n,2,a,r)}function BI(e,t,n,s,r,a){Ie(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),Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,l,n,s,0,e.RGBA,i,o)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function WI(e,t,n){Ie(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):Ie(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),Ie(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function VI(e,t,n,s){let r=e.createBuffer();Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let i=4*4*t*n;return Ie(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,i,e.STREAM_READ)),Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),Ie(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function UI(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 GI(e,t,n,s){let[r,a]=lp(t,n),o=4,i=new Uint8Array(XZ(t*n,o));return Ie(e,()=>e.readPixels(0,0,r,a,s.downloadTextureFormat,e.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function HI(e,t,n,s,r,a,o,i){let l=e,c=new Float32Array(KZ(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 jI(e,t,n){let s=new Float32Array(t*n*4);return Ie(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,s)),s}var Om=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=K().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Tm(t,e)):this.gl=Lr(t);let n="WEBGL_color_buffer_float",s="EXT_color_buffer_half_float";if(K().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=up(this.gl,r),Fs(this.gl,a))this.textureHalfFloatExtension=up(this.gl,a);else if(K().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),Fs(this.gl,s))this.colorBufferHalfFloatExtension=up(this.gl,s);else if(K().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",Fs(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(Fs(this.gl,s))this.colorBufferHalfFloatExtension=this.gl.getExtension(s);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=_I(this.gl),this.indexBuffer=DI(this.gl),this.framebuffer=dI(this.gl),this.textureConfig=Zy(this.gl,this.textureHalfFloatExtension)}get debug(){return K().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;Ie(e,()=>e.finish()),Ie(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ie(e,()=>e.deleteFramebuffer(this.framebuffer)),Ie(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ie(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ie(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),PI(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),FI(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),OI(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),WI(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,s){this.throwIfDisposed(),BI(this.gl,e,t,n,s,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),zI(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),MI(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(Jy(this.gl,this.framebuffer),this.outputTexture=null),Ie(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>GI(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,s,r,a){return HI(this.gl,e,t,n,s,r,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return UI(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let s=VI(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(K().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 K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>jI(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl;this.vertexShader==null&&(this.vertexShader=$I(t));let n=aI(t);return Ie(t,()=>t.attachShader(n,this.vertexShader)),Ie(t,()=>t.attachShader(n,e)),oI(t,n),this.debug&&Em(t,n),this.vertexAttrsAreBound||(this.setProgram(n),this.vertexAttrsAreBound=LI(t,this.program,this.vertexBuffer)),n}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ie(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&Em(this.gl,this.program),Ie(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?hI(this.gl,e,t):fI(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ie(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(),mI(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[s,r]=sc(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&&Em(this.gl,this.program),cp(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ie(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ie(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=up(this.gl,K().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(K().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(K().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,K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,K().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=eJ(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(),Rm(this.gl,e,this.framebuffer),this.debug&&cp(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Rm(this.gl,this.outputTexture,this.framebuffer),this.debug&&cp(this.gl)):Jy(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;Rm(s,e,this.framebuffer),this.debug&&cp(s),this.outputTexture=e,Ie(s,()=>s.viewport(0,0,t,n)),Ie(s,()=>s.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,s){this.throwIfDisposed(),Ie(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 eJ(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{addImpl:tJ,bincountImpl:qI,bincountReduceImpl:nJ,ceilImpl:sJ,concatImpl:rJ,equalImpl:aJ,expImpl:oJ,expm1Impl:iJ,floorImpl:lJ,gatherNdImpl:uJ,gatherV2Impl:cJ,greaterImpl:dJ,greaterEqualImpl:pJ,lessImpl:hJ,lessEqualImpl:fJ,linSpaceImpl:mJ,logImpl:gJ,maxImpl:AJ,maximumImpl:yJ,minimumImpl:xJ,multiplyImpl:bJ,negImpl:vJ,notEqualImpl:wJ,prodImpl:kJ,rangeImpl:SJ,rsqrtImpl:IJ,sigmoidImpl:CJ,simpleAbsImpl:XI,sliceImpl:TJ,sparseFillEmptyRowsImpl:NJ,sparseReshapeImpl:EJ,sparseSegmentReductionImpl:KI,sqrtImpl:RJ,stridedSliceImpl:$J,stringNGramsImpl:_J,stringSplitImpl:DJ,stringToHashBucketFastImpl:PJ,subImpl:FJ,tileImpl:OJ,topKImpl:MJ,transposeImpl:ux,uniqueImpl:zJ}=vm;function ZI(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function Hn(e,t){return t===1?[e]:ZI(e,t)}function LJ(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 BJ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
|
|
void main() {
|
|
setOutput(vec4(getA(), 0., 0., 0.));
|
|
}
|
|
`;else{let n=Hn("rc",t),s=yt(t),r=VJ(t,e,n),a=UJ(t,e[e.length-1],e[e.length-2],n),o=GJ(e,n);this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
|
|
if(${r}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${a}
|
|
|
|
setOutput(vec4(${o}));
|
|
}
|
|
}
|
|
`}}};function WJ(e,t){let n=[];for(let s=0;s<=1;s++)for(let r=0;r<=1;r++){let a=`${s===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let o=2;o<e;o++)a=`${t[t.length-1-o]},`+a;n.push(a)}return n}function VJ(e,t,n){if(e===1)return`rc > ${t[0]}`;let s="";for(let r=e-2;r<e;r++)s+=`${n[r]} >= ${t[r]}`,r<e-1&&(s+="||");return s}function UJ(e,t,n,s){if(e===1)return"";let r=s.slice(-2);return`
|
|
int r = ${r[0]};
|
|
int c = ${r[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${t};
|
|
bool rEdge = rp1 >= ${n};
|
|
`}function GJ(e,t){let n=e.length,s=WJ(n,t);return n===1?`getA(rc),
|
|
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${s[0]}),
|
|
cEdge ? 0. : getA(${s[1]}),
|
|
rEdge ? 0. : getA(${s[2]}),
|
|
rEdge || cEdge ? 0. : getA(${s[3]})`}var YI=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"inputShape",type:"ivec3"}],this.outputShape=e,this.enableShapeUniforms=Os(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=`
|
|
${HJ(t,this.enableShapeUniforms)}
|
|
${this.enableShapeUniforms?nx():tx(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 HJ(e,t){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${t?lY(["r","c","d"],"inputShape"):Nl(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var jJ=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let s=QI(t,n),r=e4(e,s,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let a=JI(e,s,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let i=this.freeTextures[r].shift();return this.usedTextures[r].push(i),i}let o;return s===Cn.PACKED_2X2_FLOAT32?o=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):s===Cn.PACKED_2X2_FLOAT16?o=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):s===Cn.UNPACKED_FLOAT32?o=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):s===Cn.UNPACKED_FLOAT16?o=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):s===Cn.PACKED_4X1_UNSIGNED_BYTE&&(o=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(o),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),o}releaseTexture(e,t,n,s){if(this.freeTextures==null)return;let r=QI(n,s),a=e4(t,r,s);a in this.freeTextures||(this.freeTextures[a]=[]);let o=JI(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,s),i=K().get("WEBGL_DELETE_TEXTURE_THRESHOLD");i!==-1&&this._numBytesAllocated>i?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=o):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=o),this.numUsedTextures--;let l=this.usedTextures[a],c=l.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");l.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 qJ(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;throw new Error(`Unknown internal format ${t}`)}function JI(e,t,n,s,r){let a=XJ(t,s),o;if(r){let[l,c]=sc(e[0],e[1]);o=l*c}else{let[l,c]=lp(e[0],e[1]);o=l*c}let i=qJ(n,a);return o*i}function XJ(e,t){switch(e){case Cn.PACKED_2X2_FLOAT32:return ix(t);case Cn.PACKED_2X2_FLOAT16:return lx(t);case Cn.UNPACKED_FLOAT32:return rx(t);case Cn.UNPACKED_FLOAT16:return ax(t);case Cn.PACKED_4X1_UNSIGNED_BYTE:return ox(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function KJ(e){return K().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?Cn.PACKED_2X2_FLOAT32:Cn.UNPACKED_FLOAT32:e?Cn.PACKED_2X2_FLOAT16:Cn.UNPACKED_FLOAT16}function QI(e,t){if(e===Ps.UPLOAD)return Cn.PACKED_2X2_FLOAT32;if(e===Ps.RENDER||e==null)return KJ(t);if(e===Ps.DOWNLOAD||e===Ps.PIXELS)return Cn.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function e4(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Uo=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.enableShapeUniforms=Os(this.outputShape.length),this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},yr="if (isnan(x)) return x;",ZJ="return x;",t4="return abs(x);",YJ="return (x >= 0.0) ? x : (exp(x) - 1.0);",JJ=yr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,QJ=yr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Mm="return x;",eQ="return 1.0 / (1.0 + exp(-1.0 * x));",tQ="return x;",nQ=`
|
|
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;
|
|
`,sQ=`
|
|
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;
|
|
`,rQ=`
|
|
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;
|
|
`,aQ="return 1.0 / (1.0 + exp(-1.0 * x));",uc=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.enableShapeUniforms=Os(this.outputShape.length),this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},oQ=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=Hn("rc",t),s=yt(t),r=LJ(t,n),a=n.slice(-2),o=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${s} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${o}));
|
|
}
|
|
`}},iQ=Ks.whereImpl,lQ=1e-7,uQ=1e-4,zm={};function cQ(e){return e in zm||(zm[e]={}),zm[e]}var dQ=K().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),pQ=600;function hQ(){return K().global.screen==null?1024:K().global.screen.height*K().global.screen.width*window.devicePixelRatio*pQ/1024/1024}var n4=class extends eu{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,!K().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Lr(K().getNumber("WEBGL_VERSION"));this.binaryCache=cQ(K().getNumber("WEBGL_VERSION")),this.gpgpu=new Om(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 jJ(this.gpgpu),this.numMBBeforeWarning=hQ(),this.texData=new Zc(this,ss())}nextDataId(){return n4.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((K().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||K().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:Ps.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(K().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:Ps.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 uc(o,Mm):d=new Uo(o,Mm);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=E.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 uc(s,Mm):h=new Uo(s,Mm);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(K().getBool("DEBUG")&&!K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&K().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"&&K().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let h=this.texData.get(c.dataId);l=this.gpgpu.createBufferFromTexture(h.texture,...Nm(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=E.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;Ie(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)&&ss().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(!tI(n))throw K().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 ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:s}=this.texData.get(e),r=v.sizeFromShape(t);if(K().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let d=this.decode(e),p=this.texData.get(d.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(p.texture,...Nm(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(d),h}let a=K().getBool("WEBGL_PACK")&&s===!0,o=a?$m(t):t,i=a?new YY(o):new ZY(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 K().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(K().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 K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:v.now(),endMs:null}}endTimer(e){return K().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=v.now(),e)}async getQueryTime(e){if(K().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=dQ){return K().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){E.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return iQ(e.shape,t)}packedUnaryOp(e,t,n){let s=new uc(e.shape,t),r=this.compileAndRun(s,[e],n);return ss().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let s=XI(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,s)}if(K().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,t4,e.dtype);let t=new Uo(e.shape,t4),n=this.compileAndRun(t,[e]);return ss().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 ss().makeTensorFromDataId(s,e,t,this)}unpackTensor(e){let t=new oQ(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new BJ(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Cl(e.shape),...Tl(e.shape)],s={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Cl(t),...Tl(t)],a=new YI(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=$m(s),o,i=Nm(a);n?o=new KY(a):o=new XY(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===ip.DENSE){let m=Nm(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)<=K().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)}else if(!!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&&!dp(g.shape,m.shape)){let A=m,x=m.shape;m.shape=g.shape,m=this.packedReshape(m,x),i.push(m),g=this.texData.get(m.dataId),A.shape=x}return this.uploadToGPU(m.dataId),{shape:m.shape,texData:g,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:o,isUniform:!1},u=qY(e,l,c),d=this.getAndSaveBinary(u,()=>HY(this.gpgpu,e,l,c)),p=this.activeTimers!=null,h;p&&(h=this.startTimer()),jY(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=K().get("WEBGL_FLUSH_THRESHOLD");if(f>0){let m=v.now();m-this.lastGlFlushTime>f&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=m)}if(!K().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||(K().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=q(()=>{if(!K().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=K().getBool("DEBUG");K().set("DEBUG",!1);let t=this.abs(Re(1e-8)).dataSync()[0];if(K().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?lQ:uQ}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=yI(n,i),t.texShape=u),r!=null){let d=$m(n),p,h=u[1],f=u[0],m=r instanceof Uint8Array||r instanceof Uint8ClampedArray;i?([h,f]=sc(u[0],u[1]),p=new QY(d,m)):p=new JY(d,m);let g=this.makeTensorInfo([f,h],s);m?this.texData.get(g.dataId).usage=Ps.PIXELS:this.texData.get(g.dataId).usage=Ps.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(g.dataId),h,f,r);let A=[[f,h]],x=!0,y=this.runWebGLProgram(p,[g],s,A,x),b=this.texData.get(y.dataId);t.texture=b.texture,t.texShape=b.texShape,t.isPacked=b.isPacked,t.usage=b.usage,this.disposeIntermediateTensorInfo(g),this.texData.delete(y.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=fQ(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)}},hp=n4;hp.nextDataId=0;function fQ(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 ${t}`)}var mQ="0.0.0";function s4(){K().set("WEBGL_FORCE_F16_TEXTURES",!0)}$u.isBrowser()&&ol("webgl",()=>new hp,2);var gQ={forceHalfFloat:s4},r4=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,cc=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.enableShapeUniforms=Os(this.outputShape.length),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},Lm=`
|
|
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;
|
|
`,fp=class{constructor(e,t,n,s=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length;this.enableShapeUniforms=Os(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=`
|
|
${yt(r)} coords = getOutputCoords();
|
|
`,r===1)this.enableShapeUniforms?a+=`
|
|
result.y = (coords + 1) >= outShape ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`:a+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=Hn("coords",r);this.enableShapeUniforms?a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= outShape[${r} - 2];
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= outShape[${r} - 1];
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`:a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= ${this.outputShape[r-1]};
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`}this.userCode=`
|
|
vec4 binaryOperation(vec4 a, vec4 b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
vec4 a = getAAtOutCoords();
|
|
vec4 b = getBAtOutCoords();
|
|
|
|
vec4 result = binaryOperation(a, b);
|
|
${a}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function ys(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 AQ={kernelName:Ha,backendName:"webgl",kernelFunc:ys};function Go(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=ys({inputs:{x:s},backend:n}),l=ys({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var yQ={kernelName:ed,backendName:"webgl",kernelFunc:Go},a4="return (a < 0.) ? b * a : a;",o4=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function xQ(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{alpha:a}=s,o=n.makeTensorInfo([],"float32",v.createScalarValue(a,"float32")),i=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new fp(o4,r.shape,o.shape):new cc(a4,r.shape,o.shape),l=n.runWebGLProgram(i,[r,o],"float32");return n.disposeIntermediateTensorInfo(o),l}var bQ={kernelName:Si,backendName:"webgl",kernelFunc:xQ},i4="return (a < 0.) ? b * a : a;",l4=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function vQ(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new fp(l4,s.shape,r.shape):new cc(i4,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],"float32")}var wQ={kernelName:so,backendName:"webgl",kernelFunc:vQ},u4="if (isnan(x)) return x;",kQ=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,SQ=`
|
|
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 st({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=K().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,u;return c?u=new uc(o.shape,t):u=new Uo(o.shape,e),i.runWebGLProgram(u,[o],l)}}function Tn({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,A]=[[f.complexTensorInfos.real,m.complexTensorInfos.real],[f.complexTensorInfos.imag,m.complexTensorInfos.imag]].map(y=>{let[b,w]=y,k={dataId:b.dataId,dtype:b.dtype,shape:l.shape},C={dataId:w.dataId,dtype:w.dtype,shape:c.shape},N=new cc(e,l.shape,c.shape);return u.runWebGLProgram(N,[k,C],Ln(b.dtype,w.dtype))}),x=Go({inputs:{real:g,imag:A},backend:u});return u.disposeIntermediateTensorInfo(g),u.disposeIntermediateTensorInfo(A),x}let d=a||Ln(l.dtype,c.dtype);if((l.dtype==="string"||c.dtype==="string"||u.shouldExecuteOnCPU([l,c]))&&r!=null){let f=u.texData.get(l.dataId).values,m=u.texData.get(c.dataId).values,g=l.dtype==="string"?E.fromUint8ToStringArray(f):f,A=l.dtype==="string"?E.fromUint8ToStringArray(m):m,[x,y]=r(l.shape,c.shape,g,A,d),b=u.makeTensorInfo(y,d),w=u.texData.get(b.dataId);return w.values=x,b}let p=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return p?h=new fp(t,l.shape,c.shape,n):h=new cc(e,l.shape,c.shape),u.runWebGLProgram(h,[l,c],d)}}function Bm(e,t=!1){if(e==="linear")return t?tQ:ZJ;if(e==="relu")return t?sQ:JJ;if(e==="elu")return t?nQ:YJ;if(e==="relu6")return t?rQ:QJ;if(e==="prelu")return t?l4:i4;if(e==="leakyrelu")return t?o4:a4;if(e==="sigmoid")return t?aQ:eQ;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var c4=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=Os(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 A=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),l&&this.variableNames.push("leakyreluAlpha");let x="rc.x",y="rc.x";e[0]<t[0]?x=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(y=`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 = ${y};
|
|
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);
|
|
|
|
${A}
|
|
|
|
${g}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},d4={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},p4=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},h4="return a * b;";function cx(e){let{inputs:t,backend:n}=e,{a:s,b:r}=t,a=E.upcastType(s.dtype,r.dtype);if(s.dtype==="complex64"){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),c=new p4(d4.REAL,s.shape,r.shape),u=new p4(d4.IMAG,s.shape,r.shape),d=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:s.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:s.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:r.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:r.shape}],p=n.runWebGLProgram(c,d,"float32"),h=n.runWebGLProgram(u,d,"float32"),f=Go({inputs:{real:p,imag:h},backend:n});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),f}if(n.shouldExecuteOnCPU([s,r])){let i=n.texData.get(s.dataId),l=n.texData.get(r.dataId),[c,u]=bJ(s.shape,r.shape,i.values,l.values,a),d=n.makeTensorInfo(u,a),p=n.texData.get(d.dataId);return p.values=c,d}let o;return K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?o=new fp(h4,s.shape,r.shape):o=new cc(h4,s.shape,r.shape),n.runWebGLProgram(o,[s,r],a)}var IQ={kernelName:eo,backendName:"webgl",kernelFunc:cx};function CQ(e,t,n){let s=[Cl(e.shape),...Tl(e.shape)],r={dtype:e.dtype,shape:s,dataId:e.dataId},a=[Cl(t),...Tl(t)],o=new YI(a,s),i=!0,l=[s],c=n.runWebGLProgram(o,[r],e.dtype,l,i);return{dataId:c.dataId,shape:t,dtype:c.dtype}}function ve(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{shape:a}=s,o=n,i=v.sizeFromShape(r.shape),l=v.inferFromImplicitShape(a,i),c=v.sizeFromShape(l);v.assert(i===c,()=>`The new shape (${l}) has ${c} elements and the old shape (${r.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let u=o.texData.get(r.dataId);return u.isPacked&&!dp(r.shape,l)&&!(u.texture!==null&&dp(u.shape,l))?CQ(r,l,o):(o.incRef(r.dataId),{dataId:r.dataId,shape:l,dtype:r.dtype})}var TQ={kernelName:Oi,backendName:"webgl",kernelFunc:ve},f4=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);
|
|
|
|
${l}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},NQ=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:s,inSize:r,outSize:a}=e;this.outputShape=[s,a];let o="0.0",i="";t==="prod"?o="1.0":t==="min"?(o="1.0 / 1e-20",i="min"):t==="max"&&(o="-1.0 / 1e-20",i="max");let l=`${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 EQ(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=E.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:s,outSize:Math.ceil(n/s)})}return t}function Rl(e,t,n,s){let r=EQ(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 f4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c},i):new f4({windowSize:l,inSize:i,batchSize:e.shape[0],outSize:c}):u=new NQ({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 RQ=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=yt(this.rank),r=$Q(t);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function $Q(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 _Q=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 ${this.rank} is not yet supported.`);let s=yt(this.rank),r=ZI("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=r[c];let o=`vec2(${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] = ${l};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Wm(e,t,n){let s=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new _Q(e.shape,t):new RQ(e.shape,t);return n.runWebGLProgram(s,[e],e.dtype)}function DQ(e,t,n,s){let r=t,a=e.shape.length,o=v.parseAxisParam(r,e.shape),i=o,l=E.getAxesPermutation(i,a),c=l!=null,u=e;c&&(u=Wm(e,l,s),i=E.getInnerMostAxes(i.length,a)),E.assertAxesAreInnerMostDims("sum",i,a);let[d,p]=E.computeOutAndReduceShapes(u.shape,i),h=d;n&&(h=E.expandShapeToKeepDim(d,o));let f=v.sizeFromShape(p),g=v.sizeFromShape(e.shape)/f,A=ve({inputs:{x:u},attrs:{shape:[g,f]},backend:s}),x=wd(e.dtype),y=Rl(A,x,"sum",s),b=ve({inputs:{x:y},attrs:{shape:h},backend:s});return s.disposeIntermediateTensorInfo(A),s.disposeIntermediateTensorInfo(y),c&&s.disposeIntermediateTensorInfo(u),b}function Vm(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return DQ(r,a,o,n)}var PQ={kernelName:po,backendName:"webgl",kernelFunc:Vm};function jn(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{perm:a}=s,o=n,i=r.shape.length,l=new Array(i);for(let u=0;u<l.length;u++)l[u]=r.shape[a[u]];let c;if(o.shouldExecuteOnCPU([r])){let d=o.texData.get(r.dataId).values,p=ux(d,r.shape,r.dtype,a,l);c=o.makeTensorInfo(l,r.dtype);let h=o.texData.get(c.dataId);h.values=p}else c=Wm(r,a,o);return c}var FQ={kernelName:Ao,backendName:"webgl",kernelFunc:jn},m4=1e3;function Um({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=v.sizeFromShape(m),x=v.sizeFromShape(g),b=sl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[A,d,h]:[A,h,d],k=s?[x,f,p]:[x,p,f],C=ve({inputs:{x:e},backend:r,attrs:{shape:w}}),N=ve({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[C,N],F=Math.max(A,x),_=n?C.shape[1]:C.shape[2],P=a!=null,T=o!=null,M=l==="leakyrelu",U=l!=null?Bm(l,!0):null,j=P||T||M||U!=null,z;if((h===1||f===1)&&_>m4&&j===!1){let Z=C,J=N;n&&(Z=jn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),R.push(Z)),s&&(J=jn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),R.push(J));let ee=f!==1,re=f===1,Q=Z;ee&&(Q=ve({inputs:{x:Z},backend:r,attrs:{shape:[F,_,1]}}),R.push(Q));let te=f===1?2:1,oe=J;re&&(oe=ve({inputs:{x:J},backend:r,attrs:{shape:[F,1,_]}}),R.push(oe));let fe=cx({inputs:{a:Q,b:oe},backend:r});z=Vm({inputs:{x:fe},backend:r,attrs:{axis:te,keepDims:!0}}),R.push(fe)}else{let Z=Ln(e.dtype,t.dtype),J=new c4(w,k,[F,h,f],n,s,P,U,T,M),ee=[C,N];if(a!=null&&ee.push(a),T&&ee.push(o),M){let re=r.makeTensorInfo([],"float32",v.createScalarValue(i,"float32"));ee.push(re),R.push(re)}z=r.runWebGLProgram(J,ee,Z)}let X=ve({inputs:{x:z},backend:r,attrs:{shape:b}});R.push(z);for(let Z of R)r.disposeIntermediateTensorInfo(Z);return X}function OQ(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 Um({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var MQ={kernelName:xo,backendName:"webgl",kernelFunc:OQ},g4="return abs(x);";function zQ(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=XI(a.values);return n.makeTensorInfo(s.shape,s.dtype,o)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new uc(s.shape,g4):r=new Uo(s.shape,g4),n.runWebGLProgram(r,[s],s.dtype)}var LQ={kernelName:di,backendName:"webgl",kernelFunc:zQ},BQ=yr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,WQ=st({opSnippet:BQ}),VQ={kernelName:su,backendName:"webgl",kernelFunc:WQ},UQ=yr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,GQ=st({opSnippet:UQ}),HQ={kernelName:ru,backendName:"webgl",kernelFunc:GQ},A4="return a + b;",jQ=Tn({opSnippet:A4,packedOpSnippet:A4,supportsComplex:!0,cpuKernelImpl:tJ}),qQ={kernelName:Hr,backendName:"webgl",kernelFunc:jQ},XQ=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 = ${s};
|
|
setOutput(result);
|
|
}
|
|
`}},KQ=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,a)=>`T${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 Gm(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return ys({inputs:{x:s[0]},backend:n});if(s.length>K().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(s.length/2),c=Gm({inputs:s.slice(0,l),backend:n}),u=Gm({inputs:s.slice(l),backend:n});return Gm({inputs:[c,u],backend:n})}let r=s.map(l=>l.dtype).reduce((l,c)=>Ln(l,c)),a=s.map(l=>l.shape),i=K().getBool("WEBGL_PACK")?new KQ(s[0].shape,a):new XQ(s[0].shape,a);return n.runWebGLProgram(i,s,r)}var ZQ={kernelName:Ta,backendName:"webgl",kernelFunc:Gm};function YQ(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=E.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,i)),E.assertAxesAreInnerMostDims("all",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Rl(m,m.dtype,"all",n),A;if(o){let x=E.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var JQ={kernelName:au,backendName:"webgl",kernelFunc:YQ};function QQ(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=E.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,i)),E.assertAxesAreInnerMostDims("any",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Rl(m,m.dtype,"any",n),A;if(o){let x=E.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var eee={kernelName:ou,backendName:"webgl",kernelFunc:QQ},tee=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 ${o} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},nee=class{constructor(e,t,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,v.assert(e.length>2,()=>`Packed arg${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=yt(i),c=Hn("coords",i),u,d;if(a===1){d=i+1;let C=yt(d);u=`
|
|
${C} sourceLocR = ${C}(${c.join()}, 0);
|
|
++${c[i-1]};
|
|
${C} sourceLocG = ${C}(${c.join()}, 0);
|
|
++${c[i-2]};
|
|
${C} sourceLocA = ${C}(${c.join()}, 0);
|
|
--${c[i-1]};
|
|
${C} sourceLocB = ${C}(${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(C=>"int "+C),m=Hn("sourceLocR",d-1).concat("inIdx.r"),g=Hn("sourceLocG",d-1).concat("inIdx.g"),A=Hn("sourceLocB",d-1).concat("inIdx.b"),x=Hn("sourceLocA",d-1).concat("inIdx.a"),y=n==="max"?"greaterThan":"lessThan",b=s?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${m.join()}),
|
|
getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${A.join()}),
|
|
getBestIndicesAChannel(${x.join()})));`,w=`vec4(
|
|
getAChannel(${m.join()}),
|
|
hasNextCol ? getAChannel(${g.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${A.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${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 = ${w};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${y}(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 y4(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=E.computeOptimalWindowSize(a),i={windowSize:o,inSize:a,batchSize:r,outSize:Math.ceil(a/o)},l=new tee(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=y4(e,t,n,u);return e.disposeIntermediateTensorInfo(u),d}function x4(e,t,n,s=null){let r=s!=null?s.shape:t.shape,a=r[r.length-1],o=E.computeOptimalWindowSize(a),i=new nee(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=x4(e,t,n,c);return e.disposeIntermediateTensorInfo(c),u}return c}function b4(e,t,n,s){let r=[n];if(E.assertAxesAreInnerMostDims("arg"+s.charAt(0).toUpperCase()+s.slice(1),r,t.shape.length),!K().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]=E.computeOutAndReduceShapes(l.shape,r),d=v.sizeFromShape(u),p=ve({inputs:{x:l},backend:e,attrs:{shape:[-1,d]}});a.push(p);let h=y4(e,p,s);a.push(h);let f=ve({inputs:{x:h},backend:e,attrs:{shape:c}});return a.forEach(m=>e.disposeIntermediateTensorInfo(m)),f}return x4(e,t,s)}function see(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=jn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=b4(n,l,o[0],"max");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var ree={kernelName:Na,backendName:"webgl",kernelFunc:see};function aee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=jn({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=b4(n,l,o[0],"min");return c.forEach(d=>n.disposeIntermediateTensorInfo(d)),u}var oee={kernelName:iu,backendName:"webgl",kernelFunc:aee},iee=yr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,lee=st({opSnippet:iee}),uee={kernelName:lu,backendName:"webgl",kernelFunc:lee},cee=yr+"return log(x + sqrt(x * x + 1.0));",dee=st({opSnippet:cee}),pee={kernelName:uu,backendName:"webgl",kernelFunc:dee},hee=yr+`
|
|
return atan(x);
|
|
`,fee=st({opSnippet:hee}),mee={kernelName:cu,backendName:"webgl",kernelFunc:fee},gee=kQ+`
|
|
return atan(a, b);
|
|
`,Aee=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+SQ+`
|
|
return result;
|
|
`,yee=Tn({opSnippet:gee,packedOpSnippet:Aee}),xee={kernelName:pu,backendName:"webgl",kernelFunc:yee},bee=yr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,vee=st({opSnippet:bee}),wee={kernelName:du,backendName:"webgl",kernelFunc:vee},mp=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) * ${e.inChannels} + d`,A="0.0";if(f||(A="-1.0 / 1e-20"),n){let C=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${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 ${C} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${s?r?m:g:`wR * ${d} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let x="max",y=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(y="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 = ${A};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${A});
|
|
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
|
|
);
|
|
|
|
${k}
|
|
}
|
|
}
|
|
setOutput(${y});
|
|
}
|
|
`}},dx=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,A=e.padInfo.left;this.outputShape=e.outShape;let x=t==="avg",y="0.0";if(x||(y="-1.0 / 1e-20"),n){let R=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${o}, ${i}, ${l});
|
|
const ivec3 pads = ivec3(${m}, ${g}, ${A});
|
|
|
|
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,C=a%4,N=`
|
|
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}, ${A});
|
|
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 xD, int xR, int xC, int ch) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${y});
|
|
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)
|
|
);
|
|
|
|
${N}
|
|
}
|
|
|
|
int xC = xCCorner + ${k};
|
|
if (${C===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
} else if (${C===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${d}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${N}
|
|
} else if (${C===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
|
|
);
|
|
|
|
${N}
|
|
}
|
|
}
|
|
setOutput(${w});
|
|
}
|
|
}
|
|
`}};function kee(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;rc(r,"avgPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return ys({inputs:{x:r},backend:n});let d=new mp(u,"avg",!1);return n.runWebGLProgram(d,[r],"float32")}var See={kernelName:Ea,backendName:"webgl",kernelFunc:kee};function Iee(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=E.computePool3DInfo(r.shape,a,o,u,i,l,c),p=new dx(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var Cee={kernelName:Qc,backendName:"webgl",kernelFunc:Iee},Tee=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 >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Nee=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.effectiveFilterDepth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,h=u-1-e.padInfo.front,f=d-1-e.padInfo.top,m=p-1-e.padInfo.left,g=1/(t*n*s);this.userCode=`
|
|
const ivec3 pads = ivec3(${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 Eee(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=E.computePool3DInfo(o.shape,i,l,d,c,u),h=new Nee(p);return n.runWebGLProgram(h,[r],o.dtype)}var Ree={kernelName:Ah,backendName:"webgl",kernelFunc:Eee};function $ee(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a}=t,o=a;rc([r,a],"avgPoolGrad");let{filterSize:i,strides:l,pad:c}=s,u=E.computePool2DInfo(o.shape,i,l,1,c),d=new Tee(u);return n.runWebGLProgram(d,[r],o.dtype)}var _ee={kernelName:gh,backendName:"webgl",kernelFunc:$ee};function Dee(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return Um({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var Pee={kernelName:Ra,backendName:"webgl",kernelFunc:Dee},Fee=class{constructor(e,t,n,s,r,a){this.outputShape=[],this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let o="0.0";s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="1.0";r!=null&&(E.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)));
|
|
}
|
|
`}},Oee=class{constructor(e,t,n,s,r,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n);let o="vec4(0.0)";s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset"),o="getOffsetAtOutCoords()");let i="vec4(1.0)";r!=null&&(E.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);
|
|
}
|
|
`}},Mee=({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=K().getBool("WEBGL_PACK_NORMALIZATION")?new Oee(s.shape,r.shape,a.shape,u,d,l):new Fee(s.shape,r.shape,a.shape,u,d,l);return t.runWebGLProgram(p,c,c[0].dtype)},zee={kernelName:Ua,backendName:"webgl",kernelFunc:Mee},Lee=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=yt(this.rank);this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let n=Bee(this.rank),s,r=e.map((a,o)=>`sourceLoc.${px[o]} = start[${o}] + coords.${px[o]};`);s=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${r.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
void main() {
|
|
${s}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}},px=["x","y","z","w","u","v"];function Bee(e){if(e===1)return"sourceLoc";if(e<=6)return px.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var Wee=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length,this.customUniforms=[{name:"start",arrayIndex:this.rank,type:"int"}];let t=yt(this.rank),n=Hn("coords",this.rank),s=Hn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${s.slice(-2).join()})`,a=`getChannel(getSource(${s.join()}), ${r})`,o=`
|
|
result.x = ${a};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${s[this.rank-1]};
|
|
result.y = ${a};
|
|
--${s[this.rank-1]};
|
|
}
|
|
`,i=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${s[this.rank-2]};
|
|
result.z = ${a};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${s[this.rank-1]};
|
|
result.w = ${a};
|
|
}
|
|
}
|
|
`,l=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((c,u)=>`start[${u}]`).join()});`:e.map((c,u)=>`${s[u]} = ${n[u]} + start[${u}];`).join(`
|
|
`);this.userCode=`
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${l}
|
|
vec4 result = vec4(0.);
|
|
${o}
|
|
${i}
|
|
setOutput(result);
|
|
}
|
|
`}};function Vee(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=Ft.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 dc(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Ft.parseSliceParams(r,a,o);if(Ft.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=TJ(d.values,i,l,r.shape,r.dtype);return n.makeTensorInfo(l,r.dtype,p)}let{isPacked:c}=n.texData.get(r.dataId),u=Ft.isSliceContinous(r.shape,i,l);if(c||!u){let d=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Wee(l):new Lee(l),p=[i];return n.runWebGLProgram(d,[r],r.dtype,p)}return n.uploadToGPU(r.dataId),Vee(r,i,l,n)}var Uee={kernelName:Wi,backendName:"webgl",kernelFunc:dc},Gee=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,y)=>x*y),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:l}}),m=jn({inputs:{x:f},backend:n,attrs:{perm:c}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:u}}),A=dc({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)),A},Hee={kernelName:pi,backendName:"webgl",kernelFunc:Gee};function jee(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=qI(i,l,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,c)}var qee={kernelName:yh,backendName:"webgl",kernelFunc:jee};function Xee(e){let{inputs:t,backend:n}=e,{s0:s,s1:r}=t,a=n.readSync(s.dataId),o=n.readSync(r.dataId),i=E.assertAndGetBroadcastShape(Array.from(a),Array.from(o));return n.makeTensorInfo([i.length],"int32",Int32Array.from(i))}var Kee={kernelName:xh,backendName:"webgl",kernelFunc:Xee},Zee="return float(a != b);",v4=Tn({opSnippet:Zee,cpuKernelImpl:wJ,dtype:"bool"}),Yee={kernelName:Ei,backendName:"webgl",kernelFunc:v4};function gp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return ys({inputs:{x:r.complexTensorInfos.real},backend:n})}var Jee={kernelName:ud,backendName:"webgl",kernelFunc:gp},Qee="return float(int(x));";function ete(e,t){let n=new Uo(e.shape,Qee),s=t.runWebGLProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function hx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return ys({inputs:{x:r},backend:n});let o=Gt(r.shape),i=hx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=Go({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeIntermediateTensorInfo(i),l}if(r.dtype==="complex64"){let o=gp({inputs:{input:r},backend:n}),i=hx({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeIntermediateTensorInfo(o),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=ys({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return ete(r,n);if(a==="bool"){let o=n.makeTensorInfo([],"bool",v.getTypedArrayFromDType("bool",1)),l=v4({inputs:{a:r,b:o},backend:n});return n.disposeIntermediateTensorInfo(o),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var tte={kernelName:$a,backendName:"webgl",kernelFunc:hx},w4="return ceil(x);",nte=st({opSnippet:w4,packedOpSnippet:w4,cpuKernelImpl:sJ}),ste={kernelName:_a,backendName:"webgl",kernelFunc:nte},rte=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));
|
|
}
|
|
`}},ate=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 ote(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i;K().getBool("WEBGL_PACK_CLIP")?i=new ate(r.shape):i=new rte(r.shape);let l=[[a],[o]];return n.runWebGLProgram(i,[r],r.dtype,l)}var ite={kernelName:jr,backendName:"webgl",kernelFunc:ote},lte=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 k4(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function ute(e){let{inputs:t,backend:n}=e,{x:s}=t,r=n.texData.get(s.dataId),a=new lte(s.shape),o=[k4(s,r.complexTensorInfos.real),k4(s,r.complexTensorInfos.imag)];return n.runWebGLProgram(a,o,o[0].dtype)}var cte={kernelName:td,backendName:"webgl",kernelFunc:ute},dte=class{constructor(e){this.outputShape=[],this.outputShape=E.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;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},pte=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=E.computeOutShape(e,t);let n=this.outputShape,s=n.length,r=yt(s),a=Hn("coords",s),o=["x","y","z","w","u","v"].slice(0,s);this.variableNames=e.map((f,m)=>`T${m}`);let i=new Array(e.length-1);i[0]=e[0][t];for(let f=1;f<i.length;f++)i[f]=i[f-1]+e[f][t];let l=o[t],c=o.slice(-2),u=o.join(),d=`if (${l} < ${i[0]}) {
|
|
return getChannel(
|
|
getT0(${u}), vec2(${c.join()}));
|
|
}`;for(let f=1;f<i.length;f++){let m=i[f-1];d+=`
|
|
if (${l} < ${i[f]} && ${l} >= ${i[f-1]}) {
|
|
return getChannel(
|
|
getT${f}(${Hm(o,l,m)}),
|
|
vec2(${Hm(c,l,m)}));
|
|
}`}let p=i.length,h=i[i.length-1];d+=`
|
|
return getChannel(
|
|
getT${p}(${Hm(o,l,h)}),
|
|
vec2(${Hm(c,l,h)}));`,this.userCode=`
|
|
float getValue(${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 Hm(e,t,n){let s=e.indexOf(t);return e.map((a,o)=>o===s?`${a} - ${n}`:a).join()}function jm(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.texData.get(s.dataId);return ys({inputs:{x:r.complexTensorInfos.imag},backend:n})}var hte={kernelName:ad,backendName:"webgl",kernelFunc:jm};function pc(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>gp({inputs:{input:m},backend:n})),d=e.map(m=>jm({inputs:{input:m},backend:n})),p=pc(u,t,n),h=pc(d,t,n),f=Go({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(A=>{let x=v.sizeFromShape(A.shape.slice(t));return ve({inputs:{x:A},backend:n,attrs:{shape:[-1,x]}})}),d=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=E.computeOutShape(u.map(A=>A.shape),1),h=u[0].shape[0]===1,f=rJ(d,p,s,h),m=E.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(A=>n.disposeIntermediateTensorInfo(A)),g}if(e.length>K().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(e.length/2),d=pc(e.slice(0,u),t,n),p=pc(e.slice(u),t,n),h=pc([d,p],t,n);return n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),h}if(K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let u=new pte(e.map(d=>d.shape),t);return n.runWebGLProgram(u,e,s)}let{tensors2D:a,outShape:o}=fte(e,t,n),i=new dte(a.map(u=>u.shape)),l=n.runWebGLProgram(i,a,s);a.forEach(u=>n.disposeIntermediateTensorInfo(u));let c=ve({inputs:{x:l},attrs:{shape:o},backend:n});return n.disposeIntermediateTensorInfo(l),c}function fte(e,t,n){let s=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>ve({inputs:{x:a},attrs:{shape:[-1,v.sizeFromShape(a.shape.slice(t))]},backend:n})),outShape:s}}function S4(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.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 ys({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return E.assertParamsConsistent(l,a),pc(i,a,n)}var mte={kernelName:hi,backendName:"webgl",kernelFunc:S4},I4=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,A=m?2:3,x=m?3:1,y="",b="";n&&(s?y=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?y=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:y=`
|
|
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=`
|
|
${y}
|
|
|
|
const ivec2 strides = ivec2(${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[${g}], coords[${A}]) * 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}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}},gte=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,s=e.padInfo.left,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=e.dilationDepth,l=e.dilationHeight,c=e.dilationWidth,u=e.filterDepth,d=e.filterHeight,p=e.filterWidth,h=Math.floor(e.inChannels/4)*4,f=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${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);
|
|
}
|
|
`}},Ate=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=Os(this.outputShape.length);let{dataFormat:n}=t,s=Gn(),r=n==="channelsLast",a=r?0:1,o=r?1:2,i=this.enableShapeUniforms?"if(blockIndex < outShape[1] && pos < outShape[0]) {":`if(blockIndex < ${e[1]} && pos < ${e[0]}) {`,l="";for(let c=0;c<=1;c++)for(let u=0;u<=1;u++)l+=`
|
|
blockIndex = rc.y + ${u};
|
|
pos = rc.x + ${c};
|
|
|
|
${i}
|
|
offsetY = int(blockIndex / outWidth) * stride[0] - pad[0];
|
|
d0 = offsetY + dilation[0] * (pos / itemsPerBlockRow);
|
|
|
|
if(d0 < inputShape[${a}] && d0 >= 0) {
|
|
// Use custom imod instead mod. On Intel GPU, mod may generate
|
|
// unexpected value.
|
|
// https://github.com/tensorflow/tfjs/issues/5447
|
|
offsetX = imod(blockIndex, outWidth) * stride[1] - pad[1];
|
|
d1 = offsetX + dilation[1] * (imod(pos, itemsPerBlockRow) /
|
|
inChannels);
|
|
|
|
if(d1 < inputShape[${o}] && d1 >= 0) {
|
|
|
|
ch = imod(pos, inChannels);
|
|
|
|
if (${r}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${c*2+u}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${c*2+u}] = getChannel(
|
|
getA(ch, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
`;this.userCode=`
|
|
void main() {
|
|
ivec2 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0);
|
|
|
|
int blockIndex, pos, offsetY, d0, offsetX, d1, ch;
|
|
vec2 innerDims;
|
|
|
|
${l}
|
|
|
|
${s.output} = result;
|
|
}
|
|
`}};function C4({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,A=[];if(!((d===1||p===1)&&u>m4)&&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(dp(c.shape,w.shape),()=>`packed reshape ${c.shape} to ${w.shape} isn't free`);let C=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}});A.push(C);let N=Um({a:w,b:C,backend:s,transposeA:f,transposeB:m,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),R=s.texData.get(N.dataId);v.assert(R.isPacked,()=>"batchMatMul result is expected to be packed"),c.shape=k,R.shape=n.outShape,g=ys({inputs:{x:N},backend:s}),g.shape=n.outShape,A.push(N)}else{let b=h?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],w=ve({inputs:{x:e},backend:s,attrs:{shape:[1,b,n.inChannels]}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),C=Um({a:w,b:k,transposeA:f,transposeB:m,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o});g=ve({inputs:{x:C},backend:s,attrs:{shape:n.outShape}}),A.push(w),A.push(k),A.push(C)}for(let b of A)s.disposeIntermediateTensorInfo(b);return g}function T4({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,A=[m,g],x=!0,y=!1,b=[],w=ve({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),k=ve({inputs:{x:t},backend:s,attrs:{shape:[1,m,v.sizeFromShape(t.shape)/m]}});b.push(w),b.push(k);let C=new Ate(A,n),N=[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(C,[w],"float32",N),F=ve({inputs:{x:R},backend:s,attrs:{shape:[1,A[0],A[1]]}});b.push(R),b.push(F);let _=r!=null,P=a!=null,T=i==="leakyrelu",M=i?Bm(i,!0):null,U=new c4(F.shape,k.shape,[1,g,n.outChannels],x,y,_,M,P,T),j=[F,k];if(r&&j.push(r),P&&j.push(a),T){let J=s.makeTensorInfo([],"float32",v.createScalarValue(o,"float32"));j.push(J),b.push(J)}let z=s.runWebGLProgram(U,j,"float32"),X=f?[1,p,d,n.outChannels]:[1,n.outChannels,p,d],Z=ve({inputs:{x:z},backend:s,attrs:{shape:X}});b.push(z);for(let J of b)s.disposeIntermediateTensorInfo(J);return Z}function yte(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=E.convertConv2DDataFormat(l),p=E.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=C4({x:r,filter:a,convInfo:p,backend:n});else if(K().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=T4({x:r,filter:a,convInfo:p,backend:n});else{let m=new I4(p);h=n.runWebGLProgram(m,[r,a],"float32")}let f=ve({inputs:{x:h},backend:n,attrs:{shape:p.outShape}});return n.disposeIntermediateTensorInfo(h),f}var xte={kernelName:Da,backendName:"webgl",kernelFunc:yte},bte=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);
|
|
}
|
|
`}},vte=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);
|
|
}
|
|
`}},wte=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},kte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,s=e.filterWidth,r=e.strideDepth,a=e.strideHeight,o=e.strideWidth,i=t-1-e.padInfo.front,l=n-1-e.padInfo.top,c=s-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 Ste(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=E.convertConv2DDataFormat(l),p=E.computeConv2DInfo(r.shape,u,o,1,i,c,!1,d),h=new bte(p);return n.runWebGLProgram(h,[r,a],"float32")}var Ite={kernelName:bh,backendName:"webgl",kernelFunc:Ste};function Cte(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=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=new vte(p);return n.runWebGLProgram(h,[r,a],"float32")}var Tte={kernelName:Pa,backendName:"webgl",kernelFunc:Cte};function Nte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=E.computeConv3DInfo(r.shape,a.shape,o,l,i),u=new gte(c);return n.runWebGLProgram(u,[r,a],"float32")}var Ete={kernelName:nd,backendName:"webgl",kernelFunc:Nte};function Rte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,pad:i,filterShape:l}=s,c=E.computeConv3DInfo(r.shape,l,o,1,i),u=new wte(c);return n.runWebGLProgram(u,[r,a],"float32")}var $te={kernelName:vh,backendName:"webgl",kernelFunc:Rte};function _te(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{pad:o,strides:i,inputShape:l}=s,c=E.computeConv3DInfo(l,a.shape,i,1,o),u=new kte(c);return n.runWebGLProgram(u,[r,a],"float32")}var Dte={kernelName:wh,backendName:"webgl",kernelFunc:_te},Pte=u4+`
|
|
return cos(x);
|
|
`,Fte=st({opSnippet:Pte}),Ote={kernelName:Fa,backendName:"webgl",kernelFunc:Fte},Mte=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,zte=st({opSnippet:Mte}),Lte={kernelName:Oa,backendName:"webgl",kernelFunc:zte},Bte=class{constructor(e,t,n,s,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,o,i,l]=e,[c]=t,[u,d]=n;this.outputShape=[c,u,d,l];let p=s==="bilinear"?1:0,[h,f]=[`${o-1}.0`,`${i-1}.0`],[m,g,A]=u>1?[`${(o-1)/(u-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[x,y,b]=d>1?[`${(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 = ${y};
|
|
|
|
float in_y = ${A};
|
|
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(${p} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},Wte=e=>{let{inputs:t,backend:n,attrs:s}=e,{image:r,boxes:a,boxInd:o}=t,{cropSize:i,method:l,extrapolationValue:c}=s,u=new Bte(r.shape,a.shape,i,l,c);return n.runWebGLProgram(u,[r,a,o],"float32")},Vte={kernelName:mi,backendName:"webgl",kernelFunc:Wte},N4=class{constructor(e,t,n){this.variableNames=["x"],this.customUniforms=[{name:"index",type:"float"}],this.outputShape=e;let s=e.length,r=t?"0.0":`getX(${E4(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() {
|
|
${yt(s)} coords = getOutputCoords();
|
|
int end = ${R4(s,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${o}) {
|
|
int idx = ${i};
|
|
${R4(s,"coords")} = idx;
|
|
val += getX(${E4(s,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}};function E4(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 ${e} is not yet supported`)}function R4(e,t){if(e===1)return`${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 ${e} is not yet supported`)}function Ute(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,exclusive:o,reverse:i}=s,l=r.shape.length,c=E.getAxesPermutation([a],l),u=r;c!=null&&(u=jn({inputs:{x:r},backend:n,attrs:{perm:c}}));let d=E.getInnerMostAxes(1,l)[0];if(d!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${a}`);let p=u.shape[d],h=ys({inputs:{x:u},backend:n});for(let f=0;f<=Math.ceil(Math.log2(p))-1;f++){let m=new N4(u.shape,!1,i),g=[[f]],A=h;h=n.runWebGLProgram(m,[h],h.dtype,g),n.disposeIntermediateTensorInfo(A)}if(o){let f=new N4(u.shape,o,i),m=h;h=n.runWebGLProgram(f,[h],h.dtype),n.disposeIntermediateTensorInfo(m)}if(c!=null){let f=E.getUndoAxesPermutation(c),m=jn({inputs:{x:h},backend:n,attrs:{perm:f}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(u),m}return h}var Gte={kernelName:fi,backendName:"webgl",kernelFunc:Ute};function Hte(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,weights:a}=t,{size:o,binaryOutput:i}=s;if(r.shape.length===1){let l=n.readSync(r.dataId),c=n.readSync(a.dataId),u=qI(l,c,a.dtype,a.shape,o);return n.makeTensorInfo([o],a.dtype,u)}else if(r.shape.length===2){let l=n.bufferSync(r),c=n.bufferSync(a),u=nJ(l,c,o,i);return n.makeTensorInfo(u.shape,a.dtype,u.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var jte={kernelName:kh,backendName:"webgl",kernelFunc:Hte},qte=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 Xte(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 qte(f,a,o);return n.runWebGLProgram(m,[r],r.dtype)}var Kte={kernelName:gi,backendName:"webgl",kernelFunc:Xte},$4=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=Os(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}
|
|
${c}
|
|
setOutput(result);
|
|
}
|
|
`}},_4=class{constructor(e,t=!1,n=null,s=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.customUniforms=[{name:"pads",type:"ivec2"},{name:"strides",type:"ivec2"},{name:"dilations",type:"ivec2"},{name:"inDims",type:"ivec2"}],this.outputShape=e.outShape,this.enableShapeUniforms=Os(this.outputShape.length);let a=e.outChannels/e.inChannels,o=e.padInfo.left,i=e.strideWidth,l=e.dilationWidth,c=e.filterHeight,u=e.filterWidth,d=u,p=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let g=0;g<u;g++)p+=`
|
|
vec4 xTexelC${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 A=g*2;if(p+=`
|
|
xC = xCCorner + ${A*l};
|
|
`,i===1){if(A<u&&(o%2==1?(p+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
|
|
xTexelC${A} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${A}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A}Ready = 1;
|
|
}
|
|
`,l===1&&A>0?p+=`
|
|
xC${A} = vec4(xTexelC${A-2}.zw, xTexelC${A}.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${A} = vec4(previous.zw, xTexelC${A}.xy);
|
|
} else {
|
|
xC${A} = vec4(0.0, 0.0, xTexelC${A}.xy);
|
|
}
|
|
`):p+=`
|
|
if (xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) {
|
|
xTexelC${A} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${A}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A}Ready = 1;
|
|
}
|
|
|
|
xC${A} = xTexelC${A};
|
|
`,A+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) + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
|
|
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${A+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A+1}Ready = 1;
|
|
}
|
|
`,l>1&&(p+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
|
|
xTexelC${A} = getX(batch, xR, xCOffset, d1);
|
|
xTexelC${A}Ready = 1;
|
|
}
|
|
`),p+=`
|
|
xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.xy);
|
|
`):x===1?p+=`
|
|
xC${A+1} = xTexelC${A};
|
|
`:p+=`
|
|
xCOffset = xC + ${x};
|
|
|
|
if (xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
|
|
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${A+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A+1}Ready = 1;
|
|
}
|
|
|
|
xC${A+1} = xTexelC${A+1};
|
|
`}}else A<u&&(o%2==1?(p+=`
|
|
xCOffset = xC + 1 - strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A}Ready == 0) {
|
|
xTexelC${A} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${A}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < inDims[1] && xTexelC${A+1}Ready == 0) {
|
|
xTexelC${A+1} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= inDims[1]) {
|
|
xTexelC${A+1}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A+1}Ready = 1;
|
|
}
|
|
|
|
xC${A} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw);
|
|
`,A+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${A+1} = vec4(xTexelC${A+1}.xy, final.xy);
|
|
`)):(p+=`
|
|
if(xC >= 0 && xC < inDims[1] && xTexelC${A}Ready == 0) {
|
|
xTexelC${A} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= inDims[1]) {
|
|
xTexelC${A}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${A}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + strides[1];
|
|
if(xCOffset >= 0 && xCOffset < inDims[1] && xTexelC${A+1}Ready == 0) {
|
|
xTexelC${A+1} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= inDims[1]) {
|
|
xTexelC${A+1}.zw = vec2(0.);
|
|
}
|
|
xTexelC${A+1}Ready = 1;
|
|
}
|
|
|
|
xC${A} = vec4(
|
|
xTexelC${A}.xy, xTexelC${A+1}.xy);
|
|
`,A+1<u&&(p+=`
|
|
xC${A+1} = vec4(xTexelC${A}.zw, xTexelC${A+1}.zw);
|
|
`)));A<u&&(p+=`
|
|
wTexel = getW(r, ${A}, d1, q);
|
|
dotProd += xC${A} * vec4(wTexel.xz, wTexel.xz);
|
|
`,A+1<u&&(p+=`
|
|
wTexel = getW(r, ${A+1}, d1, q);
|
|
dotProd += xC${A+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 Zte(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(E.eitherStridesOrDilationsAreOne(o,u),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${o} and dilations '${u}'`);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;K().getBool("WEBGL_PACK_DEPTHWISECONV")&&d.strideWidth<=2&&d.outChannels/d.inChannels==1?p=new _4(d):p=new $4(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 Yte={kernelName:Ma,backendName:"webgl",kernelFunc:Zte},Jte=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 >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Qte=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,s=e.strideHeight,r=e.strideWidth,a=t-1-e.padInfo.top,o=n-1-e.padInfo.left,i=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${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 * ${i} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function ene(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,dy:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,filterShape:u}=s,d=E.computeConv2DInfo(r.shape,u,o,i,l,c,!0),p=new Jte(d);return n.runWebGLProgram(p,[r,a],"float32")}var tne={kernelName:Sh,backendName:"webgl",kernelFunc:ene};function nne(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{strides:o,dilations:i,pad:l,dimRoundingMode:c,inputShape:u}=s,d=E.computeConv2DInfo(u,a.shape,o,i,l,c,!0),p=new Qte(d);return n.runWebGLProgram(p,[r,a],"float32")}var sne={kernelName:Ih,backendName:"webgl",kernelFunc:nne},rne=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function ane(e){let{inputs:t,backend:n}=e,{x:s}=t,r=[...s.shape,...s.shape],a=v.sizeFromShape(s.shape),o=ve({inputs:{x:s},backend:n,attrs:{shape:[a]}}),i=new rne(a),l=n.runWebGLProgram(i,[o],o.dtype),c=ve({inputs:{x:l},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),c}var one={kernelName:Ch,backendName:"webgl",kernelFunc:ane},ine=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:s,strideHeight:r,strideWidth:a,filterHeight:o,filterWidth:i,dilationHeight:l,dilationWidth:c}=e,{top:u,left:d}=s;this.userCode=`
|
|
const ivec2 strides = ivec2(${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 lne(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l}=s,c=E.computeDilation2DInfo(r.shape,a.shape,o,i,"NHWC",l),u,d=new ine(c);u=n.runWebGLProgram(d,[r,a],"float32");let p=ve({inputs:{x:u},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(u),p}var une={kernelName:sd,backendName:"webgl",kernelFunc:lne};function cne(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.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:A,expandDims:x}=E.getEinsumPermutation(h,l[g]),y;E.isIdentityPermutation(A)?y=a[g]:(y=jn({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(y));let b=y.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(y.shape,b)||(y=ve({inputs:{x:y},backend:n,attrs:{shape:b}}),f.push(y)),p===null?p=y:(p=cx({inputs:{a:y,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Vm({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 dne={kernelName:rd,backendName:"webgl",kernelFunc:cne},pne="return (x >= 0.0) ? x : (exp(x) - 1.0);",hne=`
|
|
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;
|
|
`,fne=st({opSnippet:pne,packedOpSnippet:hne}),mne={kernelName:La,backendName:"webgl",kernelFunc:fne},gne="return (b >= 1.0) ? a : a * (b + 1.0);",Ane=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,yne=e=>{let{inputs:t,backend:n}=e,{dy:s,y:r}=t,a=K().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new fp(Ane,s.shape,r.shape):new cc(gne,s.shape,r.shape);return n.runWebGLProgram(a,[s,r],s.dtype)},xne={kernelName:Eh,backendName:"webgl",kernelFunc:yne},bne=`
|
|
return vec4(equal(a, b));
|
|
`,vne="return float(a == b);",wne=Tn({opSnippet:vne,packedOpSnippet:bne,dtype:"bool",cpuKernelImpl:aJ}),kne={kernelName:Ai,backendName:"webgl",kernelFunc:wne},Sne=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${E.ERF_P};
|
|
float a1 = ${E.ERF_A1};
|
|
float a2 = ${E.ERF_A2};
|
|
float a3 = ${E.ERF_A3};
|
|
float a4 = ${E.ERF_A4};
|
|
float a5 = ${E.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,Ine=st({opSnippet:Sne}),Cne={kernelName:hu,backendName:"webgl",kernelFunc:Ine},D4="return exp(x);",P4=st({opSnippet:D4,packedOpSnippet:D4,cpuKernelImpl:oJ,dtype:"float32"}),Tne={kernelName:Ba,backendName:"webgl",kernelFunc:P4};function fx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),ve({inputs:{x:a},backend:s,attrs:{shape:i}})}var Nne={kernelName:yi,backendName:"webgl",kernelFunc:fx},F4="return exp(x) - 1.0;",Ene=st({opSnippet:F4,packedOpSnippet:F4,cpuKernelImpl:iJ}),Rne={kernelName:xi,backendName:"webgl",kernelFunc:Ene},O4=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 M4(e,t,n){let s=n.texData.get(e.dataId),r=v.sizeFromShape(e.shape),a=e.shape[e.shape.length-1],o=r/a,i=ve({inputs:{x:e},backend:n,attrs:{shape:[o,a]}}),l=i.shape,c=new O4("real",l,t),u=new O4("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=Go({inputs:{real:p,imag:h},backend:n});n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h);let m=ve({inputs:{x:f},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(f),m}function $ne(e){let{inputs:t,backend:n}=e,{input:s}=t;return M4(s,!1,n)}var _ne={kernelName:Rh,backendName:"webgl",kernelFunc:$ne},Dne=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 Ap(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 Dne(s,r),i=[[r]];return t.runWebGLProgram(o,[],a,i)}}var Pne={kernelName:fu,backendName:"webgl",kernelFunc:Ap},Fne=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},One={kernelName:bi,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Fne(n.shape);return s.runWebGLProgram(r,[n],n.dtype)}},z4="return floor(x);",Mne=st({opSnippet:z4,packedOpSnippet:z4,cpuKernelImpl:lJ}),zne={kernelName:Wa,backendName:"webgl",kernelFunc:Mne},Lne=`
|
|
float s = sign(a) * sign(b);
|
|
int ia = round(a);
|
|
int ib = round(b);
|
|
if (ib != 0) {
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
return float(idiv(ia, ib, s));
|
|
} else {
|
|
return NAN;
|
|
}
|
|
`,Bne=`
|
|
ivec4 ia = round(a);
|
|
ivec4 ib = round(b);
|
|
bvec4 cond = notEqual(ib, ivec4(0));
|
|
ivec4 result = ivec4(0);
|
|
vec4 s = sign(a) * sign(b);
|
|
|
|
// Windows (D3D) wants guaranteed non-zero int division at compile-time.
|
|
if (cond[0]) {
|
|
result[0] = idiv(ia[0], ib[0], s[0]);
|
|
}
|
|
if (cond[1]) {
|
|
result[1] = idiv(ia[1], ib[1], s[1]);
|
|
}
|
|
if (cond[2]) {
|
|
result[2] = idiv(ia[2], ib[2], s[2]);
|
|
}
|
|
if (cond[3]) {
|
|
result[3] = idiv(ia[3], ib[3], s[3]);
|
|
}
|
|
return vec4(result);
|
|
`,Wne=Tn({opSnippet:Lne,packedOpSnippet:Bne,dtype:"int32"}),Vne={kernelName:Va,backendName:"webgl",kernelFunc:Wne},Une=class{constructor(e){this.variableNames=["A"];let t=Gn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${s}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},Gne=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Gn(),[n,s]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${s}.0, ${n}.0);
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
result[row * 2 + col] = floor(value * 255.0 + 0.5);
|
|
}
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},Hne={kernelName:hd,backendName:"webgl",kernelFunc:jne},hc;function jne(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)&&(hc==null&&(hc=document.createElement("canvas").getContext("2d")),hc.canvas.width=l,hc.canvas.height=c,hc.drawImage(r,0,0,l,c),r=hc.canvas);let p=n.makeTensorInfo(u,"int32");n.texData.get(p.dataId).usage=Ps.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(p.dataId),r);let h=K().getBool("WEBGL_PACK")?new Gne(d):new Une(d),f=n.runWebGLProgram(h,[p],"int32");return n.disposeData(p.dataId),f}function qne(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=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),A,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"))A=C4({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});else if(K().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)A=T4({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",C=h?Bm(h,!1):null,N=new I4(g,b,C,w,k),R=[r,a];if(o&&R.push(o),i&&R.push(i),k){let F=n.makeTensorInfo([],"float32",v.createScalarValue(f,"float32"));R.push(F),x.push(F)}A=n.runWebGLProgram(N,R,"float32")}let y=ve({inputs:{x:A},backend:n,attrs:{shape:g.outShape}});return x.push(A),x.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var Xne={kernelName:bo,backendName:"webgl",kernelFunc:qne};function Kne(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(E.eitherStridesOrDilationsAreOne(l,m),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${m}'`);let g=E.computeConv2DInfo(r.shape,a.shape,l,m,c,d,!0),A=K().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,x=p?Bm(p,A):null,y=[r,a],b=o!=null,w=i!=null,k=p==="leakyrelu";if(b&&y.push(o),w&&y.push(i),k){let F=n.makeTensorInfo([],"float32",v.createScalarValue(h,"float32"));y.push(F),f.push(F)}let C;A?C=new _4(g,b,x,w,k):C=new $4(g,b,x,w,k);let N=[[g.padInfo.top,g.padInfo.left],[g.strideHeight,g.strideWidth],[g.dilationHeight,g.dilationWidth],[g.inHeight,g.inWidth]],R=n.runWebGLProgram(C,y,"float32",N);return f.forEach(F=>n.disposeIntermediateTensorInfo(F)),R}var Zne={kernelName:vo,backendName:"webgl",kernelFunc:Kne},Yne=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let s=yt(t.length),r=yt(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 Jne(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]=E.prepareAndValidate(s,r),p=ve({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=ve({inputs:{x:s},backend:n,attrs:{shape:[v.sizeFromShape(s.shape)/u,u]}});if(n.shouldExecuteOnCPU([s,r])||s.dtype==="string"){let A=n.readSync(r.dataId),x=n.bufferSync(s),y=uJ(A,x,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,y.values)}let f=new Yne(o,d,[c,u]),m=n.runWebGLProgram(f,[h,p],h.dtype),g=ve({inputs:{x:m},backend:n,attrs:{shape:l}});return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),g}var Qne={kernelName:wi,backendName:"webgl",kernelFunc:Jne},ese=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=yt(this.rank),s=tse(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function tse(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, ${u-1}]`)}let d=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=v.sizeFromShape(a.shape),h=[],f=ve({inputs:{x:r},backend:n,attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]}}),m=ve({inputs:{x:a},backend:n,attrs:{shape:[d.batchSize,p/d.batchSize]}});h.push(f),h.push(m);let g=[d.batchSize,d.outerSize,p/d.batchSize,d.sliceSize];if(n.shouldExecuteOnCPU([r,a])||r.dtype==="string"){let b=n.bufferSync(m),w=n.bufferSync(f),k=cJ(w,b,g);return h.forEach(C=>n.disposeIntermediateTensorInfo(C)),n.makeTensorInfo(d.outputShape,k.dtype,k.values)}let A=new ese(f.shape,g),x=n.runWebGLProgram(A,[f,m],f.dtype);h.push(x);let y=ve({inputs:{x},backend:n,attrs:{shape:d.outputShape}});return h.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var nse={kernelName:vi,backendName:"webgl",kernelFunc:L4},sse="return float(a > b);",rse=`
|
|
return vec4(greaterThan(a, b));
|
|
`,ase=Tn({opSnippet:sse,packedOpSnippet:rse,cpuKernelImpl:dJ,dtype:"bool"}),ose={kernelName:ki,backendName:"webgl",kernelFunc:ase},ise="return float(a >= b);",lse=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,use=Tn({opSnippet:ise,packedOpSnippet:lse,dtype:"bool",cpuKernelImpl:pJ}),cse={kernelName:Ga,backendName:"webgl",kernelFunc:use};function dse(e){let{inputs:t,backend:n}=e,{input:s}=t;return M4(s,!0,n)}var pse={kernelName:$h,backendName:"webgl",kernelFunc:dse},hse="return float(!isnan(x) && !isinf(x));",fse=st({opSnippet:hse,dtype:"bool"}),mse={kernelName:mu,backendName:"webgl",kernelFunc:fse},gse="return float(isinf(x));",Ase=st({opSnippet:gse,dtype:"bool"}),yse={kernelName:gu,backendName:"webgl",kernelFunc:Ase},xse="return float(isnan(x));",bse=st({opSnippet:xse,dtype:"bool"}),vse={kernelName:Au,backendName:"webgl",kernelFunc:bse},wse="return float(a < b);",kse=`
|
|
return vec4(lessThan(a, b));
|
|
`,Sse=Tn({opSnippet:wse,packedOpSnippet:kse,cpuKernelImpl:hJ,dtype:"bool"}),Ise={kernelName:Ii,backendName:"webgl",kernelFunc:Sse},Cse="return float(a <= b);",Tse=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,Nse=Tn({opSnippet:Cse,packedOpSnippet:Tse,cpuKernelImpl:fJ,dtype:"bool"}),Ese={kernelName:Ci,backendName:"webgl",kernelFunc:Nse};function Rse(e){let{backend:t,attrs:n}=e,{start:s,stop:r,num:a}=n,o=mJ(s,r,a);return t.makeTensorInfo([o.length],"float32",o)}var $se={kernelName:_h,backendName:"webgl",kernelFunc:Rse},_se=`if (x < 0.0) return NAN;
|
|
return log(x);`,Dse=`
|
|
vec4 result = log(x);
|
|
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
|
|
result.r = isNaN.r == 1.0 ? NAN : result.r;
|
|
result.g = isNaN.g == 1.0 ? NAN : result.g;
|
|
result.b = isNaN.b == 1.0 ? NAN : result.b;
|
|
result.a = isNaN.a == 1.0 ? NAN : result.a;
|
|
|
|
return result;
|
|
`,Pse=st({opSnippet:_se,packedOpSnippet:Dse,cpuKernelImpl:gJ}),Fse={kernelName:ja,backendName:"webgl",kernelFunc:Pse},Ose="return log(1.0 + x);",Mse=st({opSnippet:Ose}),zse={kernelName:yu,backendName:"webgl",kernelFunc:Mse},Lse="return float(a >= 1.0 && b >= 1.0);",Bse=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,Wse=Tn({opSnippet:Lse,packedOpSnippet:Bse,dtype:"bool"}),Vse={kernelName:Ti,backendName:"webgl",kernelFunc:Wse},Use="return float(!(x >= 1.0));",Gse=st({opSnippet:Use}),Hse={kernelName:xu,backendName:"webgl",kernelFunc:Gse},jse="return float(a >= 1.0 || b >= 1.0);",qse=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,Xse=Tn({opSnippet:jse,packedOpSnippet:qse,dtype:"bool"}),Kse={kernelName:od,backendName:"webgl",kernelFunc:Xse},Zse=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[];let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${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 * ${i};
|
|
setOutput(val);
|
|
}
|
|
`}},Yse=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,o=e[3]-1;this.outputShape=e;let i,l=`float(${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);
|
|
}
|
|
`}},Jse=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{depthRadius:a,bias:o,alpha:i,beta:l}=s,c=K().getBool("WEBGL_PACK_NORMALIZATION")?new Yse(r.shape,a,o,i,l):new Zse(r.shape,a,o,i,l);return n.runWebGLProgram(c,[r],r.dtype)},Qse={kernelName:id,backendName:"webgl",kernelFunc:Jse},ere=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);
|
|
}
|
|
`}},tre=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 ere(r.shape,i,l,c,u);return n.runWebGLProgram(d,[r,a,o],r.dtype)},nre={kernelName:Dh,backendName:"webgl",kernelFunc:tre};function sre(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=Rl(i,e.dtype,"max",s),c=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}function B4(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=E.getAxesPermutation(c,i),d=u!=null,p=n.shouldExecuteOnCPU([r]),h=r;if(d){if(p){let y=n.texData.get(h.dataId).values,b=new Array(i);for(let C=0;C<b.length;C++)b[C]=r.shape[u[C]];let w=ux(y,r.shape,r.dtype,u,b);h=n.makeTensorInfo(b,r.dtype);let k=n.texData.get(h.dataId);k.values=w}else h=Wm(r,u,n);c=E.getInnerMostAxes(c.length,i)}E.assertAxesAreInnerMostDims("max",c,i);let[f,m]=E.computeOutAndReduceShapes(h.shape,c),g=f;o&&(g=E.expandShapeToKeepDim(f,l));let A;if(p){let y=n.texData.get(h.dataId).values,b=AJ(y,v.sizeFromShape(m),g,r.dtype);A=n.makeTensorInfo(g,r.dtype);let w=n.texData.get(A.dataId);w.values=b}else A=sre(h,m,g,n);return d&&n.disposeIntermediateTensorInfo(h),A}var rre={kernelName:qa,backendName:"webgl",kernelFunc:B4},are=r4+`
|
|
return max(a, b);
|
|
`,ore=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Lm+`
|
|
return result;
|
|
`,ire=Tn({opSnippet:are,packedOpSnippet:ore,cpuKernelImpl:yJ}),lre={kernelName:Xa,backendName:"webgl",kernelFunc:ire};function ure(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t;rc(r,"maxPool");let{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1;v.assert(E.eitherStridesOrDilationsAreOne(o,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${o} and dilations '${c}'`);let u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return ys({inputs:{x:r},backend:n});let d=new mp(u,"max",!1);return n.runWebGLProgram(d,[r],r.dtype)}var cre={kernelName:Ka,backendName:"webgl",kernelFunc:ure};function dre(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=E.computePool3DInfo(r.shape,a,o,u,i,c,l),p=new dx(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var pre={kernelName:ld,backendName:"webgl",kernelFunc:dre},hre=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 * ${a} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},fre=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,s=e.strideWidth,r=e.dilationDepth,a=e.dilationHeight,o=e.dilationWidth,i=e.effectiveFilterDepth,l=e.effectiveFilterHeight,c=e.effectiveFilterWidth,u=i-1-e.padInfo.front,d=l-1-e.padInfo.top,p=c-1-e.padInfo.left,h=i*l*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${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 mre(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=E.computePool3DInfo(o.shape,i,l,d,c,u),h=new dx(p,"max",!0),f=n.runWebGLProgram(h,[o],o.dtype),m=new fre(p),g=n.runWebGLProgram(m,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),g}var gre={kernelName:Fh,backendName:"webgl",kernelFunc:mre};function Are(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,input:a,output:o}=t,i=a;rc([a,o],"maxPoolGrad");let{filterSize:l,strides:c,pad:u,dimRoundingMode:d}=s,p=E.computePool2DInfo(i.shape,l,c,1,u,d),h=!0,f=new mp(p,"max",h),m=n.runWebGLProgram(f,[i],i.dtype),g=new hre(p),A=n.runWebGLProgram(g,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var yre={kernelName:Ph,backendName:"webgl",kernelFunc:Are};function xre(e,t,n,s){let r=new mp(n,"max",!1),a=s.runWebGLProgram(r,[e],"float32");r=new mp(n,"max",!0,!0,t);let o=s.runWebGLProgram(r,[e],"float32");return[a,o]}var bre={kernelName:Oh,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(E.eitherStridesOrDilationsAreOne(a,c),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let u=E.computePool2DInfo(s.shape,r,a,c,o),[d,p]=xre(s,i,u,l);return[d,p]}};function vre(e,t,n,s){let r=v.sizeFromShape(t),o=v.sizeFromShape(e.shape)/r,i=ve({inputs:{x:e},attrs:{shape:[o,r]},backend:s}),l=Rl(i,"float32","mean",s),c=ve({inputs:{x:l},attrs:{shape:n},backend:s});return s.disposeIntermediateTensorInfo(i),s.disposeIntermediateTensorInfo(l),c}var wre={kernelName:Za,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=E.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 N=0;N<w.length;N++)w[N]=s.shape[u[N]];let k=ux(b,s.shape,s.dtype,u,w);f=o.makeTensorInfo(w,s.dtype);let C=o.texData.get(f.dataId);C.values=k}else f=Wm(s,u,o);h.push(f),c=E.getInnerMostAxes(c.length,i)}E.assertAxesAreInnerMostDims("sum",c,i);let[m,g]=E.computeOutAndReduceShapes(f.shape,c),A=m;r&&(A=E.expandShapeToKeepDim(m,l));let x=vre(f,g,A,o);for(let y of h)o.disposeIntermediateTensorInfo(y);return x}};function kre(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=E.getAxesPermutation(c,i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),c=E.getInnerMostAxes(c.length,r.shape.length)),E.assertAxesAreInnerMostDims("min",c,i);let[p,h]=E.computeOutAndReduceShapes(d.shape,c),f=v.sizeFromShape(h),m=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,f]}}),g=Rl(m,m.dtype,"min",n),A;if(o){let x=E.expandShapeToKeepDim(p,l);A=ve({inputs:{x:g},backend:n,attrs:{shape:x}})}else A=ve({inputs:{x:g},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(g),u!=null&&n.disposeIntermediateTensorInfo(d),A}var Sre={kernelName:Ya,backendName:"webgl",kernelFunc:kre},Ire=r4+`
|
|
return min(a, b);
|
|
`,Cre=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Lm+`
|
|
return result;
|
|
`,Tre=Tn({opSnippet:Ire,packedOpSnippet:Cre,cpuKernelImpl:xJ}),Nre={kernelName:Ja,backendName:"webgl",kernelFunc:Tre},Ere=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=yt(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}));
|
|
}
|
|
`}},Rre=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=yt(s),a=t.map(h=>h[0]).join(","),o=t.map((h,f)=>h[0]+e[f]).join(","),i=Hn("rc",s),l=Hn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=n==="reflect"?0:1,p="";if(s===1){let h=`
|
|
${r} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${d};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${d};
|
|
}
|
|
source -= start;
|
|
`;p=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${u});
|
|
${i[s-1]} += 1;
|
|
if(${c}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`}else{let h=`
|
|
${r} source = rc;
|
|
${r} lt = ${r}(lessThan(source, start));
|
|
${r} gte = ${r}(greaterThanEqual(source, end));
|
|
${r} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${d}) +
|
|
gte * ((end - 1) * 2 - source + ${d});
|
|
source -= start;
|
|
`;p=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${l.join()}), ${u});
|
|
${i[s-1]} += 1;
|
|
if(${c}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
rc = outputLoc;
|
|
${i[s-2]} += 1;
|
|
if(${i[s-2]} < ${this.outputShape[s-2]}) {
|
|
${h}
|
|
result[2] = getChannel(getX(${l.join()}), ${u});
|
|
${i[s-1]} += 1;
|
|
if(${c}) {
|
|
${h}
|
|
result[3] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${r} start = ${r}(${a});
|
|
const ${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${p}
|
|
setOutput(result);
|
|
}
|
|
`}},$re=({inputs:e,backend:t,attrs:n})=>{let{x:s}=e,{paddings:r,mode:a}=n,o=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Rre(s.shape,r,a):new Ere(s.shape,r,a);return t.runWebGLProgram(o,[s],s.dtype)},_re={kernelName:Qa,backendName:"webgl",kernelFunc:$re},Dre=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,Pre=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+Lm+`
|
|
return result;
|
|
`,Fre=Tn({opSnippet:Dre,packedOpSnippet:Pre}),Ore={kernelName:bu,backendName:"webgl",kernelFunc:Fre},Mre=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}));
|
|
}
|
|
`}},zre=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,Lre=`
|
|
// 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;
|
|
`,W4=Tn({opSnippet:zre,packedOpSnippet:Lre,checkOutOfBounds:!0}),Bre={kernelName:za,backendName:"webgl",kernelFunc:W4},V4="return a - b;",U4=Tn({opSnippet:V4,packedOpSnippet:V4,supportsComplex:!0,cpuKernelImpl:FJ}),Wre={kernelName:mo,backendName:"webgl",kernelFunc:U4};function G4(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=B4({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=E.expandShapeToKeepDim(i.shape,o),c=ve({inputs:{x:i},backend:n,attrs:{shape:l}}),u=U4({inputs:{a:r,b:c},backend:n}),d=P4({inputs:{x:u},backend:n}),p=Vm({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=ve({inputs:{x:p},backend:n,attrs:{shape:l}}),f=W4({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 Vre={kernelName:ho,backendName:"webgl",kernelFunc:G4};function Ure(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{numSamples:a,seed:o,normalized:i}=s,l=i?r:G4({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),c=l.shape[0],u=l.shape[1],d=new Mre(c,u,a),p=[[o]],h=n.runWebGLProgram(d,[l],"int32",p);return i||n.disposeIntermediateTensorInfo(l),h}var Gre={kernelName:Mh,backendName:"webgl",kernelFunc:Ure},H4="return -x;";function Hre(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.texData.get(s.dataId),[o,i]=vJ(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r;return K().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new uc(s.shape,H4):r=new Uo(s.shape,H4),n.runWebGLProgram(r,[s],s.dtype)}var jre={kernelName:Ni,backendName:"webgl",kernelFunc:Hre},qre=Ks.nonMaxSuppressionV3Impl;function Xre(e){E.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}=qre(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Kre={kernelName:Ri,backendName:"webgl",kernelFunc:Xre},Zre=Ks.nonMaxSuppressionV4Impl;function Yre(e){E.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}=Zre(u,d,o,i,l,c);return[n.makeTensorInfo([p.length],"int32",new Int32Array(p)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var Jre={kernelName:vu,backendName:"webgl",kernelFunc:Yre},Qre=Ks.nonMaxSuppressionV5Impl;function eae(e){E.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:A}=Qre(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var tae={kernelName:$i,backendName:"webgl",kernelFunc:eae},nae=class{constructor(e,t,n,s){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${s}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},sae=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 nae(l,a,o,i),u=ve({inputs:{x:r},backend:n,attrs:{shape:[l]}}),d=n.runWebGLProgram(c,[u],r.dtype);n.disposeIntermediateTensorInfo(u);let p=[...r.shape,a],h=ve({inputs:{x:d},backend:n,attrs:{shape:p}});return n.disposeIntermediateTensorInfo(d),h},rae={kernelName:Di,backendName:"webgl",kernelFunc:sae};function qm(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=gp({inputs:{input:s},backend:n}),a=qm({inputs:{x:r},backend:n}),o=jm({inputs:{input:s},backend:n}),i=qm({inputs:{x:o},backend:n}),l=Go({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Ap({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var aae={kernelName:Zi,backendName:"webgl",kernelFunc:qm};function j4(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=gp({inputs:{input:s},backend:n}),a=j4({inputs:{x:r},backend:n}),o=jm({inputs:{input:s},backend:n}),i=qm({inputs:{x:o},backend:n}),l=Go({inputs:{real:a,imag:i},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(a),n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(i),l}else return Ap({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var oae={kernelName:_i,backendName:"webgl",kernelFunc:j4};function iae(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return fx({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=fx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=S4({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeIntermediateTensorInfo(u)),c}var lae={kernelName:Pi,backendName:"webgl",kernelFunc:iae},uae=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=yt(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}));
|
|
}
|
|
}
|
|
`}},cae=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=yt(s),a=t.map(f=>f[0]).join(","),o=t.map((f,m)=>f[0]+e[m]).join(","),i=Hn("rc",s),l=Hn("source",s),c=`${i[s-1]} < ${this.outputShape[s-1]}`,u=s===1?"source":`vec2(${l.slice(-2).join()})`,d=[`${r} rc = outputLoc;`,`${i[s-1]} += 1;
|
|
if(${c}) {
|
|
`,s===1?"":`}
|
|
rc = outputLoc;
|
|
${i[s-2]} += 1;
|
|
if(${i[s-2]} < ${this.outputShape[s-2]}) {`,s===1?"":` ${i[s-1]} += 1;
|
|
if(${c}) {`],p=s===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let f=0,m=s===1?2:4;f<m;f++)h+=`
|
|
${d[f]}
|
|
if (${p}) {
|
|
result[${f}] = float(value);
|
|
} else {
|
|
${r} source = rc - start;
|
|
result[${f}] = getChannel(getX(${l.join()}), ${u});
|
|
}
|
|
`;h+=s===1?"} ":"}}",this.userCode=`
|
|
const ${r} start = ${r}(${a});
|
|
const ${r} end = ${r}(${o});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}},q4=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 Ap({backend:n,attrs:{shape:c,value:o,dtype:r.dtype}})}let i=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new cae(r.shape,a,o):new uae(r.shape,a,o),l=[[o]];return n.runWebGLProgram(i,[r],r.dtype,l)},dae={kernelName:to,backendName:"webgl",kernelFunc:q4},pae=`
|
|
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);
|
|
`,hae=`
|
|
// 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));
|
|
`+Lm+`
|
|
return result;
|
|
`,fae=Tn({opSnippet:pae,packedOpSnippet:hae}),mae={kernelName:no,backendName:"webgl",kernelFunc:fae};function gae(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=E.getAxesPermutation(u,i),p=r;d!=null&&(p=jn({inputs:{x:r},backend:n,attrs:{perm:d}}),u=E.getInnerMostAxes(u.length,i),l.push(p)),E.assertAxesAreInnerMostDims("prod",u,i);let h;if(n.shouldExecuteOnCPU([p])){let f=n.texData.get(p.dataId).values,{outVals:m,outShape:g,outDtype:A}=kJ(p.shape,p.dtype,f,u);h=n.makeTensorInfo(g,A,m)}else{let[f,m]=E.computeOutAndReduceShapes(p.shape,u),g=v.sizeFromShape(m),A=ve({inputs:{x:p},backend:n,attrs:{shape:[-1,g]}}),x=wd(r.dtype),y=Rl(A,x,"prod",n);h=ve({inputs:{x:y},backend:n,attrs:{shape:f}}),l.push(A),l.push(y)}if(o){l.push(h);let f=E.expandShapeToKeepDim(h.shape,c);h=ve({inputs:{x:h},backend:n,attrs:{shape:f}})}return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),h}var Aae={kernelName:Fi,backendName:"webgl",kernelFunc:gae},X4=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=SJ(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},yae={kernelName:wu,backendName:"webgl",kernelFunc:X4},xae="return 1.0 / x;",bae=st({opSnippet:xae}),vae={kernelName:ku,backendName:"webgl",kernelFunc:bae},wae=yr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,kae=`
|
|
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;
|
|
`,Sae=st({opSnippet:wae,packedOpSnippet:kae}),Iae={kernelName:ro,backendName:"webgl",kernelFunc:Sae},Cae=yr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Tae=`
|
|
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;
|
|
`,Nae=st({opSnippet:Cae,packedOpSnippet:Tae}),Eae={kernelName:oo,backendName:"webgl",kernelFunc:Nae},Rae=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 = ${d};
|
|
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorRC = ivec2(max(sourceFracIndexRC, vec2(0.0)));
|
|
ivec2 sourceCeilRC = ivec2(
|
|
min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));
|
|
|
|
float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);
|
|
float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);
|
|
float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);
|
|
float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);
|
|
|
|
vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracRC.y;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
|
|
float newValue = top + (bottom - top) * fracRC.x;
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},$ae=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 _ae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new $ae(r.shape,l,c,a,o):new Rae(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],"float32")}var Dae={kernelName:ao,backendName:"webgl",kernelFunc:_ae},Pae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${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 Fae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Pae(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Oae={kernelName:Lh,backendName:"webgl",kernelFunc:Fae},Mae=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);
|
|
}
|
|
`}},zae=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 < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function Lae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=K().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new zae(r.shape,l,c,a,o):new Mae(r.shape,l,c,a,o);return n.runWebGLProgram(u,[r],r.dtype)}var Bae={kernelName:Su,backendName:"webgl",kernelFunc:Lae},Wae=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,s,r]=t,[,a,o]=e,i=[n&&a>1?s-1:s,n&&o>1?r-1:r],l=[n&&a>1?a-1:a,n&&o>1?o-1:o],c=i[0]/l[0],u=i[1]/l[1],d=1/c,p=1/u,h=Math.ceil(d)*2+2,f=Math.ceil(p)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${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),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function Vae(e){let{inputs:t,backend:n,attrs:s}=e,{images:r,dy:a}=t,{alignCorners:o}=s,i=new Wae(a.shape,r.shape,o);return n.runWebGLProgram(i,[a],a.dtype)}var Uae={kernelName:zh,backendName:"webgl",kernelFunc:Vae},Gae=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${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=yt(n);this.userCode=`
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${r}));
|
|
}
|
|
`}},Hae=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let s=Hn("rc",n),r=`${s[n-1]} + 1 < ${this.outputShape[n-1]}`,a=`${s[n-2]} + 1 < ${this.outputShape[n-2]}`,o=yt(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${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 = ${u(s.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function i(h){return d(h)}function l(h){return h[n-1]="("+h[n-1]+" + 1)",d(h)}function c(h){return h[n-2]="("+h[n-2]+" + 1)",d(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",d(h)}function d(h){let f=e.map((A,x)=>p(x,h)),m=f.join(","),g=f.slice(-2).join(",");return`getChannel(getX(${m}), vec2(${g}))`}function p(h,f){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${f[h]} - 1`:`${f[h]}`}}};function jae(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 ys({inputs:{x:r},backend:n});let l=K().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Hae(r.shape,i):new Gae(r.shape,i);return n.runWebGLProgram(l,[r],r.dtype)}var qae={kernelName:Mi,backendName:"webgl",kernelFunc:jae},Xae=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 < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},Kae={kernelName:Yi,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new Xae(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[[c,u,Math.sin(r),Math.cos(r)]];return i.runWebGLProgram(l,[s],s.dtype,d)}},Zae=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,Yae=st({opSnippet:Zae}),Jae={kernelName:zi,backendName:"webgl",kernelFunc:Yae},Qae="return inversesqrt(x);",eoe=st({opSnippet:Qae,cpuKernelImpl:IJ}),toe={kernelName:io,backendName:"webgl",kernelFunc:eoe},K4=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let i=yt(r.length),l=yt(a.length),c="";n===1?c="i":n===2&&(c="i, j");let u=`getIndices(${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 noe(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}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=ve({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=ve({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=n.makeTensorInfo([],"float32",new Float32Array([0])),g=new K4(l,i,h.shape.length,f.shape.length,u,p),A=n.runWebGLProgram(g,[f,h,m],f.dtype),x=ve({inputs:{x:A},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(m),x}var soe={kernelName:Li,backendName:"webgl",kernelFunc:noe},roe=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=yt(n);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
float cVal = getC(${s});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${r}));
|
|
} else {
|
|
setOutput(getB(${r}));
|
|
}
|
|
}
|
|
`}};function aoe(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new roe(s.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(o,[s,r,a],Ln(r.dtype,a.dtype))}var ooe={kernelName:Bi,backendName:"webgl",kernelFunc:aoe},ioe=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${E.SELU_SCALEALPHA};
|
|
float scale = ${E.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,loe=st({opSnippet:ioe}),uoe={kernelName:Iu,backendName:"webgl",kernelFunc:loe},Z4="return 1.0 / (1.0 + exp(-1.0 * x));",coe=st({opSnippet:Z4,packedOpSnippet:Z4,cpuKernelImpl:CJ}),doe={kernelName:uo,backendName:"webgl",kernelFunc:coe},poe=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,hoe=st({opSnippet:poe}),foe={kernelName:Cu,backendName:"webgl",kernelFunc:hoe},moe=u4+`
|
|
return sin(x);
|
|
`,goe=st({opSnippet:moe}),Aoe={kernelName:lo,backendName:"webgl",kernelFunc:goe},yoe=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,xoe=st({opSnippet:yoe}),boe={kernelName:Vi,backendName:"webgl",kernelFunc:xoe},voe=`
|
|
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;
|
|
`,woe=st({opSnippet:voe}),koe={kernelName:Tu,backendName:"webgl",kernelFunc:woe},Soe=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((A,x)=>A*x),l=[[0,0]];l.push(...o);for(let A=1+a.length;A<r.shape.length;++A)l.push([0,0]);let c=[],u=q4({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=E.getReshaped(u.shape,a,i,!1),p=E.getPermuted(d.length,a.length,!1),h=E.getReshapedPermuted(u.shape,a,i,!1),f=ve({inputs:{x:u},backend:n,attrs:{shape:d}}),m=jn({inputs:{x:f},backend:n,attrs:{perm:p}}),g=ve({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(A=>n.disposeIntermediateTensorInfo(A)),g},Ioe={kernelName:Ui,backendName:"webgl",kernelFunc:Soe};function Coe(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]=NJ(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 Toe={kernelName:Bh,backendName:"webgl",kernelFunc:Coe};function Noe(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]=EJ(i,s.shape,s.dtype,o,l);return[n.makeTensorInfo(u,s.dtype,c),n.makeTensorInfo([d.length],a.dtype,new Int32Array(d))]}var Eoe={kernelName:Wh,backendName:"webgl",kernelFunc:Noe};function Roe(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]=KI(o,s.shape,s.dtype,i,l,!0);return n.makeTensorInfo(u,s.dtype,c)}var $oe={kernelName:Vh,backendName:"webgl",kernelFunc:Roe};function _oe(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
|
|
${a.shape}`);let o=n.readSync(s.dataId),i=n.readSync(r.dataId),l=n.readSync(a.dataId),[c,u]=KI(o,s.shape,s.dtype,i,l);return n.makeTensorInfo(u,s.dtype,c)}var Doe={kernelName:Uh,backendName:"webgl",kernelFunc:_oe};function Poe(e){let{inputs:t,backend:n,attrs:s}=e,{sparseIndices:r,sparseValues:a,defaultValue:o}=t,{outputShape:i}=s,{sliceRank:l,numUpdates:c,strides:u,outputSize:d}=E.calculateShapes(a,r,i),p=!1,h=new K4(c,l,r.shape.length,a.shape.length,u,[d,1],p),f=n.runWebGLProgram(h,[a,r,o],a.dtype),m=ve({inputs:{x:f},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(f),m}var Foe={kernelName:cd,backendName:"webgl",kernelFunc:Poe};function Ooe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{numOrSizeSplits:a,axis:o}=s,i=v.parseAxisParam(o,r.shape)[0],l=E.prepareSplitSize(r,a,i),c=r.shape.length,u=new Array(c).fill(0),d=r.shape.slice();return l.map(p=>{let h=[...d];h[i]=p;let f=dc({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var Moe={kernelName:Gi,backendName:"webgl",kernelFunc:Ooe},Y4="return sqrt(x);",zoe=st({opSnippet:Y4,packedOpSnippet:Y4,cpuKernelImpl:RJ}),Loe={kernelName:co,backendName:"webgl",kernelFunc:zoe},Boe="return x * x;",Woe=st({opSnippet:Boe}),Voe={kernelName:Nu,backendName:"webgl",kernelFunc:Woe},J4="return (a - b) * (a - b);",Uoe=Tn({opSnippet:J4,packedOpSnippet:J4}),Goe={kernelName:fo,backendName:"webgl",kernelFunc:Uoe};function Hoe({inputs:e,attrs:t,backend:n}){let{x:s}=e,r=yr+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,a=new Uo(s.shape,r);return n.runWebGLProgram(a,[s],s.dtype)}var joe={kernelName:yo,backendName:"webgl",kernelFunc:Hoe},qoe=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let s=n.length,r=yt(n.length),a=yt(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 Xoe(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:A,begin:x,end:y,strides:b}=Ft.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=ve({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||A){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let C=Ft.computeOutShape(x,y,b),N=dc({inputs:{x:r},backend:n,attrs:{begin:x,size:C}});w=ve({inputs:{x:N},backend:n,attrs:{shape:f}}),n.disposeIntermediateTensorInfo(N)}else if(n.shouldExecuteOnCPU([r])){let N=n.readSync(r.dataId),R=ze(r.shape,r.dtype,N),F=$J(h,R,b,x);w=n.makeTensorInfo(f,r.dtype,F.values)}else{let N=new qoe(x,b,h);w=n.runWebGLProgram(N,[r],r.dtype)}let k=ve({inputs:{x:w},backend:n,attrs:{shape:f}});return n.disposeIntermediateTensorInfo(w),k}var Koe={kernelName:Hi,backendName:"webgl",kernelFunc:Xoe};function Zoe(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]=_J(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Yoe={kernelName:dd,backendName:"webgl",kernelFunc:Zoe};function Joe(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]=DJ(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 Qoe={kernelName:Gh,backendName:"webgl",kernelFunc:Joe};function eie(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=PJ(o,r);return n.makeTensorInfo(a.shape,"int32",i)}var tie={kernelName:Hh,backendName:"webgl",kernelFunc:eie},nie="return tan(x);",sie=st({opSnippet:nie}),rie={kernelName:ji,backendName:"webgl",kernelFunc:sie},aie=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,oie=st({opSnippet:aie}),iie={kernelName:go,backendName:"webgl",kernelFunc:oie},lie=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=yt(this.rank),r=uie(e);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function uie(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 Q4(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=OJ(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new lie(r.shape,a);return n.runWebGLProgram(o,[r],r.dtype)}var cie={kernelName:qr,backendName:"webgl",kernelFunc:Q4},die=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));
|
|
}
|
|
}
|
|
`}},pie=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 $l(e,t){t!==null&&e.disposeIntermediateTensorInfo(t)}function eC(e){let t=1;for(;t<e;)t*=2;return t}function hie(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{k:a,sorted:o}=s,i=K().getNumber("TOPK_LAST_DIM_CPU_HANDOFF_SIZE_THRESHOLD"),l=K().getNumber("TOPK_K_CPU_HANDOFF_THRESHOLD"),c=r.shape,u=c[c.length-1];if(n.shouldExecuteOnCPU([r])||u<i||a>l){let F=n.readSync(r.dataId),[_,P]=MJ(F,c,r.dtype,a,o);return[n.makeTensorInfo(_.shape,_.dtype,_.values),n.makeTensorInfo(P.shape,P.dtype,P.values)]}if(a===0)return c[c.length-1]=0,[n.makeTensorInfo(c,r.dtype,[]),n.makeTensorInfo(c,"int32",[])];if(u===1)return[r,Ap({attrs:{shape:c,dtype:"int32",value:0},backend:n})];let d=n.texData.get(r.dataId),p=d!==null&&d.isPacked,h=p?n.unpackTensor(r):r,m=v.sizeFromShape(c)/u,g=ve({inputs:{x:h},attrs:{shape:[m,u]},backend:n});p&&$l(n,h);let A=eC(a),x=eC(u),y=null,b=()=>y===null?[g,g]:[g,y],w=(F,_,P)=>{let T=b(),M=new die(P),j=[[u],[y===null?1:0],[Number.NEGATIVE_INFINITY],[F],[_]],z=y;y=n.runWebGLProgram(M,T,"int32",j),$l(n,z)};for(let F=1;F<A;F*=2){let _=F*2;for(let P=F;P>=1;P/=2)w(_,P,[m,x])}for(let F=x;F>A;F/=2){let _=b(),P=new pie([m,F/2]),M=[[u],[y===null?1:0],[A]],U=y;y=n.runWebGLProgram(P,_,"int32",M),$l(n,U);let j=A/2,z=j*2;for(let X=j;X>=1;X/=2)w(z,X,y.shape)}let k=y;y=dc({inputs:{x:y},backend:n,attrs:{begin:0,size:[m,a]}}),$l(n,k);let C=L4({inputs:{x:g,indices:y},backend:n,attrs:{axis:1,batchDims:1}});$l(n,g);let N=c.slice(0,-1);N.push(a),k=y,y=ve({inputs:{x:y},attrs:{shape:N},backend:n}),$l(n,k);let R=C;return C=ve({inputs:{x:C},attrs:{shape:N},backend:n}),$l(n,R),[C,y]}var fie={kernelName:qi,backendName:"webgl",kernelFunc:hie},mie=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 gie(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],A=new mie(d,p,o,i,l,g);return n.runWebGLProgram(A,[r,a],"float32")}var Aie={kernelName:Xi,backendName:"webgl",kernelFunc:gie};function yie(e){let{inputs:t,attrs:n,backend:s}=e,{axis:r}=n,{x:a}=t;rc(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}=zJ(o,r,a.shape,a.dtype);return[s.makeTensorInfo(l,a.dtype,i),s.makeTensorInfo([c.length],"int32",c)]}var xie={kernelName:jh,backendName:"webgl",kernelFunc:yie};function bie(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=dc({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),A=ve({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=A,d.push(g)}return d.forEach(m=>n.disposeIntermediateTensorInfo(m)),f}var vie={kernelName:Ki,backendName:"webgl",kernelFunc:bie},wie=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 kie(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=E.getAxesPermutation([c],i),d=r;u!=null&&(d=jn({inputs:{x:r},backend:n,attrs:{perm:u}}),l.push(d),c=E.getInnerMostAxes(1,i)[0]);let p=E.segment_util.computeOutShape(d.shape,c,o),h=v.sizeFromShape([d.shape[c]]),f=ve({inputs:{x:d},backend:n,attrs:{shape:[-1,h]}});l.push(f);let m=wd(r.dtype),g=(b,w,k,C,N)=>{let R=b.shape[0],F=b.shape[1],_=E.segment_util.segOpComputeOptimalWindowSize(F,N),P={windowSize:_,inSize:F,batchSize:R,numSegments:N},T=new wie(P,w),M=n.compileAndRun(T,[b,k],C);if(l.push(M),M.shape[1]===N)return M;let U=X4({backend:n,attrs:{start:0,stop:N,step:1,dtype:"float32"}}),j=Q4({inputs:{x:U},backend:n,attrs:{reps:[F/_]}});return l.push(U),l.push(j),g(M,w,j,C,N)},A=g(f,"unsortedSegmentSum",a,m,o),x=ve({inputs:{x:A},backend:n,attrs:{shape:p}}),y=x;if(u!=null){l.push(x);let b=E.getUndoAxesPermutation(u);y=jn({inputs:{x:y},backend:n,attrs:{perm:b}})}return l.forEach(b=>n.disposeIntermediateTensorInfo(b)),y}var Sie={kernelName:pd,backendName:"webgl",kernelFunc:kie},Iie=[Qse,nre,MQ,LQ,VQ,HQ,qQ,ZQ,JQ,eee,ree,oee,uee,pee,xee,mee,wee,Cee,See,Ree,_ee,Pee,zee,Hee,qee,Kee,tte,ste,ite,cte,yQ,mte,Ite,Tte,xte,$te,Dte,Ete,Ote,Lte,Vte,Gte,jte,Kte,tne,sne,Yte,one,une,dne,mne,xne,kne,Cne,Tne,Nne,Rne,_ne,Pne,One,zne,Vne,Hne,Xne,Zne,Qne,nse,ose,cse,AQ,pse,hte,mse,yse,vse,bQ,Ise,Ese,$se,zse,Fse,Vse,Hse,Kse,rre,pre,cre,gre,yre,bre,lre,wre,Sre,Nre,_re,Ore,Gre,IQ,jre,Kre,Jre,tae,Yee,rae,oae,lae,dae,mae,wQ,Aae,yae,Jee,Bre,vae,Eae,Iae,TQ,Dae,Oae,Bae,Uae,qae,Kae,Jae,toe,soe,ooe,uoe,doe,foe,Aoe,boe,Uee,Vre,koe,Ioe,Toe,Eoe,$oe,Doe,Foe,Moe,Loe,Voe,Goe,joe,Koe,Yoe,Qoe,tie,Wre,PQ,rie,iie,cie,fie,Aie,FQ,xie,vie,Sie,aae];for(let e of Iie)ar(e);var Br=K();Br.registerFlag("WEBGPU_DEFERRED_SUBMIT_BATCH_SIZE",()=>15);Br.registerFlag("WEBGPU_CPU_FORWARD",()=>!0);Br.registerFlag("WEBGPU_MATMUL_WORK_PER_THREAD",()=>4);Br.registerFlag("WEBGPU_USE_NAIVE_CONV2D",()=>!1);Br.registerFlag("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE",()=>!1);Br.registerFlag("WEBGPU_CONV_SEPARATE_IM2COL_SHADER",()=>!1);Br.registerFlag("WEBGPU_USE_LOW_POWER_GPU",()=>!1);Br.registerFlag("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD",()=>1e3);Br.registerFlag("WEBGPU_USE_PROFILE_TOOL",()=>!1);Br.registerFlag("WEBGPU_USE_IMPORT",()=>!1);function Cie(e,t){if(Math.max(...e)>3)throw new Error("Cannot symbolically compute strides for rank > 4 tensor.");let n=e.length,s=e.map(a=>`${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 Xm(e,t){return e==="float32"?t?"vec4<f32>":"f32":e==="int32"||e==="bool"?t?"vec4<i32>":"i32":e}function Km(){return`
|
|
[[stage(compute), workgroup_size(workGroupSizeX, workGroupSizeY, workGroupSizeZ)]]
|
|
`}function mx(){return`
|
|
${Km()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(global_invocation_id)]] globalId : vec3<u32>,
|
|
[[builtin(num_workgroups)]] numWorkgroups: vec3<u32>)
|
|
`}function Ho(){return`
|
|
${Km()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(global_invocation_id)]] globalId : vec3<u32>)
|
|
`}function nt(){return`
|
|
${mx()} {
|
|
let index = getGlobalIndex(globalId, localId, numWorkgroups);
|
|
`}function Tie(e,t,n,s=!1){let r=`
|
|
let workGroupSizeX = ${n.workGroupSize[0]}u;
|
|
let workGroupSizeY = ${n.workGroupSize[1]}u;
|
|
let workGroupSizeZ = ${n.workGroupSize[2]}u;`;if(s===!0){let h=sC(t.shape),f=`
|
|
[[block]] struct Matrix0 {
|
|
numbers: array<${Xm(t.dtype,n.isVec4)}>;
|
|
};
|
|
[[block]] struct Uniform {
|
|
size : i32;
|
|
numChannels : i32;
|
|
outShapeStrides : vec2<i32>;
|
|
dispatchSize : vec3<u32>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
|
|
[[group(0), binding(2)]] var<uniform> uniforms: Uniform;
|
|
`;return[tC,f,r,nC,h,n.getUserCode()].join(`
|
|
`)}let a=[],o="[[block]] struct Uniforms { NAN : f32; ";n.variableNames.forEach((h,f)=>{o+=`${h.charAt(0).toLowerCase()+h.slice(1)}Shape : ${wn(e[f].shape.length)}; `}),o+=`outShape : ${wn(t.shape.length)} ; `;let i=t.shape.length-1;o+=`
|
|
outShapeStrides: ${wn(i)}; `,n.size&&(o+="size : i32; "),n.uniforms&&(o+=n.uniforms),o+="};",a.push(o),n.atomic?a.push(`
|
|
[[block]] struct Matrix0 {
|
|
numbers: array<atomic<i32>>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, read_write> result : Matrix0;
|
|
`):a.push(`
|
|
[[block]] struct Matrix0 {
|
|
numbers: array<${Xm(t.dtype,n.isVec4)}>;
|
|
};
|
|
|
|
[[group(0), binding(0)]] var<storage, write> result : Matrix0;
|
|
`),n.variableNames.forEach((h,f)=>{a.push(`
|
|
[[block]] struct Matrix${1+f} {
|
|
numbers: array<${Xm(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]=Die(t.shape,n.dispatchLayout),u=sC(t.shape),d=[tC,a.join(`
|
|
`),nC,u,l,Nie(t.shape.length)];if(n.atomic||d.push(Eie(t.shape,t.dtype,n.isVec4)),c===t.shape.length){let h=e.map(f=>Rie(f,t.shape,n.isVec4,n.dispatchLayout.x.length===t.shape.length)).join(`
|
|
`);d.push(h)}return d.push(n.getUserCode()),d.join(`
|
|
`)}var tC=`
|
|
fn idiv(a: i32, b: i32, sign: f32) -> i32 {
|
|
var res: i32 = a / b;
|
|
let mod: i32 = a % b;
|
|
if (sign < 0. && mod != 0) {
|
|
res = res - 1;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
fn isNanCustom(val : f32) -> bool {
|
|
if (val > 0.0) {
|
|
return false;
|
|
}
|
|
if (val < 0.0) {
|
|
return false;
|
|
}
|
|
if (val == 0.0) {
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
fn isNanCustomVec4F32(val : vec4<f32>) -> vec4<f32> {
|
|
var res = vec4<f32> (0.0);
|
|
for (var i = 0u; i < 4u; i = i + 1u) {
|
|
if (isNanCustom(val[i])) {
|
|
res[i] = 1.0;
|
|
} else {
|
|
res[i] = 0.0;
|
|
}
|
|
}
|
|
return res;
|
|
}
|
|
|
|
// Checks whether coordinates lie within the bounds of the shape.
|
|
fn coordsInBounds4D(coord : vec4<i32>, shape : vec4<i32>) -> bool {
|
|
return all(coord >= vec4<i32>(0)) &&
|
|
all(coord < shape);
|
|
}
|
|
|
|
fn coordsInBounds3D(coord : vec3<i32>, shape : vec3<i32>) -> bool {
|
|
return all(coord >= vec3<i32>(0)) &&
|
|
all(coord < shape);
|
|
}
|
|
|
|
fn coordsInBounds2D(coord : vec2<i32>, shape : vec2<i32>) -> bool {
|
|
return all(coord >= vec2<i32>(0)) &&
|
|
all(coord < shape);
|
|
}
|
|
`,nC=`
|
|
fn getFlatIndex1D(coord : i32, shape : i32) -> i32 {
|
|
return coord;
|
|
}
|
|
|
|
fn getFlatIndex2D(coords : vec2<i32>, shape : vec2<i32>) -> i32 {
|
|
return i32(dot(vec2<f32>(coords), vec2<f32>(f32(shape.y), 1.0)));
|
|
}
|
|
|
|
fn getFlatIndex3D(coords : vec3<i32>, shape : vec3<i32>) -> i32 {
|
|
return i32(dot(vec3<f32>(coords), vec3<f32>(f32(shape.y) * f32(shape.z), f32(shape.z), 1.0)));
|
|
}
|
|
|
|
fn getFlatIndex4D(coords : vec4<i32>, shape : vec4<i32>) -> i32 {
|
|
return i32(dot(vec4<f32>(coords), vec4<f32>(
|
|
f32(shape.y) * f32(shape.z) * f32(shape.w), f32(shape.z) * f32(shape.w), f32(shape.w), 1.0)));
|
|
}
|
|
|
|
// Only used when the y/z dimension of workgroup size is 1.
|
|
fn getGlobalIndex(globalId : vec3<u32>, localId : vec3<u32>, numWorkgroups: vec3<u32>) -> i32 {
|
|
if (numWorkgroups.y == 1u && numWorkgroups.z == 1u) {
|
|
return i32(globalId.x);
|
|
}
|
|
|
|
let localInvocationIndex = localId.z * workGroupSizeX * workGroupSizeY +
|
|
localId.y * workGroupSizeX + localId.x;
|
|
let workGroupID = (globalId - localId)/vec3<u32>(
|
|
workGroupSizeX, workGroupSizeY, workGroupSizeZ);
|
|
|
|
return i32((workGroupID.z * numWorkgroups.x * numWorkgroups.y +
|
|
workGroupID.y * numWorkgroups.x + workGroupID.x) *
|
|
(workGroupSizeX * workGroupSizeY * workGroupSizeZ) +
|
|
localInvocationIndex);
|
|
}
|
|
`;function Nie(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 i32(dot(vec2<f32>(coords), vec2<f32>(f32(uniforms.outShapeStrides), 1.0)));
|
|
}
|
|
`;break;case 3:t+=`
|
|
fn getOutputFlatIndex(coords : vec3<i32>) -> i32 {
|
|
return i32(dot(vec3<f32>(coords), vec3<f32>(f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), 1.0)));
|
|
}
|
|
`;break;case 4:t+=`
|
|
fn getOutputFlatIndex(coords : vec4<i32>) -> i32 {
|
|
return i32(dot(vec4<f32>(coords), vec4<f32>(
|
|
f32(uniforms.outShapeStrides.x), f32(uniforms.outShapeStrides.y), f32(uniforms.outShapeStrides.z), 1.0)));
|
|
}
|
|
`;break;default:v.assert(!1,()=>`Unsupported ${e}D shape`);break}return t}function Eie(e,t,n){let s=e.length,r=Xm(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}(${o.join(", ")}));
|
|
setOutputFlatI32(flatIndex, value);
|
|
}
|
|
`}return a}function Rie(e,t,n,s){let r=$ie(e,n);return e.shape.length<=t.length&&(r+=_ie(e,t,n,s)),r}function $ie(e,t){let n=e.name,s=e.shape.length,r=wn(s),a="get"+n.charAt(0).toUpperCase()+n.slice(1),o=["d0","d1","d2","d3"].slice(0,s),i=o.map(u=>`${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 _ie(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=E.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[${g+d}] = 0;`).join(`
|
|
`);let h="";if(l<2&&i>0)h="coords";else if(l>1){let g=wn(i),A=e.shape.map((x,y)=>`coords[${y+d}]`).join(", ");h=`${g}(${A})`}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 Die(e,t){let{x:n,y:s=[],z:r=[]}=t,a=e.length;if(n.length===a)return[`fn getOutputCoordsWithFlatDispatchLayout(globalId : vec3<u32>, localId : vec3<u32>, numWorkgroups: vec3<u32>) -> ${wn(a)}{
|
|
let globalIndex = getGlobalIndex(globalId, localId, numWorkgroups);
|
|
return getCoordsFromFlatIndex(globalIndex);
|
|
}
|
|
`,a];let o="",i=[n,s,r],l=0;for(let p=0;p<i.length;p++){let h=i[p];if(h.length!==0)if(l+=h.length,h.length===1)o+=`let d${h[0]} = i32(globalId[${p}]);`;else{let f=Cie(h,"uniforms.outShape");o+=`var index${p} = i32(globalId[${p}]);`;for(let m=0;m<f.length;m++)o+=`let d${h[m]} = index${p} / ${f[m]};`,m===f.length-1?o+=`let d${h[m+1]} = index${p} - d${h[m]} * ${f[m]};`:o+=`index${p} = index${p} - d${h[m]} * ${f[m]};`}}let c=[];for(let p=0;p<l;p++)c.push(`d${p}`);let u=wn(l),d=`fn getOutputCoordsWithNonFlatDispatchLayout(globalId : vec3<u32>) -> ${u} {
|
|
${o}
|
|
`;return c.length===0?d+=`return ${u}(0); }`:d+=`return ${u}(${c.join(",")}); }`,[d,l]}function sC(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 rC={};Oe(rC,{ArrayBufferToTypedArray:()=>aC,GPUBytesPerElement:()=>xx,computeDispatch:()=>Fe,computeWorkGroupSizeForConv2d:()=>gx,computeWorkGroupSizeForMatMul:()=>Ax,computeWorkPerThreadForConv2d:()=>yx,flatDispatchLayout:()=>je,isWebGPUSupported:()=>bx,tilesFitEvenlyIntoShape:()=>aa});var fc=65535,_l=e=>{let t=1;for(let n=0;n<e.length;n++)t*=e[n];return t};function aa(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 Fe(e,t,n=[1,1,1],s=[1,1,1]){let[r,a,o]=[Math.ceil(_l(e.x.map(l=>t[l]))/(n[0]*s[0])),e.y?Math.ceil(_l(e.y.map(l=>t[l]))/(n[1]*s[1])):1,e.z?Math.ceil(_l(e.z.map(l=>t[l]))/(n[2]*s[2])):1];if(r<=fc&&a<=fc&&o<=fc)return[r,a,o];v.assert(r>fc&&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>fc?(i=Math.ceil(Math.cbrt(r)),v.assert(i<=fc,()=>"Total dispatch size exceeds WebGPU maximum."),[i,i,i]):[i,i,1]}function gx(e,t){let n=_l(e.x.map(r=>t[r])),s=_l(e.y.map(r=>t[r]));return n<=4?[4,16,1]:s<=4?[16,4,1]:[16,16,1]}function Ax(e,t,n){return e===1?[32,1,1]:n===1?[1,32,1]:[8,8,1]}function yx(e,t){let n=_l(e.x.map(r=>t[r])),s=_l(e.y.map(r=>t[r]));return n<=4?[1,2,1]:s<=4?[2,1,1]:[2,2,1]}function je(e){return{x:e.map((t,n)=>n)}}function xx(e){if(e==="float32"||e==="int32"||e==="bool"||e==="string")return 4;if(e==="complex64")return 8;throw new Error(`Unknown dtype ${e}`)}function aC(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 bx(){return!!navigator.gpu}var Vt;(function(e){e[e.MUL=0]="MUL",e[e.ADD=1]="ADD",e[e.SUB=2]="SUB",e[e.DIV=3]="DIV",e[e.EQUAL=4]="EQUAL",e[e.GREATER=5]="GREATER",e[e.GREATER_EQUAL=6]="GREATER_EQUAL",e[e.LESS=7]="LESS",e[e.LESS_EQUAL=8]="LESS_EQUAL",e[e.LOGICAL_AND=9]="LOGICAL_AND",e[e.NOT_EQUAL=10]="NOT_EQUAL",e[e.SQUARED_DIFFERENCE=11]="SQUARED_DIFFERENCE",e[e.INT_DIV=12]="INT_DIV",e[e.POW=13]="POW",e[e.PRELU=14]="PRELU",e[e.MAX=15]="MAX",e[e.MIN=16]="MIN",e[e.COMPLEX_MULTIPLY_REAL=17]="COMPLEX_MULTIPLY_REAL",e[e.COMPLEX_MULTIPLY_IMAG=18]="COMPLEX_MULTIPLY_IMAG"})(Vt||(Vt={}));var Pie="return a + b;",Fie="return areal * breal - aimag * bimag;",Oie="return areal * bimag + aimag * breal;",Mie="return a / b;",zie="return a * b;",Lie="return (a - b) * (a - b);",Bie="return a - b;",Wie="return f32(a == b);",Vie="return vec4<f32>(a == b);",Uie="return f32(a > b);",Gie="return vec4<f32>(a > b);",Hie="return f32(a >= b);",jie="return vec4<f32>(a >= b);",qie="return f32(a < b);",Xie="return vec4<f32>(a < b);",Kie="return f32(a <= b);",Zie="return vec4<f32>(a <= b);",Yie="return f32(f32(a) >= 1.0 && f32(b) >= 1.0);",Jie=`return (vec4<f32>(a >= vec4<f32>(1.0)) *
|
|
vec4<f32>(b >= vec4<f32>(1.0)));`,Qie=`
|
|
if (isNanCustom(a)) { return a; }
|
|
if (isNanCustom(b)) { return b; }
|
|
`,oC=`
|
|
if (isNaN.r > 0.) {
|
|
resultTemp.r = uniforms.NAN;
|
|
}
|
|
if (isNaN.g > 0.) {
|
|
resultTemp.g = uniforms.NAN;
|
|
}
|
|
if (isNaN.b > 0.) {
|
|
resultTemp.b = uniforms.NAN;
|
|
}
|
|
if (isNaN.a > 0.) {
|
|
resultTemp.a = uniforms.NAN;
|
|
}
|
|
`,ele=`
|
|
let s = sign(a) * sign(b);
|
|
let ia = i32(round(a));
|
|
let ib = i32(round(b));
|
|
return f32(idiv(ia, ib, s));
|
|
`,tle=`
|
|
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);
|
|
`,nle="return f32(a != b);",sle="return vec4<f32>(a != b);",rle=`
|
|
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);
|
|
`,ale=`
|
|
let isModRound1Bool = vec4<i32>(round(abs(b) % vec4<f32>(2.0))) == vec4<i32>(1);
|
|
let isModRound1 = vec4<f32>(isModRound1Bool);
|
|
let multiplier = sign(a) * isModRound1 + (vec4<f32>(1.0) - isModRound1);
|
|
var resultTemp = multiplier * pow(abs(a), b);
|
|
|
|
// Ensure that a^0 = 1, including 0^0 = 1 as this correspond to TF and JS
|
|
let isExpZero = b == vec4<f32>(0.0);
|
|
if (isExpZero.r) {
|
|
resultTemp.r = 1.0;
|
|
}
|
|
if (isExpZero.g) {
|
|
resultTemp.g = 1.0;
|
|
}
|
|
if (isExpZero.b) {
|
|
resultTemp.b = 1.0;
|
|
}
|
|
if (isExpZero.a) {
|
|
resultTemp.a = 1.0;
|
|
}
|
|
let isNaN = vec4<f32>(a < vec4<f32>(0.0)) * vec4<f32>(floor(b) < b);
|
|
${oC}
|
|
return resultTemp;
|
|
`,ole="if (a < 0.0) { return b * a; } return a;",ile=`
|
|
let aLessThanZero = vec4<f32>(a < vec4<f32>(0.0));
|
|
return (aLessThanZero * (b * a)) + ((vec4<f32>(1.0) - aLessThanZero) * a);
|
|
`;function iC(e,t){let n=t?oC:Qie;return t?`
|
|
var resultTemp = vec4<f32>(${e}(a, b));
|
|
let isNaN = min(vec4<f32>(isNanCustomVec4F32(a)) + vec4<f32>(isNanCustomVec4F32(b)), vec4<f32>(1.0));
|
|
`+n+`
|
|
return resultTemp;
|
|
`:n+`
|
|
return ${e}(a, b);
|
|
`}function yp(e,t){switch(e){case 0:return zie;case 1:return Pie;case 2:return Bie;case 3:return Mie;case 4:return t?Vie:Wie;case 5:return t?Gie:Uie;case 6:return t?jie:Hie;case 7:return t?Xie:qie;case 8:return t?Zie:Kie;case 9:return t?Jie:Yie;case 10:return t?sle:nle;case 11:return Lie;case 12:return t?tle:ele;case 14:return t?ile:ole;case 15:return iC("max",t);case 16:return iC("min",t);case 13:return t?ale:rle;case 17:return Fie;case 18:return Oie;default:throw new Error(`BinaryType ${e} is not implemented!`)}}var xt;(function(e){e[e.ABS=0]="ABS",e[e.CEIL=1]="CEIL",e[e.COS=2]="COS",e[e.COSH=3]="COSH",e[e.ELU=4]="ELU",e[e.EXP=5]="EXP",e[e.EXPM1=6]="EXPM1",e[e.FLOOR=7]="FLOOR",e[e.LINEAR=8]="LINEAR",e[e.LOG=9]="LOG",e[e.LOGICAL_NOT=10]="LOGICAL_NOT",e[e.NEG=11]="NEG",e[e.PRELU=12]="PRELU",e[e.RELU=13]="RELU",e[e.RELU6=14]="RELU6",e[e.RSQRT=15]="RSQRT",e[e.SIN=16]="SIN",e[e.SINH=17]="SINH",e[e.SIGMOID=18]="SIGMOID",e[e.SQRT=19]="SQRT",e[e.SQUARE=20]="SQUARE",e[e.TANH=21]="TANH",e[e.TO_INT=22]="TO_INT"})(xt||(xt={}));var lle="return abs(a);",ule="return ceil(a);",cle="return cos(a);",dle=`
|
|
let e2x = exp(-a);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,ple="return exp(a) - 1.0;",hle="if (a >= 0.0) { return a; } return (exp(a) - 1.0);",fle=`
|
|
var resFloat = exp(a) - vec4<f32>(1.0);
|
|
if (a.r >= 0.0) {
|
|
resFloat.r = a.r;
|
|
}
|
|
if (a.g >= 0.0) {
|
|
resFloat.g = a.g;
|
|
}
|
|
if (a.b >= 0.0) {
|
|
resFloat.b = a.b;
|
|
}
|
|
if (a.a >= 0.0) {
|
|
resFloat.a = a.a;
|
|
}
|
|
return resFloat;
|
|
`,mle="return exp(a);",gle="return floor(a);",Ale="return a;",yle=`if (a < 0.0) { return 1.0/0.0; }
|
|
return log(a);`,xle="return f32(!(a >= 1.0));",ble="return -a;",vle="return (a < 0.0) ? b * a : a;",wle="return max(a, 0.0);",kle="return clamp(a, 0.0, 6.0);",Sle="return clamp(a, vec4<f32>(0.0, 0.0, 0.0, 0.0), vec4<f32>(6.0, 6.0, 6.0, 6.0));",Ile=`
|
|
var resFloat = a * vec4<f32>(a >= vec4<f32>(0.0));
|
|
let isNaN = isNan(a);
|
|
|
|
if (isNaN.r) {
|
|
resFloat.r = a.r;
|
|
}
|
|
if (isNaN.g) {
|
|
resFloat.g = a.g;
|
|
}
|
|
if (isNaN.b) {
|
|
resFloat.b = a.b;
|
|
}
|
|
if (isNaN.a) {
|
|
resFloat.a = a.a;
|
|
}
|
|
return resFloat;
|
|
`,Cle="return 1.0/sqrt(a);",Tle="return 1.0 / (1.0 + exp(-1.0 * a));",Nle="return sin(a);",Ele=`
|
|
let e2x = exp(a);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,Rle="return sqrt(a);",$le="return a * a;",_le=`
|
|
let e2x = exp(-2.0 * abs(a));
|
|
return sign(a) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,Dle="return f32(i32((a)));";function mc(e,t){switch(e){case 0:return lle;case 2:return cle;case 3:return dle;case 1:return ule;case 4:return t?fle:hle;case 5:return mle;case 6:return ple;case 7:return gle;case 8:return Ale;case 9:return yle;case 10:return xle;case 11:return ble;case 12:return vle;case 13:return t?Ile:wle;case 14:return t?Sle:kle;case 15:return Cle;case 18:return Tle;case 16:return Nle;case 17:return Ele;case 19:return Rle;case 20:return $le;case 21:return _le;case 22:return Dle;default:throw new Error(`BinaryType ${e} is not implemented!`)}}function oa(e,t=!1){if(e===null)return null;if(e==="linear")return mc(xt.LINEAR);if(e==="relu")return mc(xt.RELU,t);if(e==="elu")return mc(xt.ELU,t);if(e==="relu6")return mc(xt.RELU6,t);if(e==="prelu")return yp(Vt.PRELU,t);if(e==="sigmoid")return mc(xt.SIGMOID);throw new Error(`Activation ${e} has not been implemented for the WebGPU backend.`)}function lC(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};
|
|
|
|
${Ho()} {
|
|
|
|
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 Ple(e){return`
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
let tileSize = ${e[0]*4};
|
|
${Ho()} {
|
|
let tileCol = i32(localId.x);
|
|
let globalCol = i32(globalId.x);
|
|
let globalRow = i32(globalId.y);
|
|
|
|
let numTiles = (uniforms.dimInner - 1) / tileSize + 1;
|
|
|
|
// Without this initialization strange values show up in acc.
|
|
var acc = vec4<f32>(0.0);
|
|
|
|
// Loop over shared dimension.
|
|
for (var t = 0; t < numTiles; t = t + 1) {
|
|
// Load one tile of A into local memory.
|
|
let colA = t * tileSize / 4 + tileCol;
|
|
mm_Asub[tileCol] = mm_readA(globalRow, colA, globalId);
|
|
|
|
workgroupBarrier();
|
|
|
|
// Compute acc values for a single thread.
|
|
for (var k = 0; k < tileSize / 4; k = k + 1) {
|
|
let rowB = t * tileSize + k * 4;
|
|
let BCached0 = mm_readB(rowB, globalCol, globalId);
|
|
let BCached1 = mm_readB(rowB + 1, globalCol, globalId);
|
|
let BCached2 = mm_readB(rowB + 2, globalCol, globalId);
|
|
let BCached3 = mm_readB(rowB + 3, globalCol, globalId);
|
|
|
|
let ACached = mm_Asub[k];
|
|
acc = acc + BCached0 * ACached.x;
|
|
acc = acc + BCached1 * ACached.y;
|
|
acc = acc + BCached2 * ACached.z;
|
|
acc = acc + BCached3 * ACached.w;
|
|
}
|
|
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (globalRow < uniforms.dimAOuter && globalCol < uniforms.dimBOuter) {
|
|
mm_write(globalRow, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var Fle=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[16,16,1],this.isVec4=!0,this.vecSize=4,this.outputShape=t,this.workGroupSize=Ax(t[1],e[2],t[2]),this.dispatchLayout={x:[2],y:[1],z:[0]},t[1]===1&&(n=1),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.vecSize,n,1]);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.workPerThread=n,this.aShape=e,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`matMulPackedVec4_${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[aa(o,this.aShape.slice(1)),aa(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=oa(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?lC([this.vecSize,this.workPerThread,1],this.workGroupSize):Ple(this.workGroupSize)}
|
|
|
|
`}};function vx(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}>;
|
|
${Ho()} {
|
|
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 Ole(e){return`
|
|
let TileSize = ${e[0]*4};
|
|
var<workgroup> mm_Asub : array<vec4<f32>, ${e[0]}>;
|
|
|
|
${Ho()} {
|
|
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 uC=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=Ax(t[1],l,t[2]),(t[1]===1||t[2]===1)&&(n=1),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[n,n,1]),v.arraysEqual(this.dispatch,[1,1,1])&&(n=1,this.dispatch=Fe(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[aa(r,this.aShape.slice(1)),aa(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=oa(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?vx([this.workPerThread,this.workPerThread,1],this.workGroupSize):Ole(this.workGroupSize)}
|
|
`}};function Mle(){return`
|
|
var<workgroup> sumValues : array<f32, workGroupSizeX>;
|
|
${Ho()} {
|
|
let coords = getOutputCoordsWithNonFlatDispatchLayout(globalId);
|
|
let batch = coords[0];
|
|
let row = coords[1];
|
|
let col = coords[2];
|
|
var sum = 0.0;
|
|
let Length = uniforms.dimInner;
|
|
for (var k = i32(localId.x); k < Length; k = k + i32(workGroupSizeX)) {
|
|
let dataA = mm_readA(batch, row, k);
|
|
let dataB = mm_readB(batch, k, col);
|
|
sum = sum + dataA * dataB;
|
|
}
|
|
sumValues[localId.x] = sum;
|
|
workgroupBarrier();
|
|
|
|
for(var currentSize = workGroupSizeX / 2u; currentSize > 1u;
|
|
currentSize = currentSize / 2u) {
|
|
if (localId.x < currentSize)
|
|
{
|
|
sumValues[localId.x] = sumValues[localId.x] + sumValues[localId.x + currentSize];
|
|
}
|
|
workgroupBarrier();
|
|
}
|
|
|
|
if (localId.x == 0u) {
|
|
sum = sumValues[0] + sumValues[1];
|
|
mm_write(batch, row, col, sum);
|
|
}
|
|
}
|
|
`}var zle=class{constructor(e,t=!1,n=!1,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[256,1,1],this.outputShape=e,this.dispatchLayout={x:[],y:[1,2],z:[0]},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize);let o=s!=null,i=a!=null;o&&this.variableNames.push("bias"),i&&this.variableNames.push("preluActivationWeights"),this.transposeA=t,this.transposeB=n,this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulReduce_${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=oa(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);
|
|
}
|
|
${Mle()}
|
|
`}};function Lle(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.
|
|
${Ho()} {
|
|
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 >= ${t} && writeCol >= 0) {
|
|
mm_write(writeCol, globalCol, acc, globalId);
|
|
}
|
|
}
|
|
`}var Ble=class{constructor(e,t,n,s=null,r=null,a=null){this.variableNames=["A","B"],this.uniforms="dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.workGroupSize=[8,16,1],v.assert(e[1]<=16||t[2]<=16,()=>"This program can be only used when A width or B Height are small"),this.outputShape=n,this.dispatchLayout={x:[2],y:[1],z:[0]},this.dispatch=[Math.ceil(n[2]/this.workGroupSize[0]),Math.ceil(n[1]*2/this.workGroupSize[1]),n[0]];let o=s!=null;o&&this.variableNames.push("bias");let i=a!=null;i&&this.variableNames.push("preluActivationWeights"),this.addBias=o,this.activation=r,this.hasPreluActivationWeights=i,this.shaderKey=`matMulSmallOutputSize_${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=oa(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);
|
|
}
|
|
}
|
|
${Lle(this.workGroupSize)}
|
|
`}};function Ge(e){let{inputs:t,attrs:n}=e,{x:s}=t,{shape:r}=n,a=v.sizeFromShape(s.shape),o=v.inferFromImplicitShape(r,a),i=v.sizeFromShape(o);return v.assert(a===i,()=>`The new shape (${o}) has ${i} elements and the old shape (${s.shape}) has ${a} elements. The new shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Wle={kernelName:Oi,backendName:"webgpu",kernelFunc:Ge};function wx({a:e,b:t,transposeA:n,transposeB:s,backend:r,bias:a=null,preluActivationWeights:o=null,leakyreluAlpha:i=0,activation:l=null}){let c=e.shape.length,u=t.shape.length,d=n?e.shape[c-2]:e.shape[c-1],p=s?t.shape[u-1]:t.shape[u-2],h=n?e.shape[c-1]:e.shape[c-2],f=s?t.shape[u-2]:t.shape[u-1],m=e.shape.slice(0,-2),g=t.shape.slice(0,-2),A=v.sizeFromShape(m),x=v.sizeFromShape(g),b=sl.assertAndGetBroadcastShape(e.shape.slice(0,-2),t.shape.slice(0,-2)).concat([h,f]);v.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${s} must match.`);let w=n?[A,d,h]:[A,h,d],k=s?[x,f,p]:[x,p,f],C=Ge({inputs:{x:e},backend:r,attrs:{shape:w}}),N=Ge({inputs:{x:t},backend:r,attrs:{shape:k}}),R=[C,N],F=Math.max(A,x),_=d%4==0&&f%4==0&&!n&&!s&&f>=32,P;h*f<=32?P=new zle([F,h,f],n,s,a,l,o):!n&&!s&&(h<=16&&(f<=512||p>=2*f)||f<=16&&(h<=512||d>=2*h))?P=new Ble(w,k,[F,h,f],a,l,o):_?P=new Fle(w,[F,h,f],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),a,l,o):P=new uC(w,[F,h,f],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),n,s,a,l,o);let T=[C,N];a&&T.push(a),o&&T.push(o);let M=[{type:"int32",data:[h]},{type:"int32",data:[f]},{type:"int32",data:[d]}],U=r.runWebGPUProgram(P,T,e.dtype,M),j=Ge({inputs:{x:U},backend:r,attrs:{shape:b}});R.push(U);for(let z of R)r.disposeData(z.dataId);return j}function Vle(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a,bias:o,preluActivationWeights:i}=t,{transposeA:l,transposeB:c,activation:u,leakyreluAlpha:d}=s;return wx({a:r,b:a,transposeA:l,transposeB:c,backend:n,bias:o,preluActivationWeights:i,leakyreluAlpha:d,activation:u})}var Ule={kernelName:xo,backendName:"webgpu",kernelFunc:Vle},cC=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.workGroupSize=[128,1,1],this.size=!0,this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binaryOpComplex_${e}`,this.op=e}getUserCode(){return`
|
|
fn binaryOpComplex(
|
|
areal : f32, aimag : f32, breal : f32, bimag : f32) -> f32 {
|
|
${yp(this.op,!1)}
|
|
}
|
|
|
|
${nt()}
|
|
if(index < uniforms.size) {
|
|
let areal = getARealAtOutCoordsByGlobalIndex(index);
|
|
let aimag = getAImagAtOutCoordsByGlobalIndex(index);
|
|
let breal = getBRealAtOutCoordsByGlobalIndex(index);
|
|
let bimag = getBImagAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
}
|
|
`}},Gle=class{constructor(e,t,n,s){this.variableNames=["A","B"],this.size=!0;let r=256;this.workGroupSize=[r,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=je(this.outputShape),this.lastDimensionSize=s?n[0]:t[0],this.lastDimensionSize<256?this.workPerThread=1:this.lastDimensionSize<512?this.workPerThread=2:this.workPerThread=4,this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.useSharedMemoryWithB=s,this.op=e,this.shaderKey=`binaryShared_${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 {
|
|
${yp(this.op,!1)}
|
|
}
|
|
var<workgroup> sharedBuf : array<f32, ${this.lastDimensionSize}>;
|
|
${nt()}
|
|
|
|
// 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);
|
|
|
|
${t}
|
|
setOutputFlat(flatIndex, binaryOperation(a, b));
|
|
}
|
|
}
|
|
}
|
|
`}},Hle=class{constructor(e,t,n){this.variableNames=["A","B"],this.workPerThread=4,this.isVec4=!0,this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.op=e,this.shaderKey=`binaryVec4_${e}`}getUserCode(){return`
|
|
fn binaryOperation(a : vec4<f32>, b : vec4<f32>) -> vec4<f32> {
|
|
${yp(this.op,this.isVec4)}
|
|
}
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
let b = getBAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}},dC=class{constructor(e,t,n){this.variableNames=["A","B"],this.size=!0;let s=128;this.workGroupSize=[s,1,1],this.outputShape=E.assertAndGetBroadcastShape(t,n),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`binary_${e}`,this.op=e}getUserCode(){return`
|
|
fn binaryOperation(a : f32, b : f32) -> f32 {
|
|
${yp(this.op,!1)}
|
|
}
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
let b = getBAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, binaryOperation(a, b));
|
|
}
|
|
}
|
|
`}};function pC(e,t,n){if(v.arraysEqual(t,n)&&v.sizeFromShape(t)%4==0)return new Hle(e,t,n);let r=t.length===1&&n.length>1&&t[0]<1024,a=n.length===1&&t.length>1&&n[0]<1024;return r||a?new Gle(e,t,n,a):new dC(e,t,n)}function Qs(e){let{inputs:t}=e,{x:n}=t;return e.backend.incRef(n.dataId),{dataId:n.dataId,shape:n.shape,dtype:n.dtype}}var jle={kernelName:Ha,backendName:"webgpu",kernelFunc:Qs};function gc(e){let{inputs:t,backend:n}=e,{real:s,imag:r}=t,a=n.makeTensorInfo(s.shape,"complex64"),o=n.tensorMap.get(a.dataId),i=Qs({inputs:{x:s},backend:n}),l=Qs({inputs:{x:r},backend:n});return o.complexTensorInfos={real:i,imag:l},a}var qle={kernelName:ed,backendName:"webgpu",kernelFunc:gc},Zm=class{constructor(e,t){this.variableNames=["A"],this.size=!0;let n=128;this.workGroupSize=[n,1,1],this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.op=t,this.shaderKey=`unary_${t}`}getUserCode(){return`
|
|
fn unaryOperation(a : f32) -> f32 {
|
|
${mc(this.op,!1)}
|
|
}
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let a = getAAtOutCoordsByGlobalIndex(index);
|
|
setOutputFlat(index, unaryOperation(a));
|
|
}
|
|
}
|
|
`}};function Nn({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 Zm(a.shape,e);return o.runWebGPUProgram(l,[a],i)}}function qn({opSnippet:e,cpuKernelImpl:t,supportsComplex:n=!1,dtype:s}){return({inputs:r,backend:a})=>{let{a:o,b:i}=r,l=a;if(n&&o.dtype==="complex64"){let d=l.tensorMap.get(o.dataId),p=l.tensorMap.get(i.dataId),h,f;if(e!==Vt.MUL)[h,f]=[[d.complexTensorInfos.real,p.complexTensorInfos.real],[d.complexTensorInfos.imag,p.complexTensorInfos.imag]].map(g=>{let[A,x]=g,y={dataId:A.dataId,dtype:A.dtype,shape:o.shape},b={dataId:x.dataId,dtype:x.dtype,shape:i.shape},w=pC(e,o.shape,i.shape);return l.runWebGPUProgram(w,[y,b],Ln(A.dtype,x.dtype))});else{let g=new cC(Vt.COMPLEX_MULTIPLY_REAL,o.shape,i.shape),A=new cC(Vt.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(A,x,"float32")}let m=gc({inputs:{real:h,imag:f},backend:l});return l.disposeData(h.dataId),l.disposeData(f.dataId),m}let c=s||Ln(o.dtype,i.dtype);if((o.dtype==="string"||i.dtype==="string"||l.shouldExecuteOnCPU([o,i]))&&t!=null){let d=l.tensorMap.get(o.dataId).values,p=l.tensorMap.get(i.dataId).values,h=o.dtype==="string"?E.fromUint8ToStringArray(d):d,f=o.dtype==="string"?E.fromUint8ToStringArray(p):p,[m,g]=t(o.shape,i.shape,h,f,c);return l.makeTensorInfo(g,c,m)}let u=pC(e,o.shape,i.shape);return l.runWebGPUProgram(u,[o,i],c)}}var{addImpl:Xle,ceilImpl:Kle,concatImpl:Zle,equalImpl:Yle,expImpl:Jle,expm1Impl:Qle,floorImpl:eue,gatherNdImpl:tue,gatherV2Impl:nue,greaterEqualImpl:sue,greaterImpl:rue,lessEqualImpl:aue,lessImpl:oue,logImpl:iue,maxImpl:lue,maximumImpl:uue,minimumImpl:cue,multiplyImpl:due,negImpl:pue,notEqualImpl:hue,prodImpl:fue,rangeImpl:mue,rsqrtImpl:gue,simpleAbsImpl:Aue,sliceImpl:yue,stridedSliceImpl:xue,stringNGramsImpl:bue,subImpl:vue,tileImpl:wue,topKImpl:kue,transposeImpl:Sue,uniqueImpl:P2e}=vm,Iue=Nn({opType:xt.ABS,cpuKernelImpl:Aue}),Cue={kernelName:di,backendName:"webgpu",kernelFunc:Iue},Tue=qn({opSnippet:Vt.ADD,cpuKernelImpl:Xle,supportsComplex:!0}),Nue={kernelName:Hr,backendName:"webgpu",kernelFunc:Tue},Eue=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=je(this.outputShape),this.dispatch=Fe(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`
|
|
${nt()}
|
|
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 Rue(e){let{inputs:t,backend:n}=e,s=t;if(s.length===1)return Qs({inputs:{x:s[0]},backend:n});let r=s.map(i=>i.dtype).reduce((i,l)=>Ln(i,l)),a=s.map(i=>i.shape),o=new Eue(a);return n.runWebGPUProgram(o,s,r)}var $ue={kernelName:Ta,backendName:"webgpu",kernelFunc:Rue},hC=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="axis : i32;";let s=[t];E.assertAxesAreInnerMostDims("arg"+n.charAt(0).toUpperCase()+n.slice(1),s,e.length),this.op=n==="min"?"<":">";let[r,a]=E.computeOutAndReduceShapes(e,s);this.outputShape=r.length===0?[1]:r;let o=v.sizeFromShape(a);this.reductionFactor=2;let i=256,l=Math.min(Math.ceil(o/this.reductionFactor),i);this.workGroupSize=[l,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((c,u)=>u)},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.inputShape=e,this.shaderKey=`argMinMax${this.op}`}getUserCode(){let e=this.workGroupSize[0]>1,t=`
|
|
var<workgroup> xBestIndices : array<i32, ${this.workGroupSize[0]}>;
|
|
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
|
|
`,n=`
|
|
xBestIndices[localId.x] = bestIndex;
|
|
xBestValues[localId.x] = bestValue;
|
|
|
|
for(var currentSize = WorkGroupSize; currentSize > 1; currentSize = DIV_CEIL(currentSize, ${this.reductionFactor})) {
|
|
workgroupBarrier();
|
|
|
|
for (var w = 0; w < ${this.reductionFactor}; w = w + 1) {
|
|
let i = i32(localId.x) * ${this.reductionFactor} + w;
|
|
if (i < currentSize) {
|
|
let candidateIndex = xBestIndices[i];
|
|
let candidate = xBestValues[i];
|
|
if(candidate ${this.op} bestValue && !isNanCustom(candidate)) {
|
|
bestValue = candidate;
|
|
bestIndex = candidateIndex;
|
|
}
|
|
}
|
|
}
|
|
|
|
xBestIndices[localId.x] = bestIndex;
|
|
xBestValues[localId.x] = bestValue;
|
|
}
|
|
|
|
if (localId.x == 0u) {
|
|
setOutputFlatI32(flatOutputIndex, i32(bestIndex));
|
|
}
|
|
`,s=(o,i)=>this.outputShape.length===1?o:`${o}[${i}]`,r=o=>this.inputShape.length===1?"uniforms.xShape":`uniforms.xShape[${o}]`;return`
|
|
fn DIV_CEIL(a : i32, b : i32) -> i32 {
|
|
return ((a - 1) / b + 1);
|
|
}
|
|
|
|
let WorkGroupSize = ${this.workGroupSize[0]};
|
|
|
|
${e?t:""}
|
|
|
|
// 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(globalId : vec3<u32>) -> vec2<i32>{
|
|
let outputCoords = getOutputCoordsWithNonFlatDispatchLayout(globalId);
|
|
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 = ${r(`${this.inputShape.length} - r`)};
|
|
if (${this.inputShape.length} - r == uniforms.axis) {
|
|
inputStride = stride;
|
|
} else {
|
|
offset = offset + ${s("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;
|
|
}
|
|
|
|
${Ho()} {
|
|
let coordInfo = getInputCoordInfo(globalId);
|
|
|
|
var bestIndex = 0;
|
|
var bestValue = f32(x.numbers[getInputIndex(coordInfo, bestIndex)]);
|
|
|
|
let Length = ${r("uniforms.axis")};
|
|
let WorkPerThread = DIV_CEIL(Length, WorkGroupSize);
|
|
|
|
for (var w = 0; w < WorkPerThread; w = w + 1) {
|
|
let i = i32(globalId.x) * WorkPerThread + w;
|
|
if (i < Length) {
|
|
let candidate = f32(x.numbers[getInputIndex(coordInfo, i)]);
|
|
if (candidate ${this.op} bestValue && !isNanCustom(f32(candidate))) {
|
|
bestValue = candidate;
|
|
bestIndex = i;
|
|
}
|
|
}
|
|
}
|
|
|
|
let flatOutputIndex = i32(globalId.y);
|
|
${e?n:"setOutputFlatI32(flatOutputIndex, bestIndex);"}
|
|
}
|
|
`}},_ue=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=Fe(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]}>;
|
|
${Km()}
|
|
fn main([[builtin(local_invocation_id)]] localId : vec3<u32>,
|
|
[[builtin(workgroup_id)]] workgroupId : vec3<u32>) {
|
|
var x = i32(workgroupId.x) * TILE_DIM + i32(localId.x);
|
|
var y = i32(workgroupId.y) * TILE_DIM + i32(localId.y);
|
|
let width = uniforms.outShape[0];
|
|
let height = uniforms.outShape[1];
|
|
if (x < width && y < height) {
|
|
tile[localId.y][localId.x] =
|
|
A.numbers[y * width + x];
|
|
}
|
|
workgroupBarrier();
|
|
|
|
x = i32(workgroupId.y) * TILE_DIM + i32(localId.x);
|
|
y = i32(workgroupId.x) * TILE_DIM + i32(localId.y);
|
|
if (x < height && y < width) {
|
|
setOutputFlat((y * height + x), tile[localId.x]
|
|
[localId.y]);
|
|
}
|
|
}
|
|
`}},Due=class{constructor(e,t){this.variableNames=["A"],this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0;let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.newDim=t,this.shaderKey=`transpose_${t}`}getUserCode(){let e=wn(this.outputShape.length),t=Pue(this.newDim);return`
|
|
${nt()}
|
|
|
|
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 Pue(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 Dl(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=Sue(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 _ue(r.shape,a);return o.runWebGPUProgram(u,[r],r.dtype)}let c=new Due(r.shape,a);return o.runWebGPUProgram(c,[r],r.dtype)}var Fue={kernelName:Ao,backendName:"webgpu",kernelFunc:Dl};function Oue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Dl({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMax",[o[0]],l.shape.length);let u=new hC(l.shape,o[0],"max"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var Mue={kernelName:Na,backendName:"webgpu",kernelFunc:Oue};function zue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a}=s,o=v.parseAxisParam(a,r.shape),i=E.getAxesPermutation(o,r.shape.length),l=r,c=[];i!=null&&(l=Dl({inputs:{x:r},backend:n,attrs:{perm:i}}),c.push(l),o=E.getInnerMostAxes(o.length,l.shape.length)),E.assertAxesAreInnerMostDims("argMin",[o[0]],l.shape.length);let u=new hC(l.shape,o[0],"min"),d=[{type:"int32",data:[o[0]]}],p=n.runWebGPUProgram(u,[l],"int32",d);return c.forEach(h=>n.disposeData(h.dataId)),p}var Lue={kernelName:iu,backendName:"webgpu",kernelFunc:zue},fC=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=je(this.outputShape),this.dispatch=Fe(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"),`
|
|
${nt()}
|
|
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});
|
|
}
|
|
}
|
|
`}},mC=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=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="poolWithFilterSizeEqualsOne"}getUserCode(){return`
|
|
${nt()}
|
|
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 Bue(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=E.computePool2DInfo(r.shape,a,o,c,i,l);if(u.filterWidth===1&&u.filterHeight===1&&v.arraysEqual(u.inShape,u.outShape))return Qs({inputs:{x:r},backend:n});let d,p=[{type:"int32",data:[u.strideHeight,u.strideWidth]}];return u.filterHeight===1&&u.filterWidth===1?d=new mC(u):(d=new fC(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 Wue={kernelName:Ea,backendName:"webgpu",kernelFunc:Bue};function Vue(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;return wx({a:r,b:a,transposeA:o,transposeB:i,backend:n})}var Uue={kernelName:Ra,backendName:"webgpu",kernelFunc:Vue},Gue=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=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.start=e,this.uniforms=`start : ${wn(e.length)}; `,this.shaderKey="slice"}getUserCode(){let e=wn(this.rank),t=Hue(this.rank),n;return this.start.length===1?n=this.outputShape.map((r,a)=>"sourceLoc = uniforms.start + coords;"):n=this.outputShape.map((r,a)=>`sourceLoc.${kx[a]} = uniforms.start[${a}] + coords.${kx[a]};`),`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
var sourceLoc : ${e};
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
${n.join(`
|
|
`)}
|
|
setOutputFlat(index, getSource(${t}));
|
|
}
|
|
}
|
|
`}},kx=["x","y","z","w","u","v"];function Hue(e){if(e===1)return"sourceLoc";if(e<=6)return kx.slice(0,e).map(t=>`sourceLoc.${t}`).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}function Ac(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{begin:a,size:o}=s,[i,l]=Ft.parseSliceParams(r,a,o);if(Ft.assertParamsValid(r,i,l),n.shouldExecuteOnCPU([r])||r.dtype==="string"){let d=n.tensorMap.get(r.dataId),p=yue(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 Gue(i,l),u=[{type:"int32",data:i}];return n.runWebGPUProgram(c,[r],r.dtype,u)}var jue={kernelName:Wi,backendName:"webgpu",kernelFunc:Ac},que=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,y)=>x*y),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=[],f=Ge({inputs:{x:r},backend:n,attrs:{shape:l}}),m=Dl({inputs:{x:f},backend:n,attrs:{perm:c}}),g=Ge({inputs:{x:m},backend:n,attrs:{shape:u}}),A=Ac({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)),A},Xue={kernelName:pi,backendName:"webgpu",kernelFunc:que},gC=qn({opSnippet:Vt.NOT_EQUAL,dtype:"bool",cpuKernelImpl:hue}),Kue={kernelName:Ei,backendName:"webgpu",kernelFunc:gC};function xp(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Qs({inputs:{x:r.complexTensorInfos.real},backend:n})}var Zue={kernelName:ud,backendName:"webgpu",kernelFunc:xp};function Yue(e,t){let n=new Zm(e.shape,xt.TO_INT),s=t.runWebGPUProgram(n,[e],"int32");return{dataId:s.dataId,shape:s.shape,dtype:s.dtype}}function Sx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{dtype:a}=s;if(a==="complex64"){if(r.dtype==="complex64")return Qs({inputs:{x:r},backend:n});let o=Gt(r.shape),i=Sx({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),l=gc({inputs:{real:i,imag:o},backend:n});return o.dispose(),n.disposeData(i.dataId),l}if(r.dtype==="complex64"){let o=xp({inputs:{input:r},backend:n}),i=Sx({inputs:{x:o},backend:n,attrs:{dtype:a}});return n.disposeData(o.dataId),i}if(!v.hasEncodingLoss(r.dtype,a)){let o=Qs({inputs:{x:r},backend:n});return{dataId:o.dataId,shape:o.shape,dtype:a}}if(a==="int32")return Yue(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.disposeData(o.dataId),l}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${a}`)}var Jue={kernelName:$a,backendName:"webgpu",kernelFunc:Sx},Que=Nn({opType:xt.CEIL,cpuKernelImpl:Kle}),ece={kernelName:_a,backendName:"webgpu",kernelFunc:Que},tce=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=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shaderKey="clipVec4"}getUserCode(){return`
|
|
${nt()}
|
|
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);
|
|
}
|
|
}
|
|
`}},nce=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=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="clip"}getUserCode(){return`
|
|
${nt()}
|
|
if(index < uniforms.size) {
|
|
let value = getAAtOutCoordsByGlobalIndex(index);
|
|
if (isNanCustom(value)) {
|
|
setOutputFlat(index, value);
|
|
return;
|
|
}
|
|
setOutputFlat(index, clamp(value, uniforms.minVal, uniforms.maxVal));
|
|
}
|
|
}
|
|
`}};function sce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{clipValueMin:a,clipValueMax:o}=s,i,l=[{type:"float32",data:[a]},{type:"float32",data:[o]}];return v.sizeFromShape(r.shape)%4==0?i=new tce(r.shape):i=new nce(r.shape),n.runWebGPUProgram(i,[r],r.dtype,l)}var rce={kernelName:jr,backendName:"webgpu",kernelFunc:sce},ace=class{constructor(e){this.workPerThread=4,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=E.computeOutShape(e,1),this.variableNames=e.map((t,n)=>`T${n}`),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]),this.shapes=e,this.shaderKey=`concat${e}`}getUserCode(){let e=new Array(this.shapes.length-1),t=[];if(e.length>0){e[0]=this.shapes[0][1];for(let a=1;a<e.length;a++)e[a]=e[a-1]+this.shapes[a][1];t.push(`if (yC < ${e[0]}){ setOutput(coords.x, coords.y, getT0(yR, yC)); }`);for(let a=1;a<e.length;a++){let o=e[a-1];t.push(`elseif (yC < ${e[a]}){ setOutput(coords.x, coords.y, getT${a}(yR, yC - ${o})); }`)}let s=e.length,r=e[e.length-1];t.push(`else { setOutput(coords.x, coords.y, getT${s}(yR, yC - ${r})); }`)}else t.push("setOutput(coords.x, coords.y, getT0(yR, yC));");return`
|
|
${nt()}
|
|
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;
|
|
|
|
${t.join(`
|
|
`)}
|
|
}
|
|
}
|
|
}
|
|
`}};function Ym(e){let{inputs:t,backend:n}=e,{input:s}=t,r=n.tensorMap.get(s.dataId);return Qs({inputs:{x:r.complexTensorInfos.imag},backend:n})}var oce={kernelName:ad,backendName:"webgpu",kernelFunc:Ym};function Ix(e,t,n){let s=e[0].dtype;if(s==="complex64"){let u=e.map(m=>xp({inputs:{input:m},backend:n})),d=e.map(m=>Ym({inputs:{input:m},backend:n})),p=Ix(u,t,n),h=Ix(d,t,n),f=gc({inputs:{real:p,imag:h},backend:n});return u.forEach(m=>n.disposeData(m.dataId)),d.forEach(m=>n.disposeData(m.dataId)),n.disposeData(p.dataId),n.disposeData(h.dataId),f}let r=n.shouldExecuteOnCPU(e);if(s==="string"&&(r=!0),r){let u=e.map(A=>{let x=v.sizeFromShape(A.shape.slice(t));return Ge({inputs:{x:A},backend:n,attrs:{shape:[-1,x]}})}),d=u.map(A=>({vals:n.readSync(A.dataId),shape:A.shape})),p=E.computeOutShape(u.map(A=>A.shape),1),h=u[0].shape[0]===1,f=Zle(d,p,s,h),m=E.computeOutShape(e.map(A=>A.shape),t),g=n.makeTensorInfo(m,s,f);return u.forEach(A=>n.disposeData(A.dataId)),g}let{tensors2D:a,outShape:o}=ice(e,t,n),i=new ace(a.map(u=>u.shape)),l=n.runWebGPUProgram(i,a,a[0].dtype);a.forEach(u=>n.disposeData(u.dataId));let c=Ge({inputs:{x:l},backend:n,attrs:{shape:o}});return n.disposeData(l.dataId),c}function ice(e,t,n){let s=E.computeOutShape(e.map(a=>a.shape),t);return{tensors2D:e.map(a=>Ge({inputs:{x:a},backend:n,attrs:{shape:[v.sizeFromShape(a.shape.slice(0,t)),v.sizeFromShape(a.shape.slice(t))]}})),outShape:s}}function AC(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s,a=v.parseAxisParam(r,t[0].shape)[0],o=E.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 Qs({inputs:{x:i[0]},backend:n});let l=i.map(c=>c.shape);return E.assertParamsConsistent(l,a),Ix(i,a,n)}var lce={kernelName:hi,backendName:"webgpu",kernelFunc:AC},uce=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=je(this.outputShape),this.dispatch=Fe(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`
|
|
${nt()}
|
|
|
|
for(var i = 0; i<${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 yC({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=Ge({inputs:{x:e},backend:s,attrs:{shape:[1,p,n.inChannels]}}),f=Ge({inputs:{x:t},backend:s,attrs:{shape:[1,n.inChannels,n.outChannels]}}),m=wx({a:h,b:f,transposeA:u,transposeB:d,backend:s,bias:r,activation:i,preluActivationWeights:a,leakyreluAlpha:o}),g=Ge({inputs:{x:m},backend:s,attrs:{shape:n.outShape}});return s.disposeData(h.dataId),s.disposeData(f.dataId),s.disposeData(m.dataId),g}function cce({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:A,dataFormat:x}=n,y=x==="channelsLast",b=l*c*u,w=m*f,k=[w,b],C=!1,N=!1,R=[],F=Ge({inputs:{x:e},backend:s,attrs:{shape:e.shape.slice(1)}}),_=Ge({inputs:{x:t},backend:s,attrs:{shape:[1,b,-1]}});R.push(F),R.push(_);let P=new uce(k,y),T=[{type:"int32",data:[h.left,h.top]},{type:"int32",data:[d,p]},{type:"int32",data:[g,A]},{type:"int32",data:[f]},{type:"int32",data:[u*l]},{type:"int32",data:[u]}],M=s.runWebGPUProgram(P,[F],F.dtype,T),U=Ge({inputs:{x:M},backend:s,attrs:{shape:[1,k[0],k[1]]}});R.push(M),R.push(U);let j=[1,k[0],k[1]],z=new uC(j,[1,w,n.outChannels],K().get("WEBGPU_MATMUL_WORK_PER_THREAD"),C,N),X=j[1],Z=j[2],J=n.outChannels,ee=[{type:"int32",data:[X]},{type:"int32",data:[J]},{type:"int32",data:[Z]}],re=s.runWebGPUProgram(z,[U,_],U.dtype,ee),Q=y?[1,m,f,n.outChannels]:[1,n.outChannels,m,f],te=Ge({inputs:{x:re},backend:s,attrs:{shape:Q}});R.push(re);for(let oe of R)s.disposeData(oe.dataId);return te}var xC=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=Fe(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[aa(r,[o,l]),aa(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=lC([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=oa(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);
|
|
}
|
|
}
|
|
${t}
|
|
`}},bC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; dimAOuter : i32; dimBOuter : i32; dimInner : i32;",this.outputShape=e.outShape,v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),this.dispatchLayout={x:[3],y:[1,2],z:[0]},this.workGroupSize=gx(this.dispatchLayout,this.outputShape),this.elementsPerThread=yx(this.dispatchLayout,this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,this.elementsPerThread),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,[this.fitA,this.fitB]=this.getShapeFit(),this.shaderKey=`conv2DMM_${this.elementsPerThread}_${this.activation}_${this.fitA}_${this.fitB}`}getShapeFit(){let e=this.workGroupSize[1]*this.elementsPerThread[1],t=this.workGroupSize[0]*this.elementsPerThread[0],n=e>t?e:t;v.assert(n%this.workGroupSize[0]==0&&n%this.workGroupSize[1]==0,()=>"tileInner must be multiple of workgroupsize.x and workgroupsize.y");let s=[e,n],r=[n,t],a=this.outputShape[1]*this.outputShape[2],o=this.outputShape[3],i=this.convInfo.filterHeight*this.convInfo.filterWidth*this.convInfo.inChannels;return[aa(s,[a,i]),aa(r,[i,o])]}getUserCode(){let e=vx(this.elementsPerThread,this.workGroupSize),t=`
|
|
let outRow = row / uniforms.outShape[2];
|
|
let outCol = row % uniforms.outShape[2];
|
|
|
|
let WRow = col / (uniforms.filterDims[1] * uniforms.xShape[3]);
|
|
let WCol = col / uniforms.xShape[3] % uniforms.filterDims[1];
|
|
let coord = vec4<i32>(
|
|
batch,
|
|
outRow * uniforms.stride[0] + uniforms.dilation[0] * WRow - uniforms.pad[0],
|
|
outCol * uniforms.stride[1] + uniforms.dilation[1] * WCol - uniforms.pad[1],
|
|
col % uniforms.xShape[3]);
|
|
// The bounds checking is always needed since we use it to pad zero for the
|
|
// 'same' padding type.
|
|
if(coordsInBounds4D(coord, uniforms.xShape)) {
|
|
return x.numbers[getFlatIndex4D(coord, uniforms.xShape)];
|
|
}
|
|
return 0.0;`,n=this.fitA?`${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=oa(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;
|
|
}
|
|
${e}
|
|
`}},vC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="filterDims : vec2<i32>; pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>;",this.workGroupSize=[128,1,1],this.outputShape=e.outShape,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivationWeights=s,this.shaderKey=`conv2DNaive_${this.activation}`}getUserCode(){let e="",t="";if(this.activation){let r=oa(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);
|
|
}
|
|
}
|
|
|
|
${mx()} {
|
|
let coords = getOutputCoordsWithFlatDispatchLayout(globalId, localId, numWorkgroups);
|
|
let batch = coords[0];
|
|
let outChannel = coords[3];
|
|
|
|
var acc = 0.0;
|
|
|
|
for (var row = 0; row < uniforms.filterDims[0]; row = row + 1) {
|
|
for (var col = 0; col < uniforms.filterDims[1]; col = col + 1) {
|
|
for (var xChannel = 0; xChannel < uniforms.xShape[3]; xChannel = xChannel + 1) {
|
|
let coordRow = coords[1] * uniforms.stride[0] + uniforms.dilation[0] * row - uniforms.pad[0];
|
|
let coordCol = coords[2] * uniforms.stride[1] + uniforms.dilation[1] * col - uniforms.pad[1];
|
|
let v = readInp(batch, coordRow, coordCol, xChannel);
|
|
let f = readFilt(row, col, xChannel, outChannel);
|
|
acc = acc + v * f;
|
|
}
|
|
}
|
|
}
|
|
|
|
writeResult(batch, coords[1], coords[2], outChannel, acc);
|
|
}
|
|
`}};function dce(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=E.convertConv2DDataFormat(l),p=E.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 yC({x:r,filter:a,convInfo:p,backend:s});if(K().getBool("WEBGPU_CONV_SEPARATE_IM2COL_SHADER")&&r.shape[0]===1)return cce({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=K().getBool("WEBGPU_USE_NAIVE_CONV2D");if(g?h=new vC(p):(p.inChannels%4==0||p.inChannels===3&&p.padInfo.type==="VALID")&&p.outChannels%4==0&&p.outChannels>=64?h=new xC(p):h=new bC(p),!g){let A=p.outShape[1]*p.outShape[2],x=p.outShape[3],y=p.filterHeight*p.filterWidth*p.inShape[3];m.push({type:"int32",data:[A]},{type:"int32",data:[x]},{type:"int32",data:[y]})}return s.runWebGPUProgram(h,[r,a],r.dtype,m)}var pce={kernelName:Da,backendName:"webgpu",kernelFunc:dce},hce=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=gx(this.dispatchLayout,this.outputShape),this.elementsPerThread=yx(this.dispatchLayout,this.outputShape),this.dispatch=Fe(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;
|
|
}
|
|
|
|
${vx(this.elementsPerThread,this.workGroupSize)}
|
|
`}},fce=class{constructor(e){this.variableNames=["dy","W"],this.uniforms="filterDims : vec2<i32>; pads : vec2<i32>; stride : vec2<i32>; outBackprop : vec4<i32>;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e.inShape,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.isChannelsLast=e.dataFormat==="channelsLast",this.shaderKey=`conv2DDerInput_${this.isChannelsLast}`}getUserCode(){let e=this.isChannelsLast?1:2,t=this.isChannelsLast?2:3,n=this.isChannelsLast?3:1;return`
|
|
${nt()} {
|
|
if(index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let batch = coords[0];
|
|
let d1 = coords[${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 (${this.isChannelsLast}) {
|
|
let xValue = getDy(batch, idyR, idyC, d2);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
} else {
|
|
let xValue = getDy(batch, d2, idyR, idyC);
|
|
let wValue = getW(wRPerm, wCPerm, d1, d2);
|
|
dotProd = dotProd + xValue * wValue;
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
setOutputFlat(index, dotProd);
|
|
}
|
|
}
|
|
`}};function mce(e){let{inputs:t,backend:n,attrs:s}=e,{dy:r,filter:a}=t,{inputShape:o,strides:i,pad:l,dataFormat:c,dimRoundingMode:u}=s,d=E.convertConv2DDataFormat(c),p=E.computeConv2DInfo(o,a.shape,i,1,l,u,!1,d),h=[{type:"int32",data:[p.filterHeight,p.filterWidth]},{type:"int32",data:[p.filterHeight-1-p.padInfo.top,p.filterWidth-1-p.padInfo.left]},{type:"int32",data:[p.strideHeight,p.strideWidth]},{type:"int32",data:[p.batchSize,p.outHeight,p.outWidth,p.outChannels]}],f;if(K().getBool("WEBGPU_USE_NAIVE_CONV2D_TRANSPOSE"))f=new fce(p);else{f=new hce(p);let m=p.inShape[1]*p.inShape[2],g=p.inShape[3],A=p.filterHeight*p.filterWidth*p.outChannels;h.push({type:"uint32",data:[m]},{type:"uint32",data:[g]},{type:"uint32",data:[A]})}return n.runWebGPUProgram(f,[r,a],"float32",h)}var gce={kernelName:Pa,backendName:"webgpu",kernelFunc:mce},Ace=Nn({opType:xt.COS}),yce={kernelName:Fa,backendName:"webgpu",kernelFunc:Ace},xce=Nn({opType:xt.COSH}),bce={kernelName:Oa,backendName:"webgpu",kernelFunc:xce},vce=class{constructor(e,t,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.uniforms="extrapolationValue : f32;",this.workGroupSize=[64,1,1],this.size=!0;let[r]=t;this.outputShape=[r,n[0],n[1],e],this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.methodId=s==="bilinear"?1:0,this.cropHeightBiggerThan1=this.outputShape[1]>1,this.cropWidthBiggerThan1=this.outputShape[2]>1,this.shaderKey=`cropAndResize_${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`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let height_ratio = f32(${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);
|
|
}
|
|
}
|
|
}
|
|
`}},wce=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 vce(r.shape[3],a.shape,i,l),d=[{type:"float32",data:[c]}];return n.runWebGPUProgram(u,[r,a,o],"float32",d)},kce={kernelName:mi,backendName:"webgpu",kernelFunc:wce},Sce=class{constructor(e,t){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.uniforms="blockSize : i32;",this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey=`depthToSpace_${t}`,this.dataFormat=t}getUserCode(){return`
|
|
${nt()}
|
|
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 = ${this.getInputSamplingString()};
|
|
setOutputFlat(index, rlt);
|
|
}
|
|
}`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?"uniforms.outShape[3]":"uniforms.outShape[1]"}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function Ice(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockSize:a,dataFormat:o}=s,i=r.shape[0],l=o==="NHWC"?r.shape[1]:r.shape[2],c=o==="NHWC"?r.shape[2]:r.shape[3],u=o==="NHWC"?r.shape[3]:r.shape[1],d=l*a,p=c*a,h=u/(a*a),f=o==="NHWC"?[i,d,p,h]:[i,h,d,p],m=[{type:"int32",data:[a]}],g=new Sce(f,o);return n.runWebGPUProgram(g,[r],r.dtype,m)}var Cce={kernelName:gi,backendName:"webgpu",kernelFunc:Ice},wC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[4,4,4],this.isVec4=!0,this.outputShape=e.outShape,this.dispatchLayout={x:[0,1],y:[2],z:[3]},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[1,4,4]),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise3x3_${n}`}getUserCode(){let e="",t="";if(this.activation){let r=oa(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}
|
|
|
|
${Km()}
|
|
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}
|
|
${t}
|
|
setOutput(coords[0], coords[1], coords[2], coords[3], dotProd[i]);
|
|
}
|
|
}
|
|
}
|
|
`}},kC=class{constructor(e,t=!1,n=null,s=!1){this.variableNames=["x","W"],this.uniforms="pad : vec2<i32>; stride : vec2<i32>; dilation : vec2<i32>; inDims : vec2<i32>;",this.workGroupSize=[256,1,1],this.outputShape=e.outShape,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),v.assert(e.dataFormat==="channelsLast",()=>"TODO: NCHW is unimplemented"),t&&this.variableNames.push("bias"),s&&this.variableNames.push("preluActivationWeights"),this.convInfo=e,this.addBias=t,this.activation=n,this.hasPreluActivation=s,this.shaderKey=`depthwise_${this.convInfo.filterHeight}_${this.convInfo.filterWidth}_${this.activation}_${this.convInfo.outChannels/this.convInfo.inChannels}`}getUserCode(){let e=this.convInfo.outChannels/this.convInfo.inChannels,t="",n="";if(this.activation){let a=oa(this.activation,!1);this.hasPreluActivation?t=`fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
|
|
let b = getPreluActivationWeightsAtOutCoordsByCoords(outCoord);
|
|
${a}
|
|
}`:t=`
|
|
fn activation(a : f32, outCoord : vec4<i32>) -> f32 {
|
|
${a}
|
|
}
|
|
`,n="dotProd = activation(dotProd, coords);"}let s=this.addBias?"dotProd = dotProd + getBiasAtOutCoordsByCoords(coords);":"";return`
|
|
${t}
|
|
|
|
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);
|
|
}
|
|
}
|
|
|
|
${mx()} {
|
|
let coords = getOutputCoordsWithFlatDispatchLayout(globalId, localId, numWorkgroups);
|
|
let batch = coords[0];
|
|
let xRCCorner = vec2<i32>(coords.yz) * uniforms.stride - uniforms.pad;
|
|
let d2 = coords[3];
|
|
let d1 = d2 / ${e};
|
|
let q = d2 - d1 * ${e};
|
|
|
|
let inputRowStart = xRCCorner.x;
|
|
let inputColStart = xRCCorner.y;
|
|
let inputRowEnd = inputRowStart + ${this.convInfo.filterHeight} * uniforms.dilation[0];
|
|
let inputColEnd = inputColStart + ${this.convInfo.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 < ${this.convInfo.filterHeight}; wR = wR + 1) {
|
|
let xR = inputRowStart + wR * uniforms.dilation[0];
|
|
|
|
for (var wC = 0; wC < ${this.convInfo.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 < ${this.convInfo.filterHeight}; wR = wR + 1) {
|
|
let xR = inputRowStart + wR * uniforms.dilation[0];
|
|
|
|
if (xR < 0 || xR >= uniforms.inDims[0]) {
|
|
continue;
|
|
}
|
|
|
|
for (var wC = 0; wC < ${this.convInfo.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;
|
|
}
|
|
}
|
|
}
|
|
|
|
${s}
|
|
${n}
|
|
writeResult(batch, coords[1], coords[2], d2, dotProd);
|
|
}
|
|
`}};function Tce(e){let{inputs:t,backend:n,attrs:s}=e,{x:r,filter:a}=t,{strides:o,pad:i,dilations:l,dimRoundingMode:c}=s,u=l;u==null&&(u=[1,1]);let d=E.computeConv2DInfo(r.shape,a.shape,o,u,i,c,!0),p;d.batchSize===1&&d.inHeight===d.outHeight&&d.inWidth===d.outWidth&&d.strideHeight===1&&d.strideWidth===1&&d.filterHeight===d.filterWidth&&d.inChannels===d.outChannels&&d.filterHeight===3&&d.inChannels%4==0?p=new wC(d):p=new kC(d);let h=[{type:"int32",data:[d.padInfo.top,d.padInfo.left]},{type:"int32",data:[d.strideHeight,d.strideWidth]},{type:"int32",data:[d.dilationHeight,d.dilationWidth]},{type:"int32",data:[d.inHeight,d.inWidth]}];return n.runWebGPUProgram(p,[r,a],r.dtype,h)}var Nce={kernelName:Ma,backendName:"webgpu",kernelFunc:Tce},SC=qn({opSnippet:Vt.MUL,cpuKernelImpl:due,supportsComplex:!0}),Ece={kernelName:eo,backendName:"webgpu",kernelFunc:SC},Rce=class{constructor(e,t,n){this.variableNames=["x"],this.uniforms="reduceSize : i32;",this.inputShape=[e.batchSize,e.inSize];let[s]=E.computeOutAndReduceShapes(this.inputShape,[1]);this.outputShape=s.length===0?[1]:s,this.reductionFactor=2;let r=256,a=Math.min(Math.ceil(e.inSize/this.reductionFactor),r);this.workGroupSize=[a,1,1],this.dispatchLayout={x:[],y:this.outputShape.map((o,i)=>i)},this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.reduceType=t,this.shaderKey=`reduce_${t}_${n}`}getUserCode(){let e=this.workGroupSize[0]>1,t="",n="0.0";this.reduceType==="min"||this.reduceType==="max"?(t=`
|
|
if (isNanCustom(candidate)) {
|
|
bestValue = uniforms.NAN;
|
|
} elseif (candidate ${this.reduceType==="min"?"<":">"}
|
|
bestValue)
|
|
{ bestValue = candidate; }`,n="f32(x.numbers[offset])"):this.reduceType==="sum"||this.reduceType==="mean"?t=" bestValue = bestValue + candidate; ":this.reduceType==="prod"&&(t=" bestValue = bestValue * candidate; ",n="1.0");let s=this.reduceType==="mean"?"setOutputFlat(flatOutputIndex, bestValue / f32(uniforms.reduceSize));":"setOutputFlat(flatOutputIndex, bestValue);",r=`
|
|
var<workgroup> xBestValues : array<f32, ${this.workGroupSize[0]}>;
|
|
`,a=`
|
|
xBestValues[localId.x] = bestValue;
|
|
${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`bestValue = ${n};`:" "}
|
|
var currentSize = WorkGroupSize;
|
|
for(; currentSize > 1;) {
|
|
workgroupBarrier();
|
|
for (var w = 0; w < ${this.reductionFactor}; w = w + 1) {
|
|
let i = i32(localId.x) * ${this.reductionFactor} + w;
|
|
if (i < currentSize) {
|
|
let candidate = xBestValues[i];
|
|
${t}
|
|
}
|
|
}
|
|
workgroupBarrier();
|
|
xBestValues[localId.x] = bestValue;
|
|
currentSize = DIV_CEIL(currentSize, ${this.reductionFactor});
|
|
${this.reduceType==="sum"||this.reduceType==="mean"||this.reduceType==="prod"?`if(currentSize > 1) { bestValue = ${n}; }`:""}
|
|
}
|
|
if (localId.x == 0u) {
|
|
${s}
|
|
}
|
|
`;return`
|
|
fn DIV_CEIL(a : i32, b : i32) -> i32 {
|
|
return ((a - 1) / b + 1);
|
|
}
|
|
let WorkGroupSize = ${this.workGroupSize[0]};
|
|
${e?r:""}
|
|
fn getOffset(globalId : vec3<u32>) -> i32 {
|
|
let outputCoords = getOutputCoordsWithNonFlatDispatchLayout(globalId);
|
|
let offset = ${this.outputShape.length===1?"outputCoords":"outputCoords[0]"} * uniforms.reduceSize;
|
|
return offset;
|
|
}
|
|
${Ho()} {
|
|
let offset = getOffset(globalId);
|
|
var bestValue = ${n};
|
|
let Length = uniforms.reduceSize;
|
|
let WorkPerThread = DIV_CEIL(Length, WorkGroupSize);
|
|
for (var w = 0; w < WorkPerThread; w = w + 1) {
|
|
let i = i32(globalId.x) * WorkPerThread + w;
|
|
if (i < Length) {
|
|
let candidate = f32(x.numbers[offset + i]);
|
|
${t}
|
|
}
|
|
}
|
|
let flatOutputIndex = i32(globalId.y);
|
|
${e?a:s}
|
|
}
|
|
`}};function bp(e,t,n,s,r){let a=e.shape.length,o=[],i=v.parseAxisParam(t,e.shape),l=i,c=E.getAxesPermutation(l,a),u=e;c!=null&&(u=Dl({inputs:{x:e},attrs:{perm:c},backend:r}),l=E.getInnerMostAxes(l.length,a),o.push(u)),E.assertAxesAreInnerMostDims(s,l,a);let[d,p]=E.computeOutAndReduceShapes(u.shape,l),h=d;n&&(h=E.expandShapeToKeepDim(d,i));let f;if((s==="max"||s==="prod")&&r.shouldExecuteOnCPU([u])){let m=r.tensorMap.get(u.dataId).values;switch(s){case"max":let g=lue(m,v.sizeFromShape(p),h,e.dtype);f=r.makeTensorInfo(h,e.dtype,g);break;case"prod":let{outVals:A,outShape:x,outDtype:y}=fue(u.shape,u.dtype,m,l);f=r.makeTensorInfo(x,y,A);break;default:throw new Error(`${s} CPU implementation is not yet supported.`)}}else{let m=v.sizeFromShape(p),A=v.sizeFromShape(u.shape)/m,x={windowSize:m,inSize:m,batchSize:A,outSize:1},y=s==="mean"?"float32":wd(e.dtype),b=[{type:"int32",data:[m]}],w=new Rce(x,s,y),k=r.runWebGPUProgram(w,[u],y,b);o.push(k),f=Ge({inputs:{x:k},attrs:{shape:h},backend:r})}return o.forEach(m=>r.disposeData(m.dataId)),f}function Cx(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return bp(r,a,o,"sum",n)}var $ce={kernelName:po,backendName:"webgpu",kernelFunc:Cx};function _ce(e){let{inputs:t,backend:n,attrs:s}=e,{equation:r}=s,a=t,{allDims:o,summedDims:i,idDims:l}=E.decodeEinsumEquation(r,a.length);E.checkEinsumDimSizes(o.length,l,a);let{path:c,steps:u}=E.getEinsumComputePath(i,l),d=u.length,p=null,h=o.length,f=[];for(let m=0;m<d;++m){for(let g of u[m]){let{permutationIndices:A,expandDims:x}=E.getEinsumPermutation(h,l[g]),y;E.isIdentityPermutation(A)?y=a[g]:(y=Dl({inputs:{x:a[g]},backend:n,attrs:{perm:A}}),f.push(y));let b=y.shape.slice();for(let w=0;w<x.length;++w)b.splice(x[w],0,1);v.arraysEqual(y.shape,b)||(y=Ge({inputs:{x:y},backend:n,attrs:{shape:b}}),f.push(y)),p===null?p=y:(p=SC({inputs:{a:y,b:p},backend:n}),f.push(p))}m<d-1&&(c[m]>=0&&(p=Cx({inputs:{x:p},backend:n,attrs:{axis:c[m]-(o.length-h),keepDims:!1}}),f.push(p)),h--)}for(let m of f)m!==p&&n.disposeData(m.dataId);return p}var Dce={kernelName:rd,backendName:"webgpu",kernelFunc:_ce},Pce=Nn({opType:xt.ELU}),Fce={kernelName:La,backendName:"webgpu",kernelFunc:Pce},Oce=qn({opSnippet:Vt.EQUAL,dtype:"bool",cpuKernelImpl:Yle}),Mce={kernelName:Ai,backendName:"webgpu",kernelFunc:Oce},IC=Nn({opType:xt.EXP,cpuKernelImpl:Jle,dtype:"float32"}),zce={kernelName:Ba,backendName:"webgpu",kernelFunc:IC};function Tx(e){let{inputs:t,attrs:n,backend:s}=e,{dim:r}=n,{input:a}=t,o=a.shape.length,i=a.shape.slice(),l=r;return r<0&&(v.assert(-(o+1)<=r,()=>`Axis must be in the interval [${-(o+1)}, ${o}]`),l=o+r+1),i.splice(l,0,1),Ge({inputs:{x:a},backend:s,attrs:{shape:i}})}var Lce={kernelName:yi,backendName:"webgpu",kernelFunc:Tx},Bce=Nn({opType:xt.EXPM1,cpuKernelImpl:Qle}),Wce={kernelName:xi,backendName:"webgpu",kernelFunc:Bce},Vce=class{constructor(e){this.variableNames=[],this.outputShape=[],this.uniforms="value : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="fill"}getUserCode(){return`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
setOutputFlat(index, uniforms.value);
|
|
}
|
|
}
|
|
`}};function yc(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 Vce(s),i=[{type:"float32",data:[r]}];return t.runWebGPUProgram(o,[],a,i)}}var Uce={kernelName:fu,backendName:"webgpu",kernelFunc:yc},Gce=class{constructor(e){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="flipLeftRight"}getUserCode(){return`
|
|
${nt()}
|
|
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);
|
|
}
|
|
}
|
|
`}},Hce={kernelName:bi,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,s=t,r=new Gce(n.shape);return s.runWebGPUProgram(r,[n],n.dtype)}},jce=Nn({opType:xt.FLOOR,cpuKernelImpl:eue}),qce={kernelName:Wa,backendName:"webgpu",kernelFunc:jce},Xce=qn({opSnippet:Vt.INT_DIV,dtype:"int32"}),Kce={kernelName:Va,backendName:"webgpu",kernelFunc:Xce},Zce=(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}))})},CC=(e,t,n,s,r,a=!1)=>{let o={dtype:r.dtype,shape:r.shape},i=Tie(s,o,t,a),l=e.createShaderModule({code:i});return e.createComputePipeline({layout:n,compute:{module:l,entryPoint:"main"}})};function TC(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 NC(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=TC(u,d,p),f=u.getLayout(n.device),m=n.getAndSavePipeline(h,()=>CC(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 A=[i,o,...l,...u.dispatch];u.setUniform(n.device,A);let x;if(a){let y={source:t};x=n.device.importExternalTexture(y)}else x=u.inputTexture.createView();return n.runFromPixelsProgram(u,g.bufferInfo.buffer,f,x,c.dataId),c}var Yce={kernelName:hd,backendName:"webgpu",kernelFunc:Jce},xc;function Jce(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(K().getBool("WEBGPU_USE_IMPORT")&&o)return NC({externalImage:r,backend:n,attrs:s,outShape:p,useImport:!0});if((o||i)&&(xc==null&&(xc=document.createElement("canvas").getContext("2d")),xc.canvas.width=u,xc.canvas.height=d,xc.drawImage(r,0,0,u,d),r=xc.canvas),c||l||o||i)return NC({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 A=h.length,x=0;for(let y=0;y<A;y++)y%4<a&&(f[x++]=h[y])}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 Qce=class{constructor(e,t,n,s,r){this.uniforms="varianceEpsilon : f32;",this.workGroupSize=[128,1,1],this.size=!0,this.variableNames=["x","mean","variance"],E.assertAndGetBroadcastShape(e,t),E.assertAndGetBroadcastShape(e,n),this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),s!=null&&(E.assertAndGetBroadcastShape(e,s),this.variableNames.push("offset")),r!=null&&(E.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)"),`
|
|
${nt()}
|
|
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)));
|
|
}
|
|
}
|
|
`}},ede={kernelName:Ua,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 Qce(s.shape,o.shape,i.shape,d,p),f=[{type:"float32",data:[l]}];return c.runWebGPUProgram(h,u,s.dtype,f)}};function tde(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=E.convertConv2DDataFormat(u),g=E.computeConv2DInfo(r.shape,a.shape,l,d,c,p,!1,m),A=o!=null,x=i!=null,y;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 yC({x:r,filter:a,convInfo:g,backend:n,bias:o,activation:h,preluActivationWeights:i,leakyreluAlpha:f});let b=K().getBool("WEBGPU_USE_NAIVE_CONV2D"),w=g.inChannels%4==0&&g.outChannels%4==0,k=[g.padInfo.top,g.padInfo.left],C=[{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)y=new vC(g,A,h,x);else{w?y=new xC(g,A,h,x):y=new bC(g,A,h,x);let R=g.outShape[1]*g.outShape[2],F=g.outShape[3],_=g.filterHeight*g.filterWidth*g.inShape[3];C.push({type:"int32",data:[R]},{type:"int32",data:[F]},{type:"int32",data:[_]})}let N=[r,a];return A&&N.push(o),x&&N.push(i),n.runWebGPUProgram(y,N,r.dtype,C)}var nde={kernelName:bo,backendName:"webgpu",kernelFunc:tde};function sde(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(E.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let f=E.computeConv2DInfo(r.shape,a.shape,l,h,c,d,!0),m=[r,a],g=o!=null,A=i!=null;g&&m.push(o),A&&m.push(i);let x;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?x=new wC(f,g,p,A):x=new kC(f,g,p,A);let y=[{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]}];return n.runWebGPUProgram(x,m,"float32",y)}var rde={kernelName:vo,backendName:"webgpu",kernelFunc:sde},ade=class{constructor(e,t){this.variableNames=["A","indices"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(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",`
|
|
${nt()}
|
|
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 ode(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]=E.prepareAndValidate(s,r),p=Ge({inputs:{x:r},backend:n,attrs:{shape:[c,o]}}),h=Ge({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),y=n.bufferSync(s),b=tue(x,y,s.dtype,c,o,u,d,s.shape,i);return n.makeTensorInfo(l,s.dtype,b.values)}let f=new ade(o,[c,u]),m=[{type:"int32",data:[o]},{type:"int32",data:d}],g=n.runWebGPUProgram(f,[h,p],h.dtype,m),A=Ge({inputs:{x:g},backend:n,attrs:{shape:l}});return n.disposeData(p.dataId),n.disposeData(h.dataId),n.disposeData(g.dataId),A}var ide={kernelName:wi,backendName:"webgpu",kernelFunc:ode},lde=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=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.shaderKey="gather"}getUserCode(){let e=ude(this.aShape,"i32");return`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function ude(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 EC(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=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),u=v.sizeFromShape(a.shape),d=[],p=Ge({inputs:{x:r},backend:n,attrs:{shape:[c.batchSize,c.outerSize,c.dimSize,c.sliceSize]}}),h=Ge({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 y=n.tensorMap.get(h.dataId).values,b=ze(h.shape,h.dtype,y),k=n.tensorMap.get(p.dataId).values,C=ze(p.shape,p.dtype,k),N=nue(C,b,f);return d.forEach(R=>n.disposeData(R.dataId)),n.makeTensorInfo(c.outputShape,N.dtype,N.values)}let m=new lde(p.shape,f),g=n.runWebGPUProgram(m,[p,h],p.dtype);d.push(g);let A=Ge({inputs:{x:g},backend:n,attrs:{shape:c.outputShape}});return d.forEach(x=>n.disposeData(x.dataId)),A}var cde={kernelName:vi,backendName:"webgpu",kernelFunc:EC},dde=qn({opSnippet:Vt.GREATER,cpuKernelImpl:rue,dtype:"bool"}),pde={kernelName:ki,backendName:"webgpu",kernelFunc:dde},hde=qn({opSnippet:Vt.GREATER_EQUAL,dtype:"bool",cpuKernelImpl:sue}),fde={kernelName:Ga,backendName:"webgpu",kernelFunc:hde},mde=qn({opSnippet:Vt.LESS,dtype:"bool",cpuKernelImpl:oue}),gde={kernelName:Ii,backendName:"webgpu",kernelFunc:mde},Ade=qn({opSnippet:Vt.LESS_EQUAL,dtype:"bool",cpuKernelImpl:aue}),yde={kernelName:Ci,backendName:"webgpu",kernelFunc:Ade},xde=Nn({opType:xt.LOG,cpuKernelImpl:iue}),bde={kernelName:ja,backendName:"webgpu",kernelFunc:xde},vde=qn({opSnippet:Vt.LOGICAL_AND,dtype:"bool"}),wde={kernelName:Ti,backendName:"webgpu",kernelFunc:vde},kde=Nn({opType:xt.LOGICAL_NOT}),Sde={kernelName:xu,backendName:"webgpu",kernelFunc:kde};function RC(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{reductionIndices:a,keepDims:o}=s;return bp(r,a,o,"max",n)}var Ide={kernelName:qa,backendName:"webgpu",kernelFunc:RC},Cde=qn({opSnippet:Vt.MAX,cpuKernelImpl:uue}),Tde={kernelName:Xa,backendName:"webgpu",kernelFunc:Cde};function Nde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{filterSize:a,strides:o,pad:i,dimRoundingMode:l}=s,c=1,u=E.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 Qs({inputs:{x:r},backend:n});d=new mC(u),p.push({type:"int32",data:[u.strideHeight,u.strideWidth]})}else d=new fC(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 Ede={kernelName:Ka,backendName:"webgpu",kernelFunc:Nde};function Rde(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{keepDims:a,axis:o}=s;return bp(r,o,a,"mean",n)}var $de={kernelName:Za,backendName:"webgpu",kernelFunc:Rde};function _de(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return bp(r,a,o,"min",n)}var Dde={kernelName:Ya,backendName:"webgpu",kernelFunc:_de},Pde=qn({opSnippet:Vt.MIN,cpuKernelImpl:cue}),Fde={kernelName:Ja,backendName:"webgpu",kernelFunc:Pde},Ode=class{constructor(e,t,n){this.uniforms="",this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((s,r)=>s[0]+e[r]+s[1]),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.xShape=e,t.map((s,r)=>{this.uniforms+=` pad${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`
|
|
${nt()}
|
|
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}));
|
|
}
|
|
}
|
|
`}},Mde={kernelName:Qa,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 Ode(s.shape,r,a);return o.runWebGPUProgram(l,[s],s.dtype,i)}};function zde(e){let{inputs:t,backend:n}=e,{x:s}=t;if(n.shouldExecuteOnCPU([s])){let a=n.tensorMap.get(s.dataId),[o,i]=pue(a.values,s.shape,s.dtype);return n.makeTensorInfo(i,s.dtype,o)}let r=new Zm(s.shape,xt.NEG);return n.runWebGPUProgram(r,[s],s.dtype)}var Lde={kernelName:Ni,backendName:"webgpu",kernelFunc:zde};function Bde(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}=Ks.nonMaxSuppressionV3Impl(c,u,o,i,l);return n.makeTensorInfo([d.length],"int32",new Int32Array(d))}var Wde={kernelName:Ri,backendName:"webgpu",kernelFunc:Bde};function Vde(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:A}=Ks.nonMaxSuppressionV5Impl(u,d,p,h,f,m);return[n.makeTensorInfo([g.length],"int32",new Int32Array(g)),n.makeTensorInfo([A.length],"float32",new Float32Array(A))]}var Ude={kernelName:$i,backendName:"webgpu",kernelFunc:Vde};function Jm(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="complex64"){let r=xp({inputs:{input:s},backend:n}),a=Jm({inputs:{x:r},backend:n}),o=Ym({inputs:{input:s},backend:n}),i=Jm({inputs:{x:o},backend:n}),l=gc({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 yc({attrs:{shape:s.shape,dtype:s.dtype,value:s.dtype==="string"?"":0},backend:n})}var Gde={kernelName:Zi,backendName:"webgpu",kernelFunc:Jm};function $C(e){let{inputs:t,backend:n}=e,{x:s}=t;if(s.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(s.dtype==="complex64"){let r=xp({inputs:{input:s},backend:n}),a=$C({inputs:{x:r},backend:n}),o=Ym({inputs:{input:s},backend:n}),i=Jm({inputs:{x:o},backend:n}),l=gc({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 yc({attrs:{shape:s.shape,dtype:s.dtype,value:1},backend:n})}var Hde={kernelName:_i,backendName:"webgpu",kernelFunc:$C};function jde(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Tx({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let a=t[0].shape,o=t[0].dtype;t.forEach(u=>{v.assertShapesMatch(a,u.shape,"All tensors passed to stack must have matching shapes"),v.assert(o===u.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=t.map(u=>{let d=Tx({inputs:{input:u},backend:n,attrs:{dim:r}});return i.push(d),d}),c=AC({inputs:l,backend:n,attrs:{axis:r}});return i.forEach(u=>n.disposeData(u.dataId)),c}var qde={kernelName:Pi,backendName:"webgpu",kernelFunc:jde},Xde=class{constructor(e,t){this.variableNames=["x"],this.uniforms="constantValue : f32;",this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t.map((n,s)=>n[0]+e[s]+n[1]),this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),t.map((n,s)=>{this.uniforms+=` pad${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`
|
|
${nt()}
|
|
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}));
|
|
}
|
|
}
|
|
}
|
|
`}},_C=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 Qs({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 yc({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 Xde(r.shape,a);return n.runWebGPUProgram(l,[r],r.dtype,i)},Kde={kernelName:to,backendName:"webgpu",kernelFunc:_C},Zde=qn({opSnippet:Vt.POW}),Yde={kernelName:no,backendName:"webgpu",kernelFunc:Zde};function Jde(e){let{inputs:t,backend:n}=e,{x:s,alpha:r}=t,a=new dC(Vt.PRELU,s.shape,r.shape);return n.runWebGPUProgram(a,[s,r],"float32")}var Qde={kernelName:so,backendName:"webgpu",kernelFunc:Jde};function epe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{axis:a,keepDims:o}=s;return bp(r,a,o,"prod",n)}var tpe={kernelName:Fi,backendName:"webgpu",kernelFunc:epe},npe=e=>{let{backend:t,attrs:n}=e,{start:s,stop:r,step:a,dtype:o}=n,i=mue(s,r,a,o);return t.makeTensorInfo([i.length],o,i)},spe={kernelName:wu,backendName:"webgpu",kernelFunc:npe},DC=qn({opSnippet:Vt.DIV}),rpe={kernelName:za,backendName:"webgpu",kernelFunc:DC},ape=Nn({opType:xt.RELU}),ope={kernelName:ro,backendName:"webgpu",kernelFunc:ape},ipe=Nn({opType:xt.RELU6}),lpe={kernelName:oo,backendName:"webgpu",kernelFunc:ipe},upe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeBilinear_${s}_${r}_${this.outputShape[1]>1}_${this.outputShape[2]>1}`}getUserCode(){let e=this.alignCorners&&this.outputShape[1]>1,t=this.alignCorners&&this.outputShape[2]>1;return`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let b = coords[0];
|
|
let d = coords[3];
|
|
let rc = coords.yz;
|
|
|
|
let effectiveInSize = vec2<f32>(
|
|
${e?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"},
|
|
${t?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"});
|
|
|
|
let effectiveOutSize = vec2<f32>(
|
|
${e?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"},
|
|
${t?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"});
|
|
|
|
let effectiveInputOverOutputRatioRC =
|
|
effectiveInSize / effectiveOutSize;
|
|
|
|
// Fractional source index
|
|
let sourceFracIndexRC = ${this.halfPixelCenters?"(vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC - vec2<f32>(0.5)":"vec2<f32>(rc) * effectiveInputOverOutputRatioRC"};
|
|
|
|
// Compute the four integer indices.
|
|
let sourceFloorRC = vec2<i32>(sourceFracIndexRC);
|
|
let sourceCeilRC = vec2<i32>(
|
|
min(vec2<f32>(uniforms.xShape.yz) - vec2<f32>(1.0), ceil(sourceFracIndexRC)));
|
|
|
|
let topLeft = getX(b, sourceFloorRC.x, sourceFloorRC.y, d);
|
|
let bottomLeft = getX(b, sourceCeilRC.x, sourceFloorRC.y, d);
|
|
let topRight = getX(b, sourceFloorRC.x, sourceCeilRC.y, d);
|
|
let bottomRight = getX(b, sourceCeilRC.x, sourceCeilRC.y, d);
|
|
|
|
let fracRC = sourceFracIndexRC - vec2<f32>(sourceFloorRC);
|
|
|
|
let top = topLeft + (topRight - topLeft) * fracRC.y;
|
|
let bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;
|
|
let newValue = top + (bottom - top) * fracRC.x;
|
|
|
|
setOutputFlat(index, newValue);
|
|
}
|
|
}
|
|
`}};function cpe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,size:o,halfPixelCenters:i}=s,[l,c]=o,u=new upe(r.shape,l,c,a,i);return n.runWebGPUProgram(u,[r],"float32")}var dpe={kernelName:ao,backendName:"webgpu",kernelFunc:cpe},ppe=class{constructor(e,t,n,s,r){this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=[e[0],t,n,e[3]],this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.alignCorners=s,this.halfPixelCenters=r,this.shaderKey=`resizeNearest_${s}_${this.outputShape[1]>1}_${this.outputShape[2]>1}_${r}`}getUserCode(){let e=this.alignCorners?"0.5":"0.0",t;this.halfPixelCenters?t="max((vec2<f32>(rc) + vec2<f32>(0.5)) * effectiveInputOverOutputRatioRC, vec2<f32>(0.0))":t="vec2<f32>(rc) * effectiveInputOverOutputRatioRC";let n=this.alignCorners&&this.outputShape[1]>1,s=this.alignCorners&&this.outputShape[2]>1;return`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
let b = coords[0];
|
|
let d = coords[3];
|
|
let rc = coords.yz;
|
|
|
|
let effectiveInSize = vec2<f32>(
|
|
${n?"f32(uniforms.xShape.y) - 1.0":"f32(uniforms.xShape.y)"},
|
|
${s?"f32(uniforms.xShape.z) - 1.0":"f32(uniforms.xShape.z)"});
|
|
|
|
let effectiveOutSize = vec2<f32>(
|
|
${n?"f32(uniforms.outShape.y) - 1.0":"f32(uniforms.outShape.y)"},
|
|
${s?"f32(uniforms.outShape.z) - 1.0":"f32(uniforms.outShape.z)"});
|
|
|
|
let effectiveInputOverOutputRatioRC =
|
|
effectiveInSize / effectiveOutSize;
|
|
|
|
// Fractional source index
|
|
let sourceFracIndexRC = ${t};
|
|
|
|
// 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 + ${e})));
|
|
let newValue = getX(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutputFlat(index, newValue);
|
|
}
|
|
}
|
|
`}};function hpe(e){let{inputs:t,backend:n,attrs:s}=e,{images:r}=t,{alignCorners:a,halfPixelCenters:o,size:i}=s,[l,c]=i,u=new ppe(r.shape,l,c,a,o);return n.runWebGPUProgram(u,[r],r.dtype)}var fpe={kernelName:Su,backendName:"webgpu",kernelFunc:hpe},mpe=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(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`
|
|
${nt()}
|
|
|
|
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));
|
|
${this.fillSnippet}
|
|
if(coordX >= 0 && coordX < uniforms.xShape[2] && coordY >= 0 &&
|
|
coordY < uniforms.xShape[1]) {
|
|
outputValue = getX(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutputFlat(index, outputValue);
|
|
}
|
|
}
|
|
`}},gpe={kernelName:Yi,backendName:"webgpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:s}=e,{radians:r,fillValue:a,center:o}=t,i=n,l=new mpe(s.shape,a),[c,u]=E.getImageCenter(o,s.shape[1],s.shape[2]),d=[{type:"float32",data:[c]},{type:"float32",data:[u]},{type:"float32",data:[Math.sin(r)]},{type:"float32",data:[Math.cos(r)]}];return typeof a=="number"?d.push({type:"float32",data:[Number.parseFloat(a.toFixed(2))]}):d.push({type:"float32",data:a}),i.runWebGPUProgram(l,[s],s.dtype,d)}},Ape=Nn({opType:xt.RSQRT,cpuKernelImpl:gue}),ype={kernelName:io,backendName:"webgpu",kernelFunc:Ape},xpe=class{constructor(e,t,n,s,r,a,o){this.variableNames=["updates","indices"],this.workGroupSize=[64,1,1],this.atomic=!0,this.outputShape=a,this.type=o,this.dispatchLayout=je(e),this.dispatch=Fe(this.dispatchLayout,e,this.workGroupSize),this.sliceDimGreaterThanOne=t>1,this.shaderKey=`scatter_${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"?"ignore(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}
|
|
|
|
${nt()}
|
|
|
|
if (index < uniforms.size) {
|
|
let coords = getUpdatesCoordsFromFlatIndex(index);
|
|
var flattenedIndex = 0;
|
|
for (var j = 0; j < uniforms.sliceDim; j = j + 1) {
|
|
let indexInside = i32(round(${t}));
|
|
flattenedIndex = flattenedIndex + indexInside * ${n};
|
|
}
|
|
let updateValue = ${o};
|
|
let flatIndex = getOutputFlatIndex(${r});
|
|
|
|
${i}
|
|
}
|
|
}`}};function bpe(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}=E.calculateShapes(a,r,o),p=[d/c,c];if(d===0)return n.makeTensorInfo(o,r.dtype);let h=Ge({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),f=Ge({inputs:{x:a},backend:n,attrs:{shape:[l,c]}}),m=f.dtype,g=yc({backend:n,attrs:{shape:p,value:0,dtype:m}}),A=v.sizeFromShape(f.shape),x=[{type:"int32",data:[i]},{type:"int32",data:u},{type:"int32",data:[A]}],y=new xpe(f.shape,i,h.shape.length,f.shape.length,u,p,m),b=n.runWebGPUProgram(y,[f,h],m,x,g),w=Ge({inputs:{x:b},backend:n,attrs:{shape:o}});return n.disposeData(h.dataId),n.disposeData(f.dataId),n.disposeData(b.dataId),w}var vpe={kernelName:Li,backendName:"webgpu",kernelFunc:bpe},wpe=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=t,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(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`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
let cVal = getC(${e});
|
|
if (cVal >= 1.0) {
|
|
setOutputFlat(index, getA(${t}));
|
|
} else {
|
|
setOutputFlat(index, getB(${t}));
|
|
}
|
|
}
|
|
}
|
|
`}};function kpe(e){let{inputs:t,backend:n}=e,{condition:s,t:r,e:a}=t,o=new wpe(s.shape.length,r.shape,r.shape.length);return n.runWebGPUProgram(o,[s,r,a],Ln(r.dtype,a.dtype))}var Spe={kernelName:Bi,backendName:"webgpu",kernelFunc:kpe},Ipe=Nn({opType:xt.SIGMOID}),Cpe={kernelName:uo,backendName:"webgpu",kernelFunc:Ipe},Tpe=Nn({opType:xt.SIN}),Npe={kernelName:lo,backendName:"webgpu",kernelFunc:Tpe},Epe=Nn({opType:xt.SINH}),Rpe={kernelName:Vi,backendName:"webgpu",kernelFunc:Epe},PC=qn({opSnippet:Vt.SUB,cpuKernelImpl:vue,supportsComplex:!0}),$pe={kernelName:mo,backendName:"webgpu",kernelFunc:PC};function _pe(e){let{inputs:t,backend:n,attrs:s}=e,{logits:r}=t,{dim:a}=s,o=v.parseAxisParam([a],r.shape),i=RC({inputs:{x:r},backend:n,attrs:{reductionIndices:o,keepDims:!1}}),l=E.expandShapeToKeepDim(i.shape,o),c=Ge({inputs:{x:i},backend:n,attrs:{shape:l}}),u=PC({inputs:{a:r,b:c},backend:n}),d=IC({inputs:{x:u},backend:n}),p=Cx({inputs:{x:d},backend:n,attrs:{axis:o,keepDims:!1}}),h=Ge({inputs:{x:p},backend:n,attrs:{shape:l}}),f=DC({inputs:{a:d,b:h},backend:n});return n.disposeData(i.dataId),n.disposeData(c.dataId),n.disposeData(u.dataId),n.disposeData(d.dataId),n.disposeData(p.dataId),n.disposeData(h.dataId),f}var Dpe={kernelName:ho,backendName:"webgpu",kernelFunc:_pe},Ppe=e=>{let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,paddings:o}=s;v.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGPU backend not implemented yet");let i=a.reduce((A,x)=>A*x),l=[[0,0]];l.push(...o);for(let A=1+a.length;A<r.shape.length;++A)l.push([0,0]);let c=[],u=_C({inputs:{x:r},backend:n,attrs:{paddings:l,constantValue:0}}),d=E.getReshaped(u.shape,a,i,!1),p=E.getPermuted(d.length,a.length,!1),h=E.getReshapedPermuted(u.shape,a,i,!1),f=Ge({inputs:{x:u},backend:n,attrs:{shape:d}}),m=Dl({inputs:{x:f},backend:n,attrs:{perm:p}}),g=Ge({inputs:{x:m},backend:n,attrs:{shape:h}});return c.push(u),c.push(f),c.push(m),c.forEach(A=>n.disposeData(A.dataId)),g},Fpe={kernelName:Ui,backendName:"webgpu",kernelFunc:Ppe},Ope=class{constructor(e,t,n,s,r,a,o=!0){this.variableNames=["updates","indices","defaultValue"],this.workGroupSize=[64,1,1],this.workPerThread=4,this.size=!0,this.outputShape=a,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize,[this.workPerThread,1,1]);let i=t>1;this.shaderKey=`scatter_${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`
|
|
${nt()}
|
|
|
|
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 Mpe(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}=E.calculateShapes(a,r,i),p=!1,h=[{type:"int32",data:[c]},{type:"int32",data:[l]},{type:"int32",data:u}],f=new Ope(c,l,r.shape.length,a.shape.length,u,[d,1],p),m=n.runWebGPUProgram(f,[a,r,o],a.dtype,h),g=Ge({inputs:{x:m},backend:n,attrs:{shape:i}});return n.disposeData(m.dataId),g}var zpe={kernelName:cd,backendName:"webgpu",kernelFunc:Mpe};function Lpe(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=E.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=Ac({inputs:{x:r},backend:n,attrs:{begin:u,size:h}});return u[i]+=p,f})}var Bpe={kernelName:Gi,backendName:"webgpu",kernelFunc:Lpe},Wpe=Nn({opType:xt.SQRT}),Vpe={kernelName:co,backendName:"webgpu",kernelFunc:Wpe},Upe={kernelName:Nu,backendName:"webgpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,s=t,r=new Zm(n.shape,xt.SQUARE);return s.runWebGPUProgram(r,[n],n.dtype)}},Gpe=qn({opSnippet:Vt.SQUARED_DIFFERENCE}),Hpe={kernelName:fo,backendName:"webgpu",kernelFunc:Gpe},jpe=class{constructor(e){this.variableNames=["x"],this.workPerThread=1,this.workGroupSize=[64,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(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`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let coords = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getX(${t}));
|
|
}
|
|
}
|
|
`}};function qpe(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:A,begin:x,end:y,strides:b}=Ft.sliceInfo(r.shape,a,o,i,l,c,u,d,p),w;if(m)w=Ge({inputs:{x:r},backend:n,attrs:{shape:f}});else if(g||A){v.assert(r.shape.length>=1,()=>`Input must have rank at least 1, got: ${r.shape.length}`);let k=Ft.computeOutShape(x,y,b),C=Ac({inputs:{x:r},backend:n,attrs:{begin:x,size:k}});w=Ge({inputs:{x:C},backend:n,attrs:{shape:f}}),n.disposeData(C.dataId)}else if(n.shouldExecuteOnCPU([r])){let C=n.readSync(r.dataId),N=ze(r.shape,r.dtype,C),R=xue(h,N,b,x);w=n.makeTensorInfo(f,r.dtype,R.values)}else{let C=new jpe(h),N=[{type:"int32",data:x},{type:"int32",data:b}],R=n.runWebGPUProgram(C,[r],r.dtype,N);w=Ge({inputs:{x:R},backend:n,attrs:{shape:f}}),n.disposeData(R.dataId)}return w}var Xpe={kernelName:Hi,backendName:"webgpu",kernelFunc:qpe};function Kpe(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]=bue(p,h,r,a,o,i,l,c);return[n.makeTensorInfo([f.length],"string",f),n.makeTensorInfo(d.shape,"int32",m)]}var Zpe={kernelName:dd,backendName:"webgpu",kernelFunc:Kpe},Ype=Nn({opType:xt.TANH}),Jpe={kernelName:go,backendName:"webgpu",kernelFunc:Ype},Qpe=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=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.rank=this.outputShape.length,this.shaderKey="tile"}getUserCode(){let e=ehe(this.rank,"uniforms.");return`
|
|
${nt()}
|
|
if (index < uniforms.size) {
|
|
let resRC = getCoordsFromFlatIndex(index);
|
|
setOutputFlat(index, getA(${e}));
|
|
}
|
|
}
|
|
`}};function ehe(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 the(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=wue(u,a);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let o=new Qpe(r.shape,a);return n.runWebGPUProgram(o,[r],r.dtype)}var nhe={kernelName:qr,backendName:"webgpu",kernelFunc:the},she=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms=`inputSize : i32; firstPass : i32; negativeInf : f32;
|
|
dir : i32; inc : i32;`,this.shaderKey="swap"}getUserCode(){return`
|
|
${nt()}
|
|
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));
|
|
}
|
|
}
|
|
}
|
|
`}},rhe=class{constructor(e){this.variableNames=["x","indices"],this.workGroupSize=[256,1,1],this.size=!0,this.outputShape=e,this.dispatchLayout=je(this.outputShape),this.dispatch=Fe(this.dispatchLayout,this.outputShape,this.workGroupSize),this.uniforms="inputSize : i32; firstPass : i32; k : i32;",this.shaderKey="merge"}getUserCode(){return`
|
|
${nt()}
|
|
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 bc(e,t){t!==null&&e.disposeData(t.dataId)}function FC(e){let t=1;for(;t<e;)t*=2;return t}function ahe(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,C]=kue(w,i,r.dtype,a,o);return[n.makeTensorInfo(k.shape,k.dtype,k.values),n.makeTensorInfo(C.shape,C.dtype,C.values)]}if(a===0)return i[i.length-1]=0,[n.makeTensorInfo(i,r.dtype,[]),n.makeTensorInfo(i,"int32",[])];if(l===1)return[r,yc({attrs:{shape:i,dtype:"int32",value:0},backend:n})];let u=v.sizeFromShape(i)/l,d=Ge({inputs:{x:r},attrs:{shape:[u,l]},backend:n}),p=FC(a),h=FC(l),f=null,m=()=>f===null?[d,d]:[d,f],g=(w,k,C)=>{let N=m(),R=new she(C),_=[{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]}],P=f;f=n.runWebGPUProgram(R,N,"int32",_),bc(n,P)};for(let w=1;w<p;w*=2){let k=w*2;for(let C=w;C>=1;C/=2)g(k,C,[u,h])}for(let w=h;w>p;w/=2){let k=m(),C=new rhe([u,w/2]),R=[{type:"int32",data:[l]},{type:"int32",data:[f===null?1:0]},{type:"int32",data:[p]}],F=f;f=n.runWebGPUProgram(C,k,"int32",R),bc(n,F);let _=p/2,P=_*2;for(let T=_;T>=1;T/=2)g(P,T,f.shape)}let A=f;f=Ac({inputs:{x:f},backend:n,attrs:{begin:0,size:[u,a]}}),bc(n,A);let x=EC({inputs:{x:d,indices:f},backend:n,attrs:{axis:1,batchDims:1}});bc(n,d);let y=i.slice(0,-1);y.push(a),A=f,f=Ge({inputs:{x:f},attrs:{shape:y},backend:n}),bc(n,A);let b=x;return x=Ge({inputs:{x},attrs:{shape:y},backend:n}),bc(n,b),[x,f]}var ohe={kernelName:qi,backendName:"webgpu",kernelFunc:ahe},ihe=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=je(this.outputShape),this.dispatch=Fe(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;
|
|
}
|
|
|
|
${nt()}
|
|
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 lhe(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],A=new ihe(g),x=o==="nearest"?1:2,y;switch(i){case"constant":y=1;break;case"reflect":y=2;break;case"wrap":y=3;break;case"nearest":y=4;break;default:y=1;break}let b=[{type:"int32",data:[x]},{type:"int32",data:[y]},{type:"float32",data:[l]}];return n.runWebGPUProgram(A,[r,a],"float32",b)}var uhe={kernelName:Xi,backendName:"webgpu",kernelFunc:lhe};function che(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=Ac({inputs:{x:o},backend:n,attrs:{begin:p,size:h}}),A=Ge({inputs:{x:g},backend:n,attrs:{shape:c}});f[m]=A,d.push(g)}return d.forEach(m=>n.disposeData(m.dataId)),f}var dhe={kernelName:Ki,backendName:"webgpu",kernelFunc:che},phe=[Ule,Cue,Nue,$ue,Mue,Lue,Wue,Uue,Xue,Jue,ece,rce,qle,lce,pce,gce,yce,bce,kce,Cce,Nce,Dce,Fce,Mce,Lce,zce,Wce,Uce,Hce,Yce,qce,Kce,ede,nde,rde,ide,cde,pde,fde,jle,oce,gde,yde,bde,wde,Sde,Ide,Tde,Ede,$de,Dde,Fde,Mde,Ece,Lde,Wde,Ude,Kue,Hde,qde,Kde,Qde,tpe,Yde,spe,Zue,rpe,ope,lpe,Wle,dpe,fpe,gpe,ype,vpe,Spe,Cpe,Npe,Rpe,jue,Xpe,Zpe,Dpe,Fpe,Bpe,zpe,Vpe,Upe,Hpe,$pe,$ce,Jpe,nhe,ohe,uhe,Fue,dhe,Gde];for(let e of phe)ar(e);var hhe=class{constructor(e){this.device=e,this.numUsedBuffers=0,this.numFreeBuffers=0,this.freeBuffers=new Map,this.usedBuffers=new Map,this.numBytesUsed=0,this.numBytesAllocated=0}acquireBuffer(e,t){let n=OC(e,t);if(this.freeBuffers.has(n)||this.freeBuffers.set(n,[]),this.usedBuffers.has(n)||this.usedBuffers.set(n,[]),this.numBytesUsed+=e,this.numUsedBuffers++,this.freeBuffers.get(n).length>0){this.numFreeBuffers--;let r=this.freeBuffers.get(n).shift();return this.usedBuffers.get(n).push(r),r}this.numBytesAllocated+=e;let s=this.device.createBuffer({size:e,usage:t});return this.usedBuffers.get(n).push(s),s}releaseBuffer(e,t,n){if(this.freeBuffers==null)return;let s=OC(t,n);this.freeBuffers.has(s)||this.freeBuffers.set(s,[]),this.freeBuffers.get(s).push(e),this.numFreeBuffers++,this.numUsedBuffers--;let r=this.usedBuffers.get(s),a=r.indexOf(e);if(a<0)throw new Error("Cannot release a buffer that was never provided by this buffer manager");r.splice(a,1),this.numBytesUsed-=t}getNumUsedBuffers(){return this.numUsedBuffers}getNumFreeBuffers(){return this.numFreeBuffers}reset(){this.freeBuffers=new Map,this.usedBuffers=new Map,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0}dispose(){this.freeBuffers==null&&this.usedBuffers==null||(this.freeBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.usedBuffers.forEach((e,t)=>{e.forEach(n=>{n.destroy()})}),this.freeBuffers=null,this.usedBuffers=null,this.numUsedBuffers=0,this.numFreeBuffers=0,this.numBytesUsed=0,this.numBytesAllocated=0)}};function OC(e,t){return`${e}_${t}`}var MC=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=je(this.outputShape),this.dispatch=Fe(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>"};
|
|
|
|
${nt()}
|
|
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}}},fhe=class extends MC{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}}},mhe=K().getNumber("WEBGPU_CPU_HANDOFF_SIZE_THRESHOLD"),zC=class extends eu{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,!bx())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 hhe(this.device),this.tensorMap=new Zc(this,ss()),this.supportTimeQuery&&(this.querySet=this.device.createQuerySet({type:"timestamp",count:2})),K().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 zC.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)*xx(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)*xx(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 MC),this.fromPixelProgram;case"import":return this.fromPixelImportProgram||(this.fromPixelImportProgram=new fhe),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),K().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 ${e} was not registered!`);let t=this.tensorMap.get(e),{values:n}=t;if(n!=null)return this.convertAndCacheOnCPU(e,n);let s;if(t.dtype==="complex64"){let r=await Promise.all([this.read(t.complexTensorInfos.real.dataId),this.read(t.complexTensorInfos.imag.dataId)]),a=r[0],o=r[1];s=E.mergeRealAndImagArrays(a,o)}else{let r=await this.getBufferData(t);s=aC(r,t.dtype)}return this.convertAndCacheOnCPU(e,s),s}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(s=>v.decodeString(s))}catch(s){throw new Error("Failed to decode encoded string bytes into utf-8")}return ze(e.shape,e.dtype,n)}async time(e){let t=this.activeTimers,n=[],s=!1;this.programTimersStack==null?(this.programTimersStack=n,s=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=v.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=v.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,s&&(this.programTimersStack=null);let o={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null},i=await Promise.all(r);return o.kernelMs=v.sum(i),o.getExtraProfileInfo=()=>i.map((l,c)=>({name:a[c],ms:l})).map(l=>`${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 N=this.tensorMap.get(r.dataId);return N.values=v.getTypedArrayFromDType(r.dtype,0),r}this.uploadToGPU(r.dataId)}let a=[{type:"float32",data:[NaN]}],o=t.concat(r).map(N=>N.shape),i="int32";o.map(N=>{a.push({type:i,data:N})});let l=v.computeStrides(r.shape);if(a.push({type:i,data:l}),e.size){let N=v.sizeFromShape(e.outputShape);a.push({type:i,data:[e.isVec4?N/4:N]})}s&&(a=[...a,...s]);let c=null,u=this.computePadding(a),d=u.byteLength;c=this.makeUniformsDataView(u);let p=t.map((N,R)=>{if(N.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(N.dataId),{dtype:this.tensorMap.get(N.dataId).dtype,shape:N.shape,name:e.variableNames[R]}}),h=p.map(N=>N.dtype).concat(r.dtype),f=p.map(N=>E.getBroadcastDims(N.shape,r.shape)),m=p.map(N=>v.arraysEqual(N.shape,r.shape)).join("_"),g=f.map(N=>N.join("_")).join(";"),A=TC(e,o,h,g,m),{bindGroupLayout:x,pipelineLayout:y}=this.getCachedOrCreateLayout(e.variableNames.length),b=this.getAndSavePipeline(A,()=>CC(this.device,e,y,p,r)),w=this.activeTimers!=null,k=Zce(this.device,x,t.map(N=>this.tensorToBinding(N)),this.tensorToBinding(r),c);this.ensureCommandEncoderReady();let C=this.getComputePass();if(w&&this.supportTimeQuery&&C.writeTimestamp(this.querySet,0),C.setPipeline(b),C.setBindGroup(0,k),C.dispatch(e.dispatch[0],e.dispatch[1],e.dispatch[2]),w&&this.supportTimeQuery&&C.writeTimestamp(this.querySet,1),this.dispatchNumberInEncoder++,t.forEach(N=>{this.commandQueueOwnedIds.add(N.dataId)}),this.commandQueueOwnedIds.add(r.dataId),c){let N={byteSize:d,usage:GPUBufferUsage.COPY_DST|GPUBufferUsage.UNIFORM,buffer:c.buffer};this.uniformDisposalQueue.push(N)}return K().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=mhe){return K().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)}},Nx=zC;Nx.nextDataId=0;var LC={};Oe(LC,{WebGPUBackend:()=>Nx,webgpu_util:()=>rC});$u.isBrowser()&&bx()&&ol("webgpu",async()=>{K().set("CHECK_COMPUTATION_FOR_ERRORS",!1);let e={powerPreference:K().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 Nx(r,s)},3);var Jt;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Jt||(Jt={}));var vp;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid",e[e.elu=6]="elu"})(vp||(vp={}));var BC;function ghe(e){BC=e.wasm.cwrap(xo,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function Ahe(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 N=n.dataIdMap.get(o.dataId);if(N.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${N.shape.length}.`);f=N.id}let m=i==null?0:n.dataIdMap.get(i.dataId).id,g=vp[u];if(g==null)throw new Error(`${u} activation not yet supported for FusedConv2D in the wasm backend.`);let A=l?r.shape[2]:r.shape[1],x=c?a.shape[1]:a.shape[2],y=sl.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)),b=n.makeOutput([...y,A,x],r.dtype),w=n.dataIdMap.get(b.dataId).id,k=new Uint8Array(new Int32Array(r.shape).buffer),C=new Uint8Array(new Int32Array(a.shape).buffer);return BC(p,k,r.shape.length,h,C,a.shape.length,l,c,g,f,m,d||0,w),b}var yhe={kernelName:xo,backendName:"wasm",setupFunc:ghe,kernelFunc:Ahe};function En(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,Jt[i.dtype],u),c}return{kernelName:e,backendName:"wasm",setupFunc:s,kernelFunc:r}}var xhe=En(di);function Xn(e,t,n){let s;function r(o){s=o.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function a(o){let{backend:i,inputs:l}=o,{a:c,b:u}=l,d=i.dataIdMap.get(c.dataId).id,p=i.dataIdMap.get(u.dataId).id,h=n!=null?n:c.dtype,f=E.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),A=new Uint8Array(new Int32Array(u.shape).buffer),x=i.dataIdMap.get(m.dataId).id,y=()=>s(d,g,c.shape.length,p,A,u.shape.length,Jt[c.dtype],x);if(t&&c.dtype==="float32")return y(),m;let b=E.getBroadcastDims(c.shape,f),w=E.getBroadcastDims(u.shape,f),k=b.every((N,R)=>N===R),C=w.every((N,R)=>N===R);if(k&&C)return y(),m;throw new Error(`Broadcasting along outer dims is not yet supported for ${c.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:a}}var bhe=!0,vhe=Xn(Hr,bhe),WC;function whe(e){WC=e.wasm.cwrap(Ta,null,["array","number","number","number"])}function khe(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 WC(a,r.length,Jt[s.dtype],o),s}var She={kernelName:Ta,backendName:"wasm",setupFunc:whe,kernelFunc:khe};function Qm(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 Ihe={kernelName:Ha,backendName:"wasm",kernelFunc:Qm},VC;function Che(e){VC=e.wasm.cwrap(Ao,null,["number","array","number","number","number","array","number"])}function vc(e){let{inputs:t,backend:n,attrs:s}=e,[r,a]=Nhe(t.x.shape,s.perm),o=!0;for(let f=0;f<a.length;f++)a[f]!==f&&(o=!1);let i=The(t.x.shape,s.perm),l={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(o){let f=Qm({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 VC(u,h,l.shape.length,Jt[l.dtype],d,p,a.length),c}function The(e,t){let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];return n}function Nhe(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 Ehe={kernelName:Ao,backendName:"wasm",kernelFunc:vc,setupFunc:Che};function jo(e,t,n){let s=e.shape,r=e.shape.length,a=v.parseAxisParam(t,s),o=a,i=E.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=E.getInnerMostAxes(o.length,r),l=vc({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 UC;function Rhe(e){UC=e.wasm.cwrap(au,null,["number, number, number"])}function $he(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=jo(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;c=u,l=y}let f=c.shape.length;E.assertAxesAreInnerMostDims("all",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;UC(l,A,y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var _he={kernelName:au,backendName:"wasm",setupFunc:Rhe,kernelFunc:$he},GC;function Dhe(e){GC=e.wasm.cwrap(ou,null,["number, number, number"])}function Phe(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}=jo(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;c=u,l=y}let f=c.shape.length;E.assertAxesAreInnerMostDims("any",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;GC(l,A,y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var Fhe={kernelName:ou,backendName:"wasm",setupFunc:Dhe,kernelFunc:Phe},HC;function Ohe(e){HC=e.wasm.cwrap(Na,null,["number","number","number","number","number"])}function Mhe(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}=jo(a,r,t);if(d){let A=t.dataIdMap.get(c.dataId).id;A!==o&&(l=c,i=A)}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 HC(i,Jt[l.dtype],m,g,f),d&&t.disposeData(c.dataId),h}var zhe={kernelName:Na,backendName:"wasm",kernelFunc:Mhe,setupFunc:Ohe},jC;function Lhe(e){jC=e.wasm.cwrap(Ea,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Bhe(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=E.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,A=u.strideHeight,x=u.strideWidth,y=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 jC(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,x,y,w),b}var Whe={kernelName:Ea,backendName:"wasm",setupFunc:Lhe,kernelFunc:Bhe};function ls(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: ${s.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(s.dataId),{dataId:s.dataId,shape:o,dtype:s.dtype}}var Vhe={kernelName:Oi,backendName:"wasm",kernelFunc:ls},qC;function Uhe(e){qC=e.wasm.cwrap(Ra,null,["number","array","number","number","array","number","number","number","number"])}function Ghe(e){let{inputs:t,backend:n,attrs:s}=e,{a:r,b:a}=t,{transposeA:o,transposeB:i}=s;if(r.dtype!=="float32"||a.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let l=r.shape.length,c=a.shape.length,u=o?r.shape[l-2]:r.shape[l-1],d=i?a.shape[c-1]:a.shape[c-2],p=o?r.shape[l-1]:r.shape[l-2],h=i?a.shape[c-2]:a.shape[c-1],f=r.shape.slice(0,-2),m=a.shape.slice(0,-2),g=v.sizeFromShape(f),A=v.sizeFromShape(m),y=sl.assertAndGetBroadcastShape(r.shape.slice(0,-2),a.shape.slice(0,-2)).concat([p,h]);v.assert(u===d,()=>`Error in matMul: inner shapes (${u}) and (${d}) of Tensors with shapes ${r.shape} and ${a.shape} and transposeA=${o} and transposeB=${i} must match.`);let b=o?[g,u,p]:[g,p,u],w=i?[A,h,d]:[A,d,h],k=ls({inputs:{x:r},backend:n,attrs:{shape:b}}),C=ls({inputs:{x:a},backend:n,attrs:{shape:w}}),N=n.dataIdMap.get(k.dataId).id,R=n.dataIdMap.get(C.dataId).id,F=o?k.shape[2]:k.shape[1],_=i?C.shape[1]:C.shape[2],P=Math.max(g,A),T=n.makeOutput([P,F,_],k.dtype),M=n.dataIdMap.get(T.dataId).id,U=new Uint8Array(new Int32Array(k.shape).buffer),j=new Uint8Array(new Int32Array(C.shape).buffer);return qC(N,U,k.shape.length,R,j,C.shape.length,o,i,M),n.disposeData(k.dataId),n.disposeData(C.dataId),T.shape=y,T}var Hhe={kernelName:Ra,backendName:"wasm",setupFunc:Uhe,kernelFunc:Ghe};function wp(e){let{inputs:{x:t},attrs:{begin:n,size:s},backend:r}=e,[a,o]=Ft.parseSliceParams(t,n,s),i=Ft.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=Ft.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=Sm(l,a,o,t.shape,t.dtype);return d.stringBytes=f,c}let p=r.typedArrayFromHeap(c),h=t.shape.length;if(h===2)jhe(l,u[0],p,a,o);else if(h===3)qhe(l,u[0],u[1],p,a,o);else if(h===4)Xhe(l,u[0],u[1],u[2],p,a,o);else{let f=Sm(l,a,o,t.shape,t.dtype);p.set(f)}return c}function jhe(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 qhe(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 Xhe(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 A=u;A<h;A++){let x=m*t+g*n+A*s+f;r.set(e.subarray(x,x+o[3]),i),i+=o[3]}}var Khe={kernelName:Wi,backendName:"wasm",kernelFunc:wp};function Zhe(e){let{inputs:t,backend:n,attrs:s}=e,{x:r}=t,{blockShape:a,crops:o}=s,i=a.reduce((A,x)=>A*x),l=E.getReshaped(r.shape,a,i),c=E.getPermuted(l.length,a.length),u=E.getReshapedPermuted(r.shape,a,i),d=E.getSliceBeginCoords(o,a.length),p=E.getSliceSize(u,o,a.length),h=ls({inputs:{x:r},backend:n,attrs:{shape:l}}),f=vc({inputs:{x:h},backend:n,attrs:{perm:c}}),m=ls({inputs:{x:f},backend:n,attrs:{shape:u}}),g=wp({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 Yhe={kernelName:pi,backendName:"wasm",kernelFunc:Zhe};function kp(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 Jhe={kernelName:$a,backendName:"wasm",kernelFunc:kp},Qhe=En(_a),XC;function efe(e){XC=e.wasm.cwrap(jr,null,["number","number","number","number"])}function tfe(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 XC(i,a,o,c),l}var nfe={kernelName:jr,backendName:"wasm",setupFunc:efe,kernelFunc:tfe};function KC(e){let{inputs:t,backend:n}=e,s=v.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=E.computeOutShape(t.map(h=>h.shape),s),a=t.filter(h=>v.sizeFromShape(h.shape)>0);if(a.length===1)return Qm({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(E.assertParamsConsistent(i,s),a[0].dtype==="string"){let h=a.map(y=>{let b=v.sizeFromShape(y.shape.slice(s));return ls({inputs:{x:y},backend:n,attrs:{shape:[-1,b]}})}),f=h.map(y=>({vals:n.readSync(y.dataId),shape:y.shape}));r=E.computeOutShape(h.map(y=>y.shape),1);let m=h[0].shape[0]===1,g=My(f,r,t[0].dtype,m),A=E.computeOutShape(a.map(y=>y.shape),s);o.shape=A;let x=n.dataIdMap.get(o.dataId);return x.stringBytes=E.fromStringArrayToUint8(g),h.forEach(y=>n.disposeData(y.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],A=h*g,x=d[m].subarray(A,A+g);p.set(x,f),f+=g}}return o}var sfe={kernelName:hi,backendName:"wasm",kernelFunc:KC},ZC;function rfe(e){ZC=e.wasm.cwrap(Da,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function afe(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=E.convertConv2DDataFormat(p),f=E.computeConv2DInfo(r.shape,a.shape,l,c,u,d,!1,h),m=f.filterHeight,g=f.filterWidth,A=f.padInfo.top,x=f.padInfo.right,y=f.padInfo.bottom,b=f.padInfo.left,w=f.dilationHeight,k=f.dilationWidth,C=f.strideHeight,N=f.strideWidth,R=f.inChannels,F=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 P=s.makeOutput(f.outShape,"float32"),T=s.dataIdMap.get(P.dataId).id;return ZC(o,r.shape[0],r.shape[1],r.shape[2],i,m,g,A,x,y,b,_,w,k,C,N,R,F,T),P}var ofe={kernelName:Da,backendName:"wasm",setupFunc:rfe,kernelFunc:afe},YC;function ife(e){YC=e.wasm.cwrap(Pa,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 lfe(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=E.convertConv2DDataFormat(l),h=E.computeConv2DInfo(u,a.shape,o,d,i,c,!1,p),{batchSize:f,filterHeight:m,filterWidth:g,inChannels:A,inHeight:x,inWidth:y,outChannels:b,outHeight:w,outWidth:k,strideHeight:C,strideWidth:N}=h,R=m-1-h.padInfo.top,F=g-1-h.padInfo.left,_=h.dataFormat==="channelsLast",P=v.computeStrides(h.inShape),T=v.computeStrides(r.shape),[M,U,j]=v.computeStrides(a.shape),z=P[0],X=_?P[1]:P[2],Z=_?P[2]:1,J=_?1:P[1],ee=T[0],re=_?T[1]:T[2],Q=_?T[2]:1,te=_?1:T[1],oe=t.makeOutput(h.inShape,"float32"),fe=t.dataIdMap.get(oe.dataId).id,be=t.dataIdMap.get(r.dataId).id,we=t.dataIdMap.get(a.dataId).id;return YC(be,we,f,m,g,x,y,A,w,k,b,C,N,R,F,M,U,j,z,X,Z,J,ee,re,Q,te,fe),oe}var ufe={kernelName:Pa,backendName:"wasm",setupFunc:ife,kernelFunc:lfe},cfe=En(Fa),dfe=En(Oa),Ex;(function(e){e[e.bilinear=0]="bilinear",e[e.nearest=1]="nearest"})(Ex||(Ex={}));var JC;function pfe(e){JC=e.wasm.cwrap(mi,null,["number","number","number","number","array","number","number","number","number","number"])}function hfe(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=kp({backend:t,inputs:{x:i},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(m.dataId));let g=f.id,A=t.dataIdMap.get(l.dataId).id,x=t.dataIdMap.get(c.dataId).id,y=t.makeOutput(h,"float32"),b=t.dataIdMap.get(y.dataId).id,w=new Uint8Array(new Int32Array(i.shape).buffer);return JC(g,A,x,u,w,d,p,Ex[r],a,b),m!=null&&t.disposeData(m.dataId),y}var ffe={kernelName:mi,backendName:"wasm",setupFunc:pfe,kernelFunc:hfe},QC;function mfe(e){QC=e.wasm.cwrap(fi,null,["number","number","number","number","number","number"])}function gfe(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=E.getAxesPermutation([a],l),u=r;c!==null&&(u=vc({inputs:{x:r},attrs:{perm:c},backend:n}));let d=E.getInnerMostAxes(1,l)[0];E.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;QC(f,o?1:0,i?1:0,h,m,Jt[r.dtype]);let g=p;if(c!==null){let A=E.getUndoAxesPermutation(c);g=vc({inputs:{x:p},attrs:{perm:A},backend:n}),n.disposeData(u.dataId),n.disposeData(p.dataId)}return g}var Afe={kernelName:fi,backendName:"wasm",setupFunc:mfe,kernelFunc:gfe},e6;function yfe(e){e6=e.wasm.cwrap(gi,null,["number","number","number","array","number","array","array","number","number"])}function xfe(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"),A=t.dataIdMap.get(r.dataId).id,x=new Uint8Array(new Int32Array(v.computeStrides(r.shape)).buffer),y=new Uint8Array(new Int32Array(f).buffer),b=new Uint8Array(new Int32Array(v.computeStrides(f)).buffer),w=t.dataIdMap.get(m.dataId).id;return e6(A,a,o==="NHWC"?1:0,x,r.shape.length-1,y,b,f.length,w),m}var bfe={kernelName:gi,backendName:"wasm",setupFunc:yfe,kernelFunc:xfe},t6;function vfe(e){t6=e.wasm.cwrap(Ma,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function wfe(e){let{inputs:t,attrs:n,backend:s}=e,{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=E.computeConv2DInfo(r.shape,a.shape,l,p,u,d,!0),f=h.filterHeight,m=h.filterWidth,g=h.padInfo.top,A=h.padInfo.right,x=h.padInfo.bottom,y=h.padInfo.left,b=h.dilationHeight,w=h.dilationWidth,k=h.strideHeight,C=h.strideWidth,N=h.inChannels,R=h.outChannels,F=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"),P=s.dataIdMap.get(_.dataId).id;return t6(o,r.shape[0],r.shape[1],r.shape[2],i,f,m,g,A,x,y,F,b,w,k,C,N,R,P),_}var kfe={kernelName:Ma,backendName:"wasm",setupFunc:vfe,kernelFunc:wfe},Sfe=En(La),Ife=!1,Cfe=Xn(Ai,Ife,"bool"),Tfe=En(Ba,"float32");function Rx(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),ls({inputs:{x:r},backend:s,attrs:{shape:i}})}var Nfe={kernelName:yi,backendName:"wasm",kernelFunc:Rx};function n6(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 Efe={kernelName:fu,backendName:"wasm",kernelFunc:n6},s6;function Rfe(e){s6=e.wasm.cwrap(bi,null,["number","number","number","number","number","number"])}function $fe(e){let{inputs:t,backend:n}=e,{image:s}=t,r=n.makeOutput(s.shape,s.dtype),a=n.dataIdMap.get(s.dataId).id,o=n.dataIdMap.get(r.dataId).id,[i,l,c,u]=s.shape;return s6(a,i,l,c,u,o),r}var _fe={kernelName:bi,backendName:"wasm",kernelFunc:$fe,setupFunc:Rfe},Dfe=En(Wa),Pfe=!1,Ffe=Xn(Va,Pfe),r6;function Ofe(e){r6=e.wasm.cwrap(Ua,null,["number","number","number","number","number","number","number"])}function Mfe(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 r6(u,d,p,h,f,r,g),m}var zfe={kernelName:Ua,backendName:"wasm",setupFunc:Ofe,kernelFunc:Mfe},a6;function Lfe(e){a6=e.wasm.cwrap(bo,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 Bfe(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=E.computeConv2DInfo(r.shape,a.shape,l,u,c,p),g=vp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,y=m.outChannels,b=0;if(o!=null){let Q=s.dataIdMap.get(o.dataId);if(Q.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==y)throw new Error(`FusedConv2D bias shape (${Q.shape}) does not match the number of output channels (${y})`);b=Q.id}let w=m.filterHeight,k=m.filterWidth,C=m.padInfo.top,N=m.padInfo.right,R=m.padInfo.bottom,F=m.padInfo.left,_=m.dilationHeight,P=m.dilationWidth,T=m.strideHeight,M=m.strideWidth,U=m.inChannels,j=m.padInfo.type==="SAME"?1:0,z=m.batchSize,X=m.inHeight,Z=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(J.dataId).id,re=i==null?0:s.dataIdMap.get(i.dataId).id;return a6(A,z,X,Z,x,w,k,b,C,N,R,F,j,_,P,T,M,U,y,g,re,f||0,ee),J}var Wfe={kernelName:bo,backendName:"wasm",setupFunc:Lfe,kernelFunc:Bfe},o6;function Vfe(e){o6=e.wasm.cwrap(vo,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 Ufe(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=E.computeConv2DInfo(r.shape,a.shape,l,u,c,p,!0),g=vp[h];if(g==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let A=s.dataIdMap.get(r.dataId).id,x=s.dataIdMap.get(a.dataId).id,y=m.outChannels,b=0;if(o!=null){let Q=s.dataIdMap.get(o.dataId);if(Q.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Q.shape.length}.`);if(Q.shape[0]!==y)throw new Error(`FusedDepthwiseConv2D bias shape (${Q.shape}) does not match the number of output channels (${y})`);b=Q.id}let w=m.filterHeight,k=m.filterWidth,C=m.padInfo.top,N=m.padInfo.right,R=m.padInfo.bottom,F=m.padInfo.left,_=m.dilationHeight,P=m.dilationWidth,T=m.strideHeight,M=m.strideWidth,U=m.inChannels,j=m.padInfo.type==="SAME"?1:0,z=m.batchSize,X=m.inHeight,Z=m.inWidth;if(d!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${d}'. Please use 'NHWC'.`);let J=s.makeOutput(m.outShape,"float32"),ee=s.dataIdMap.get(J.dataId).id,re=i==null?0:s.dataIdMap.get(i.dataId).id;return o6(A,z,X,Z,x,w,k,b,C,N,R,F,j,_,P,T,M,U,y,g,re,f||0,ee),J}var Gfe={kernelName:vo,backendName:"wasm",setupFunc:Vfe,kernelFunc:Ufe},i6;function Hfe(e){i6=e.wasm.cwrap(wi,null,["number","number","number","number","number","number","array","number"])}function jfe(e){let{backend:t,inputs:n}=e,{params:s,indices:r}=n,[a,o,i,l]=_2.prepareAndValidate(s,r),c=t.makeOutput(a,s.dtype);if(o===0)return c;let u=r.shape,d=u[u.length-1],h=t.dataIdMap.get(s.dataId).id,m=t.dataIdMap.get(r.dataId).id,g=new Uint8Array(new Int32Array(l).buffer),A=t.dataIdMap.get(c.dataId).id;return i6(h,Jt[s.dtype],m,o,d,i,g,A),c}var qfe={kernelName:wi,backendName:"wasm",setupFunc:Hfe,kernelFunc:jfe},l6;function Xfe(e){l6=e.wasm.cwrap("Gather",null,["number","number","array","number","number","number","array","number"])}function Kfe(e){let{backend:t,inputs:n,attrs:s}=e,{x:r,indices:a}=n,{axis:o,batchDims:i}=s,l=v.parseAxisParam(o,r.shape)[0],c=t.readSync(a.dataId),u=r.shape[l];for(let R=0;R<c.length;++R){let F=c[R];v.assert(F<=u-1&&F>=0,()=>`GatherV2: the index value ${F} is not in [0, ${u-1}]`)}let d=E.segment_util.collectGatherOpShapeInfo(r,a,l,i),p=ls({inputs:{x:r},attrs:{shape:[d.batchSize,d.outerSize,d.dimSize,d.sliceSize]},backend:t}),h=v.sizeFromShape(a.shape),f=ls({inputs:{x:a},attrs:{shape:[d.batchSize,h/d.batchSize]},backend:t}),m=[d.batchSize,d.outerSize,h/d.batchSize,d.sliceSize],g=t.makeOutput(m,r.dtype);if(v.sizeFromShape(r.shape)===0)return g;let A=p.shape.length-1,y=t.dataIdMap.get(p.dataId).id,w=t.dataIdMap.get(f.dataId).id,k=t.dataIdMap.get(g.dataId).id,C=new Uint8Array(new Int32Array(v.computeStrides(p.shape)).buffer),N=new Uint8Array(new Int32Array(v.computeStrides(m)).buffer);return l6(y,Jt[r.dtype],C,A,w,d.batchSize,N,k),t.disposeData(p.dataId),t.disposeData(f.dataId),g.shape=d.outputShape,g}var Zfe={kernelName:vi,backendName:"wasm",setupFunc:Xfe,kernelFunc:Kfe},Yfe=!1,Jfe=Xn(ki,Yfe,"bool"),Qfe=!1,eme=Xn(Ga,Qfe,"bool"),u6;function tme(e){u6=e.wasm.cwrap(Si,null,["number","number","number","number"])}function nme(e){let{inputs:{x:t},attrs:{alpha:n},backend:s}=e,r=s.dataIdMap.get(t.dataId).id,a=s.makeOutput(t.shape,"float32");if(v.sizeFromShape(t.shape)!==0){let o=s.dataIdMap.get(a.dataId).id;u6(r,Jt[t.dtype],n,o)}return a}var sme={kernelName:Si,backendName:"wasm",setupFunc:tme,kernelFunc:nme},rme=!1,ame=Xn(Ii,rme,"bool"),ome=!1,ime=Xn(Ci,ome,"bool"),lme=En(ja),ume=!1,cme=Xn(Ti,ume,"bool"),c6;function dme(e){c6=e.wasm.cwrap(qa,null,["number","number","number","number"])}function pme(e){let{backend:t,inputs:n,attrs:s}=e,{reductionIndices:r,keepDims:a}=s,{x:o}=n,l=t.dataIdMap.get(o.dataId).id,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=jo(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;c=u,l=y}let f=c.shape.length;E.assertAxesAreInnerMostDims("max",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,o.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;c6(l,Jt[o.dtype],A,y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var hme={kernelName:qa,backendName:"wasm",setupFunc:dme,kernelFunc:pme},fme=!1,mme=Xn(Xa,fme),d6;function gme(e){d6=e.wasm.cwrap(Ka,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function Ame(e){let{inputs:t,attrs:n,backend:s}=e,r=t.x,a=s.dataIdMap.get(r.dataId).id;v.assert(r.dtype==="float32",()=>`Error in MaxPool: only float32 input is supported. Got ${r.dtype}.`);let{filterSize:o,strides:i,pad:l,dimRoundingMode:c}=n,u=E.computePool2DInfo(r.shape,o,i,1,l,c),d=u.filterHeight,p=u.filterWidth,h=u.padInfo.top,f=u.padInfo.right,m=u.padInfo.bottom,g=u.padInfo.left,A=u.dilationHeight,x=u.dilationWidth,y=u.strideHeight,b=u.strideWidth,w=u.inChannels,k=u.outChannels;if(u.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${u.dataFormat}'. Please use 'channelsLast'.`);let C=s.makeOutput(u.outShape,"float32"),N=s.dataIdMap.get(C.dataId).id;return d6(a,r.shape[0],r.shape[1],r.shape[2],d,p,h,f,m,g,A,x,y,b,w,k,N),C}var yme={kernelName:Ka,backendName:"wasm",setupFunc:gme,kernelFunc:Ame},p6;function xme(e){p6=e.wasm.cwrap(Za,null,["number, number, number"])}function bme(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=jo(o,r,t),f=d;if(h){let b=t.dataIdMap.get(u.dataId).id;b!==i&&(c=u,l=b,f=E.getInnerMostAxes(f.length,c.shape.length))}E.assertAxesAreInnerMostDims("mean",f,c.shape.length);let[m,g]=E.computeOutAndReduceShapes(c.shape,f),A=v.sizeFromShape(g),x=c;c.dtype!=="float32"&&(x=kp({backend:t,inputs:{x:c},attrs:{dtype:"float32"}}),l=t.dataIdMap.get(x.dataId).id);let y=t.makeOutput(m,"float32");if(v.sizeFromShape(c.shape)!==0){let b=t.dataIdMap.get(y.dataId).id;p6(l,A,b)}if(h&&t.disposeData(u.dataId),a){let b=E.expandShapeToKeepDim(y.shape,p);y.shape=b}return c.dtype!=="float32"&&t.disposeData(x.dataId),y}var vme={kernelName:Za,backendName:"wasm",setupFunc:xme,kernelFunc:bme},h6;function wme(e){h6=e.wasm.cwrap(Ya,null,["number","number","number","number"])}function kme(e){let{backend:t,inputs:n,attrs:s}=e,{axis:r,keepDims:a}=s,{x:o}=n,i=t.dataIdMap.get(o.dataId).id,l=i,c=o,{transposed:u,axes:d,originalAxes:p,inputWasTransposed:h}=jo(o,r,t);if(h){let y=t.dataIdMap.get(u.dataId).id;y!==i&&(c=u,l=y)}let f=c.shape.length;E.assertAxesAreInnerMostDims("min",d,f);let[m,g]=E.computeOutAndReduceShapes(c.shape,d),A=v.sizeFromShape(g),x=t.makeOutput(m,c.dtype);if(v.sizeFromShape(c.shape)!==0){let y=t.dataIdMap.get(x.dataId).id;h6(l,Jt[o.dtype],A,y)}if(h&&t.disposeData(u.dataId),a){let y=E.expandShapeToKeepDim(x.shape,p);x.shape=y}return x}var Sme={kernelName:Ya,backendName:"wasm",setupFunc:wme,kernelFunc:kme},Ime=!1,Cme=Xn(Ja,Ime),$x;(function(e){e[e.reflect=0]="reflect",e[e.symmetric=1]="symmetric"})($x||($x={}));var f6;function Tme(e){f6=e.wasm.cwrap(Qa,null,["number","array","number","number","array","array","number","number"])}function Nme(e){let{inputs:{x:t},backend:n,attrs:{paddings:s,mode:r}}=e,a=s.map((f,m)=>f[0]+t.shape[m]+f[1]),o=n.dataIdMap.get(t.dataId).id,i=n.makeOutput(a,t.dtype),l=n.dataIdMap.get(i.dataId).id,c=new Uint8Array(new Int32Array(t.shape).buffer),u=s.map(f=>f[0]),d=s.map(f=>f[1]),p=new Uint8Array(new Int32Array(u).buffer),h=new Uint8Array(new Int32Array(d).buffer);return f6(o,c,t.shape.length,Jt[t.dtype],p,h,$x[r],l),i}var Eme={kernelName:Qa,backendName:"wasm",kernelFunc:Nme,setupFunc:Tme},Rme=!0,$me=Xn(eo,Rme),_me=En(Ni);function _x(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),s=n[0],r=n[1],a=n[2],o=n[3];return e.wasm._free(t),{pSelectedIndices:s,selectedSize:r,pSelectedScores:a,pValidOutputs:o}}var m6;function Dme(e){m6=e.wasm.cwrap(Ri,"number",["number","number","number","number","number"])}function Pme(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o}=s,{boxes:i,scores:l}=n,c=t.dataIdMap.get(i.dataId).id,u=t.dataIdMap.get(l.dataId).id,d=m6(c,u,a,r,o),{pSelectedIndices:p,selectedSize:h,pSelectedScores:f,pValidOutputs:m}=_x(t,d);return t.wasm._free(f),t.wasm._free(m),t.makeOutput([h],"int32",p)}var Fme={kernelName:Ri,backendName:"wasm",setupFunc:Dme,kernelFunc:Pme},g6;function Ome(e){g6=e.wasm.cwrap(vu,"number",["number","number","number","number","number","bool"])}function Mme(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,padToMaxOutputSize:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=g6(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=_x(t,p);t.wasm._free(m);let A=t.makeOutput([f],"int32",h),x=t.makeOutput([],"int32",g);return[A,x]}var zme={kernelName:vu,backendName:"wasm",setupFunc:Ome,kernelFunc:Mme},A6;function Lme(e){A6=e.wasm.cwrap($i,"number",["number","number","number","number","number","number"])}function Bme(e){let{backend:t,inputs:n,attrs:s}=e,{iouThreshold:r,maxOutputSize:a,scoreThreshold:o,softNmsSigma:i}=s,{boxes:l,scores:c}=n,u=t.dataIdMap.get(l.dataId).id,d=t.dataIdMap.get(c.dataId).id,p=A6(u,d,a,r,o,i),{pSelectedIndices:h,selectedSize:f,pSelectedScores:m,pValidOutputs:g}=_x(t,p);t.wasm._free(g);let A=t.makeOutput([f],"int32",h),x=t.makeOutput([f],"float32",m);return[A,x]}var Wme={kernelName:$i,backendName:"wasm",setupFunc:Lme,kernelFunc:Bme},Vme=!1,Ume=Xn(Ei,Vme,"bool"),y6;function Gme(e){y6=e.wasm.cwrap(Di,null,["number","number","number","number","number"])}function Hme(e){let{inputs:t,backend:n,attrs:s}=e,{indices:r}=t,{depth:a,onValue:o,offValue:i}=s,l=n.makeOutput([...r.shape,a],"int32"),c=n.dataIdMap.get(l.dataId).id,d=n.dataIdMap.get(r.dataId).id;return y6(d,a,o,i,c),l}var jme={kernelName:Di,backendName:"wasm",setupFunc:Gme,kernelFunc:Hme};function qme(e){let{inputs:{x:t},backend:n}=e,s=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(s).fill(1),s}var Xme={kernelName:_i,backendName:"wasm",kernelFunc:qme};function Kme(e){let{inputs:t,backend:n,attrs:s}=e,{axis:r}=s;if(t.length===1)return Rx({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 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eu{constructor(e){super();this.wasm=e,this.dataIdNextNumber=1,this.wasm.tfjs.initWithThreadsCount(B6),Fx=this.wasm.tfjs.getThreadsCount(),this.dataIdMap=new Zc(this,ss())}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){let{memoryOffset:t,dtype:n,shape:s,stringBytes:r}=this.dataIdMap.get(e);if(n==="string")return r;let 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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 ${t}`)}}};function bge(e){return(t,n)=>(v.fetch(e,{credentials:"same-origin"}).then(s=>{s.ok||t.env.a(`failed to load wasm binary file at '${e}'`),s.arrayBuffer().then(r=>{WebAssembly.instantiate(r,t).then(a=>{n(a.instance,a.module)})})}),{})}function z6(e,t,n){if(e0!=null)return e0;let s="tfjs-backend-wasm.wasm";return e&&t?s="tfjs-backend-wasm-threaded-simd.wasm":e&&(s="tfjs-backend-wasm-simd.wasm"),Ip!=null&&Ip[s]!=null?Ip[s]:n+s}async function vge(){let[e,t]=await Promise.all([K().getAsync("WASM_HAS_SIMD_SUPPORT"),K().getAsync("WASM_HAS_MULTITHREAD_SUPPORT")]);return new Promise((n,s)=>{let r={};r.locateFile=(i,l)=>{if(i.endsWith(".worker.js")){let c=yge,u=new Blob([c],{type:"application/javascript"});return URL.createObjectURL(u)}return i.endsWith(".wasm")?z6(e,t,Sp!=null?Sp:l):l+i},Px&&(r.instantiateWasm=bge(z6(e,t,Sp!=null?Sp:"")));let a=!1;r.onAbort=()=>{if(a||Cp)return;Cp=!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&&e0==null?(r.mainScriptUrlOrBlob=new Blob(["var WasmBackendModuleThreadedSimd = "+O6.default.toString()],{type:"text/javascript"}),o=(0,O6.default)(r)):o=(0,xge.default)(r),o.then(i=>{a=!0,Cp=!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 wge(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 kge=["tfjs-backend-wasm.wasm","tfjs-backend-wasm-simd.wasm","tfjs-backend-wasm-threaded-simd.wasm"],e0=null,Sp=null,Ip={},Cp=!1,Px=!1;function Sge(e,t=!1){if(B2("setWasmPath has been deprecated in favor of setWasmPaths and will be removed in a future release."),Cp)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");e0=e,Px=t}function L6(e,t=!1){if(Cp)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")Sp=e;else{Ip=e;let n=kge.filter(s=>Ip[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.`)}Px=t}var B6=-1,Fx=-1;function Ige(e){B6=e}function Cge(){if(Fx===-1)throw new Error("WASM backend not initialized.");return Fx}var Tge="0.0.0",Nge=2;ol("wasm",async()=>{let{wasm:e}=await vge();return new M6(e)},Nge);var qo="3.11.0-20211110",W6={tfjs:qo,"tfjs-core":qo,"tfjs-data":qo,"tfjs-layers":qo,"tfjs-converter":qo,"tfjs-backend-cpu":qo,"tfjs-backend-webgl":qo,"tfjs-backend-wasm":qo},Tp=W6["tfjs-core"];var V6=`
|
|
precision highp float;
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|
attribute vec2 pos;
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attribute vec2 uv;
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varying vec2 vUv;
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|
uniform float flipY;
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|
void main(void) {
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|
vUv = uv;
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gl_Position = vec4(pos.x, pos.y*flipY, 0.0, 1.);
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|
}
|
|
`;var U6=`
|
|
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];
|
|
}
|
|
`,G6=`
|
|
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;
|
|
}
|
|
`,H6=`
|
|
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);
|
|
}
|
|
`,j6=`
|
|
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;
|
|
}
|
|
`,q6=`
|
|
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 Ox=(e,t,n)=>{let s=new RegExp("\\b"+t+" \\w+ (\\w+)","ig");e.replace(s,(r,a)=>(n[a]=0,r))},X6=class{constructor(t,n,s){de(this,"uniform",{});de(this,"attribute",{});de(this,"gl");de(this,"id");de(this,"compile",(t,n)=>{let s=this.gl.createShader(n);if(this.gl.shaderSource(s,t),this.gl.compileShader(s),!this.gl.getShaderParameter(s,this.gl.COMPILE_STATUS))throw new Error(`filter: gl compile failed: ${this.gl.getShaderInfoLog(s)}`);return s});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(),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))throw new Error(`filter: gl link failed: ${this.gl.getProgramInfoLog(this.id)}`);this.gl.useProgram(this.id),Ox(n,"attribute",this.attribute);for(let o in 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d.bindTexture(d.TEXTURE_2D,k),d.texImage2D(d.TEXTURE_2D,0,d.RGBA,x,y,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 y=null,b=null,w=!1;e===0?y=t:y=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,y),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;i=new X6(d,V6,x);let y=Float32Array.BYTES_PER_ELEMENT,b=4*y;return d.enableVertexAttribArray(i.attribute.pos),d.vertexAttribPointer(i.attribute.pos,2,d.FLOAT,!1,b,0*y),d.enableVertexAttribArray(i.attribute.uv),d.vertexAttribPointer(i.attribute.uv,2,d.FLOAT,!1,b,2*y),c[x]=i,i}let A={colorMatrix:x=>{let y=new Float32Array(x);y[4]/=255,y[9]/=255,y[14]/=255,y[19]/=255;let b=y[18]===1&&y[3]===0&&y[8]===0&&y[13]===0&&y[15]===0&&y[16]===0&&y[17]===0&&y[19]===0?G6:U6,w=g(b);d.uniform1fv(w.uniform.m,y),m()},brightness:x=>{let y=(x||0)+1;A.colorMatrix([y,0,0,0,0,0,y,0,0,0,0,0,y,0,0,0,0,0,1,0])},saturation:x=>{let y=(x||0)*2/3+1,b=(y-1)*-.5;A.colorMatrix([y,b,b,0,0,b,y,b,0,0,b,b,y,0,0,0,0,0,1,0])},desaturate:()=>{A.saturation(-1)},contrast:x=>{let y=(x||0)+1,b=-128*(y-1);A.colorMatrix([y,0,0,0,b,0,y,0,0,b,0,0,y,0,b,0,0,0,1,0])},negative:()=>{A.contrast(-2)},hue:x=>{x=(x||0)/180*Math.PI;let 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a},this.apply=function(x){p(x.width,x.height),e=0,t||(t=d.createTexture()),d.bindTexture(d.TEXTURE_2D,t),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.texParameteri(d.TEXTURE_2D,d.TEXTURE_MIN_FILTER,d.NEAREST),d.texParameteri(d.TEXTURE_2D,d.TEXTURE_MAG_FILTER,d.NEAREST),d.texImage2D(d.TEXTURE_2D,0,d.RGBA,d.RGBA,d.UNSIGNED_BYTE,x);for(let y=0;y<a.length;y++){n=y===a.length-1;let b=a[y];b.func.apply(this,b.args||[])}return l},this.draw=function(x){return this.add("brightness",0),this.apply(x)}}async function t0(e){let t=e.shape.length===4?ot(e):e,n=sn(t,3,2),s=[Ro(n[0]),Ro(n[1]),Ro(n[2])],r=[An(n[0]),An(n[1]),An(n[2])],a=await 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a=s,o=r;if(a>n0&&(a=n0,o=Math.trunc(a*r/s)),o>n0&&(o=n0,a=Math.trunc(o*s/r)),(t.filter.width||0)>0?a=t.filter.width:(t.filter.height||0)>0&&(a=s*((t.filter.height||0)/r)),(t.filter.height||0)>0?o=t.filter.height:(t.filter.width||0)>0&&(o=r*((t.filter.width||0)/s)),!a||!o)throw new Error("input cannot determine dimension");(!it||(it==null?void 0:it.width)!==a||(it==null?void 0:it.height)!==o)&&(it=Kn(a,o));let i=it.getContext("2d");if(typeof ImageData!="undefined"&&e instanceof ImageData?i.putImageData(e,0,0):t.filter.flip&&typeof i.translate!="undefined"?(i.translate(s,0),i.scale(-1,1),i.drawImage(e,0,0,s,r,0,0,it==null?void 0:it.width,it==null?void 0:it.height),i.setTransform(1,0,0,1,0,0)):i.drawImage(e,0,0,s,r,0,0,it==null?void 0:it.width,it==null?void 0:it.height),(!ln||it.width!==ln.width||(it==null?void 0:it.height)!==(ln==null?void 0:ln.height))&&(ln=Kn(it.width,it.height)),t.filter.enabled&&ge.webgl.supported){if($t||($t=ge.browser?new K6:null),ge.filter=!!$t,!$t)return{tensor:null,canvas:it};$t.reset(),t.filter.brightness!==0&&$t.add("brightness",t.filter.brightness),t.filter.contrast!==0&&$t.add("contrast",t.filter.contrast),t.filter.sharpness!==0&&$t.add("sharpen",t.filter.sharpness),t.filter.blur!==0&&$t.add("blur",t.filter.blur),t.filter.saturation!==0&&$t.add("saturation",t.filter.saturation),t.filter.hue!==0&&$t.add("hue",t.filter.hue),t.filter.negative&&$t.add("negative"),t.filter.sepia&&$t.add("sepia"),t.filter.vintage&&$t.add("brownie"),t.filter.sepia&&$t.add("sepia"),t.filter.kodachrome&&$t.add("kodachrome"),t.filter.technicolor&&$t.add("technicolor"),t.filter.polaroid&&$t.add("polaroid"),t.filter.pixelate!==0&&$t.add("pixelate",t.filter.pixelate),$t.get()>0?ln=$t.apply(it):ln=$t.draw(it)}else Mx(it,ln),$t&&($t=null),ge.filter=!!$t;if(!n)return{tensor:null,canvas:ln};if(!ln)throw new Error("cannot create output canvas");let l,c=3;if(typeof ImageData!="undefined"&&e instanceof ImageData||e.data&&e.width&&e.height)if(ge.browser&&Hs)l=Hs?Hs.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((!wc||ln.width!==wc.width||ln.height!==wc.height)&&(wc=Kn(ln.width,ln.height)),Hs&&ge.browser)t.backend==="webgl"||t.backend==="humangl"||t.backend==="webgpu"?l=Hs.fromPixels(ln):(wc=Mx(ln),l=Hs.fromPixels(wc));else{let f=Mx(ln).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=dl(l,[0,0,0],[-1,-1,3]);ne(l),l=p}if(!l)throw new Error("cannot create tensor from input");let u=pe(l,"float32"),d=t.filter.equalization?await t0(u):Xt(u,0);return ne([l,u]),{tensor:d,canvas:t.filter.return?ln:null}}}async function Z6(e,t){let n=!1;if(e.cacheSensitivity===0)return n;if(!ia.inputTensor)ia.inputTensor=Bn(t);else 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ne([s.input1,s.input2,s.diff,s.squared,s.sum]),a}var J6=class{constructor(){de(this,"browser");de(this,"node");de(this,"worker");de(this,"platform","");de(this,"agent","");de(this,"backends",[]);de(this,"initial");de(this,"filter");de(this,"tfjs");de(this,"offscreen");de(this,"perfadd",!1);de(this,"wasm",{supported:void 0,backend:void 0,simd:void 0,multithread:void 0});de(this,"webgl",{supported:void 0,backend:void 0,version:void 0,renderer:void 0});de(this,"webgpu",{supported:void 0,backend:void 0,adapter:void 0});de(this,"cpu",{model:void 0,flags:[]});de(this,"kernels",[]);de(this,"Canvas");de(this,"Image");de(this,"ImageData");if(this.browser=typeof navigator!="undefined",this.node=typeof process!="undefined",this.tfjs={version:Tp},this.offscreen=typeof 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(ss().registryFactory),this.wasm.supported=typeof WebAssembly!="undefined",this.wasm.backend=this.backends.includes("wasm"),this.wasm.supported&&this.wasm.backend&&Cs()==="wasm"&&(this.wasm.simd=await K().getAsync("WASM_HAS_SIMD_SUPPORT"),this.wasm.multithread=await K().getAsync("WASM_HAS_MULTITHREAD_SUPPORT"));let t=Kn(100,100),n=t?t.getContext("webgl2"):void 0;if(this.webgl.supported=typeof n!="undefined",this.webgl.backend=this.backends.includes("webgl"),this.webgl.supported&&this.webgl.backend&&(Cs()==="webgl"||Cs()==="humangl")){let s=Nr().gpgpu!=="undefined"?await Nr().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"),this.webgpu.supported&&(this.webgpu.adapter=(await navigator.gpu.requestAdapter()).name),this.kernels=Xr(Cs()).map(s=>s.kernelName.toLowerCase())}async updateCPU(){let t={model:"",flags:[]};if(this.node&&this.platform.startsWith("linux")){let n=wa("fs");try{let s=n.readFileSync("/proc/cpuinfo").toString();for(let r of s.split(`
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Rp=e=>[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])],r0=e=>[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2],Ux=(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],Gx=(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],s8=(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}},Hx=(e,t,n)=>{let s=t.shape[1],r=t.shape[2],a=$e.cropAndResize(t,[[e.startPoint[1]/s,e.startPoint[0]/r,e.endPoint[1]/s,e.endPoint[0]/r]],[0],n),o=he(a,255);return ne(a),o},$p=(e,t)=>{let n=r0(e),s=Rp(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}},_p=e=>{let t=r0(e),n=Rp(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}},a0=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}},jx=[[1,0,0],[0,1,0],[0,0,1]],Dge=e=>e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI)),Pge=(e,t)=>Dge(Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]));var r8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]],Ol=(e,t)=>{let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n},Fge=(e,t)=>{let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n},a8=(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(Ol(e[r],Fge(t,a)))}return n},o8=(e,t)=>{let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=r8(t[0],t[1]),o=a8(a,r),i=r8(-t[0],-t[1]);return a8(o,i)},Oge=e=>{let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Ol(t[0],n),-Ol(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]},Mge=(e,t)=>[Ol(e,t[0]),Ol(e,t[1])];function i8(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 l8(e,t,n,s,r){let a=Rp(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?o8(n,[0,0]):jx,c=i?o.map(p=>[...Mge(p,l),p[2]]):o,u=i?Oge(s):jx,d=[...r0({startPoint:t.startPoint,endPoint:t.endPoint}),1];return c.map(p=>[Math.round(p[0]+Ol(d,u[0])),Math.round(p[1]+Ol(d,u[1])),Math.round(p[2]||0)])}function qx(e,t,n,s){let r=t.landmarks.length>=Wx.count?Wx.symmetryLine:Np.symmetryLine,a=0,o=jx,i;if(e&&ge.kernels.includes("rotatewithoffset"))if(a=Pge(t.landmarks[r[0]],t.landmarks[r[1]]),a&&a!==0&&Math.abs(a)>.2){let c=r0({startPoint:t.startPoint,endPoint:t.endPoint}),u=[c[0]/n.shape[2],c[1]/n.shape[1]],d=$e.rotateWithOffset(n,a,0,u);o=o8(-a,c),i=Hx(t,d,[s,s]),ne(d)}else i=Hx(t,n,[s,s]);else i=Hx(t,n,[s,s]);return[a,o,i]}var u8=6,Ms,c8=[],d8=null,zs=0,Xx=()=>zs;async function p8(e){var t,n;return ge.initial&&(Ms=null),Ms?e.debug&&se("cached model:",Ms.modelUrl):(Ms=await Xe(Ze(e.modelBasePath,((t=e.face.detector)==null?void 0:t.modelPath)||"")),!Ms||!Ms.modelUrl?se("load model failed:",(n=e.face.detector)==null?void 0:n.modelPath):e.debug&&se("load model:",Ms.modelUrl)),zs=Ms.inputs[0].shape?Ms.inputs[0].shape[2]:0,zs===-1&&(zs=64),c8=i8(zs),d8=ur(c8),Ms}function zge(e){let t={};t.boxStarts=De(e,[0,1],[-1,2]),t.centers=le(t.boxStarts,d8),t.boxSizes=De(e,[0,3],[-1,2]),t.boxSizesNormalized=he(t.boxSizes,zs),t.centersNormalized=he(t.centers,zs),t.halfBoxSize=he(t.boxSizesNormalized,2),t.starts=me(t.centersNormalized,t.halfBoxSize),t.ends=le(t.centersNormalized,t.halfBoxSize),t.startNormalized=L(t.starts,zs),t.endNormalized=L(t.ends,zs);let n=Mu([t.startNormalized,t.endNormalized],1);return Object.keys(t).forEach(s=>ne(t[s])),n}async function h8(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=$e.resizeBilinear(e,[zs,zs]),n.div=he(n.resized,127.5),n.normalized=me(n.div,.5);let s=Ms==null?void 0:Ms.execute(n.normalized);if(Array.isArray(s)){let d=s.sort((p,h)=>p.size-h.size);n.concat384=vt([d[0],d[2]],2),n.concat512=vt([d[1],d[3]],2),n.concat=vt([n.concat512,n.concat384],1),n.batch=ot(n.concat,0)}else n.batch=ot(s);ne(s),n.boxes=zge(n.batch),n.logits=De(n.batch,[0,0],[-1,1]),n.sigmoid=ds(n.logits),n.scores=ot(n.sigmoid),n.nms=await $e.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=De(n.boxes,[r[d],0],[1,-1]),h.slice=De(n.batch,[r[d],u8-1],[1,-1]),h.squeeze=ot(h.slice),h.landmarks=G(h.squeeze,[u8,-1]),h.startPoint=De(h.bbox,[0,0],[-1,2]),h.endPoint=De(h.bbox,[0,2],[-1,2]),a.push({box:{startPoint:await h.startPoint.data(),endPoint:await h.endPoint.data()},landmarks:await h.landmarks.array(),confidence:p}),Object.keys(h).forEach(f=>ne(h[f]))}}return Object.keys(n).forEach(d=>ne(n[d])),{boxes:a,scaleFactor:[e.shape[2]/zs,e.shape[1]/zs]}}var Yx={};jc(Yx,{connected:()=>Zx,kpt:()=>Kx});var Kx=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","bodyCenter","bodyTop","leftThumb","leftHand","rightThumb","rightHand"],Zx={leftLeg:["leftHip","leftKnee","leftAnkle","leftHeel","leftFoot"],rightLeg:["rightHip","rightKnee","rightAnkle","rightHeel","rightFoot"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist","leftPalm"],rightArm:["rightShoulder","rightElbow","rightWrist","rightPalm"],leftHand:[],rightHand:[],head:[]};var f8={initial:!0},cn=[null,null],Ko=[[0,0],[0,0]],Jx=Number.MAX_SAFE_INTEGER,Qx,o0=null,Zo=[[0,0],[0,0],[0,0],[0,0]],m8=0;async function g8(e){var t,n,s;if(f8.initial&&(cn[0]=null),!cn[0]&&((t=e.body.detector)==null?void 0:t.modelPath)){cn[0]=await Xe(Ze(e.modelBasePath,((n=e.body.detector)==null?void 0:n.modelPath)||""));let r=Object.values(cn[0].modelSignature.inputs);Ko[0][0]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[1].size):0,Ko[0][1]=Array.isArray(r)?parseInt(r[0].tensorShape.dim[2].size):0,!cn[0]||!cn[0].modelUrl?se("load model failed:",(s=e.body.detector)==null?void 0:s.modelPath):e.debug&&se("load model:",cn[0].modelUrl)}else e.debug&&cn[0]&&se("cached model:",cn[0].modelUrl);return cn[0]}async function A8(e){var t;if(f8.initial&&(cn[1]=null),cn[1])e.debug&&se("cached model:",cn[1].modelUrl);else{cn[1]=await Xe(Ze(e.modelBasePath,e.body.modelPath||""));let n=Object.values(cn[1].modelSignature.inputs);Ko[1][0]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[1].size):0,Ko[1][1]=Array.isArray(n)?parseInt(n[0].tensorShape.dim[2].size):0,((t=e.body.modelPath)==null?void 0:t.includes("lite"))?Qx=["ld_3d","output_segmentation","output_heatmap","world_3d","output_poseflag"]:Qx=["Identity","Identity_2","Identity_3","Identity_4","Identity_1"],!cn[1]||!cn[1].modelUrl?se("load model failed:",e.body.modelPath):e.debug&&se("load model:",cn[1].modelUrl)}return cn[1]}function Lge(e,t){let n=e.map(o=>o.position[0]),s=e.map(o=>o.position[1]),r=[Math.min(...n),Math.min(...s),Math.max(...n)-Math.min(...n),Math.max(...s)-Math.min(...s)],a=[r[0]/t[0],r[1]/t[1],r[2]/t[0],r[3]/t[1]];return{keypointsBox:r,keypointsBoxRaw:a}}async function Bge(e){let t={};if(!e.shape||!e.shape[1]||!e.shape[2])return 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m=y8(o[l*f+3]),g=y8(o[l*f+4]),A=Math.trunc(100*m*g*a)/100,x=[o[l*f+0]/Ko[1][0],o[l*f+1]/Ko[1][1],o[l*f+2]+0],y=[Math.trunc(n[0]*x[0]),Math.trunc(n[1]*x[1]),x[2]];i.push({part:Kx[f],positionRaw:x,position:y,score:A})}if(a<(t.body.minConfidence||0))return null;let c=Wge(i,n),u=Lge(c,[n[0],n[1]]);Object.keys(s).forEach(f=>ne(s[f]));let d={};for(let[f,m]of Object.entries(Zx)){let g=[];for(let A=0;A<m.length-1;A++){let x=c.find(b=>b.part===m[A]),y=c.find(b=>b.part===m[A+1]);x&&y&&x.score>(t.body.minConfidence||0)&&y.score>(t.body.minConfidence||0)&&g.push([x.position,y.position])}d[f]=g}return{id:0,score:Math.trunc(100*a)/100,box:u.keypointsBox,boxRaw:u.keypointsBoxRaw,keypoints:c,annotations:d}}async function eb(e,t){let n=[e.shape[2]||0,e.shape[1]||0],s=(t.body.skipTime||0)>ce()-m8,r=Jx<(t.body.skipFrames||0);return t.skipAllowed&&s&&r&&o0!==null?Jx++:(o0=await Vge(e,t,n),m8=ce(),Jx=0),o0?[o0]:[]}var Sc=[{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 meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball 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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 er,Ml=0,tb=[],x8=0,nb=Number.MAX_SAFE_INTEGER;async function b8(e){if(ge.initial&&(er=null),er)e.debug&&se("cached model:",er.modelUrl);else{er=await Xe(Ze(e.modelBasePath,e.object.modelPath||""));let t=Object.values(er.modelSignature.inputs);Ml=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):0,!er||!er.modelUrl?se("load model failed:",e.object.modelPath):e.debug&&se("load model:",er.modelUrl)}return er}async function Uge(e,t,n){if(!e)return[];let s=[],r=await e.array(),a=ot(e);ne(e);let o=sn(a,6,1);ne(a);let i=yn([o[1],o[0],o[3],o[2]],1),l=ot(i);ne(i);let c=ot(o[4]),u=ot(o[5]);o.forEach(f=>ne(f));let d=await 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rb=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","bodyCenter","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"],ab={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var dn,v8=0,Zn={id:0,keypoints:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,annotations:{}},ib=Number.MAX_SAFE_INTEGER;async function lb(e){return ge.initial&&(dn=null),dn?e.debug&&se("cached model:",dn.modelUrl):(dn=await Xe(Ze(e.modelBasePath,e.body.modelPath||"")),!dn||!dn.modelUrl?se("load model failed:",e.body.modelPath):e.debug&&se("load model:",dn.modelUrl)),dn}function Gge(e,t){let[n,s]=e.shape;return q(()=>{let r=G(e,[s*n]),a=An(r,0).dataSync()[0];if(a>t){let o=js(r,0),i=_d(o,n).dataSync()[0],l=he(o,Re(n,"int32")).dataSync()[0];return[i,l,a]}return[0,0,a]})}async function ub(e,t){let n=(t.body.skipTime||0)>ce()-v8,s=ib<(t.body.skipFrames||0);return t.skipAllowed&&n&&s&&Object.keys(Zn.keypoints).length>0?(ib++,[Zn]):(ib=0,new Promise(async r=>{var d;let a=q(()=>{if(!(dn==null?void 0:dn.inputs[0].shape))return null;let p=$e.resizeBilinear(e,[dn.inputs[0].shape[2],dn.inputs[0].shape[1]],!1);return L(p,2).sub(1)}),o;if(t.body.enabled&&(o=dn==null?void 0:dn.execute(a)),v8=ce(),ne(a),o){Zn.keypoints.length=0;let p=o.squeeze();ne(o);let h=p.unstack(2);ne(p);for(let f=0;f<h.length;f++){let[m,g,A]=Gge(h[f],t.body.minConfidence);A>(((d=t.body)==null?void 0:d.minConfidence)||0)&&Zn.keypoints.push({score:Math.round(100*A)/100,part:rb[f],positionRaw:[m/dn.inputs[0].shape[2],g/dn.inputs[0].shape[1]],position:[Math.round(e.shape[2]*m/dn.inputs[0].shape[2]),Math.round(e.shape[1]*g/dn.inputs[0].shape[1])]})}h.forEach(f=>ne(f))}Zn.score=Zn.keypoints.reduce((p,h)=>h.score>p?h.score:p,0);let i=Zn.keypoints.map(p=>p.position[0]),l=Zn.keypoints.map(p=>p.position[1]);Zn.box=[Math.min(...i),Math.min(...l),Math.max(...i)-Math.min(...i),Math.max(...l)-Math.min(...l)];let c=Zn.keypoints.map(p=>p.positionRaw[0]),u=Zn.keypoints.map(p=>p.positionRaw[1]);Zn.boxRaw=[Math.min(...c),Math.min(...u),Math.max(...c)-Math.min(...c),Math.max(...u)-Math.min(...u)];for(let[p,h]of Object.entries(ab)){let f=[];for(let m=0;m<h.length-1;m++){let g=Zn.keypoints.find(x=>x.part===h[m]),A=Zn.keypoints.find(x=>x.part===h[m+1]);g&&A&&g.score>(t.body.minConfidence||0)&&A.score>(t.body.minConfidence||0)&&f.push([g.position,A.position])}Zn.annotations[p]=f}r([Zn])}))}var Hge=["angry","disgust","fear","happy","sad","surprise","neutral"],Yn,i0=[],w8=0,k8=0,cb=Number.MAX_SAFE_INTEGER,db=[.2989,.587,.114];async function S8(e){var t,n;return ge.initial&&(Yn=null),Yn?e.debug&&se("cached model:",Yn.modelUrl):(Yn=await Xe(Ze(e.modelBasePath,((t=e.face.emotion)==null?void 0:t.modelPath)||"")),!Yn||!Yn.modelUrl?se("load model failed:",(n=e.face.emotion)==null?void 0:n.modelPath):e.debug&&se("load model:",Yn.modelUrl)),Yn}async function pb(e,t,n,s){var o,i;if(!Yn)return null;let r=cb<(((o=t.face.emotion)==null?void 0:o.skipFrames)||0),a=(((i=t.face.emotion)==null?void 0:i.skipTime)||0)>ce()-k8;return t.skipAllowed&&a&&r&&w8===s&&i0[n]&&i0[n].length>0?(cb++,i0[n]):(cb=0,new Promise(async l=>{var u,d;let c=[];if((u=t.face.emotion)==null?void 0:u.enabled){let p={},h=(Yn==null?void 0:Yn.inputs[0].shape)?Yn.inputs[0].shape[2]:0;p.resize=$e.resizeBilinear(e,[h,h],!1),[p.red,p.green,p.blue]=sn(p.resize,3,3),p.redNorm=L(p.red,db[0]),p.greenNorm=L(p.green,db[1]),p.blueNorm=L(p.blue,db[2]),p.grayscale=rf([p.redNorm,p.greenNorm,p.blueNorm]),p.grayscaleSub=me(p.grayscale,.5),p.grayscaleMul=L(p.grayscaleSub,2),p.emotion=Yn==null?void 0:Yn.execute(p.grayscaleMul),k8=ce();let f=await p.emotion.data();for(let m=0;m<f.length;m++)f[m]>(((d=t.face.emotion)==null?void 0:d.minConfidence)||0)&&c.push({score:Math.min(.99,Math.trunc(100*f[m])/100),emotion:Hge[m]});c.sort((m,g)=>g.score-m.score),Object.keys(p).forEach(m=>ne(p[m]))}i0[n]=c,w8=s,l(c)}))}var tr,Yo=0,jge=2.3,hb=Wr.leftEyeLower0,fb=Wr.rightEyeLower0,Ic={leftBounds:[hb[0],hb[hb.length-1]],rightBounds:[fb[0],fb[fb.length-1]]},Cc={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};async function I8(e){var t,n;return ge.initial&&(tr=null),tr?e.debug&&se("cached model:",tr.modelUrl):(tr=await Xe(Ze(e.modelBasePath,((t=e.face.iris)==null?void 0:t.modelPath)||"")),!tr||!tr.modelUrl?se("load model failed:",(n=e.face.iris)==null?void 0:n.modelPath):e.debug&&se("load model:",tr.modelUrl)),Yo=tr.inputs[0].shape?tr.inputs[0].shape[2]:0,Yo===-1&&(Yo=64),tr}function l0(e,t,n,s){for(let r=0;r<Vx.length;r++){let{key:a,indices:o}=Vx[r],i=Wr[`${n}${a}`];if(!s||s.includes(a))for(let l=0;l<o.length;l++){let c=o[l];e[i[l]]=[t[c][0],t[c][1],(t[c][2]+e[i[l]][2])/2]}}}var qge=e=>{let t=e[Ic.leftBounds[0]][2],n=e[Ic.rightBounds[0]][2];return t-n},C8=(e,t,n,s,r,a=!1)=>{let o=_p($p(a0([e[n],e[s]]),jge)),i=Rp(o),l=$e.cropAndResize(t,[[o.startPoint[1]/r,o.startPoint[0]/r,o.endPoint[1]/r,o.endPoint[0]/r]],[0],[Yo,Yo]);if(a&&ge.kernels.includes("flipleftright")){let c=$e.flipLeftRight(l);ne(l),l=c}return{box:o,boxSize:i,crop:l}},T8=(e,t,n,s=!1)=>{let r=[];for(let a=0;a<Cc.numCoordinates;a++){let o=e[a*3],i=e[a*3+1],l=e[a*3+2];r.push([(s?1-o/Yo:o/Yo)*n[0]+t.startPoint[0],i/Yo*n[1]+t.startPoint[1],l])}return{rawCoords:r,iris:r.slice(Cc.index)}},N8=(e,t,n)=>{let s=e[Wr[`${n}EyeUpper0`][Cc.upperCenter]][2],r=e[Wr[`${n}EyeLower0`][Cc.lowerCenter]][2],a=(s+r)/2;return t.map((o,i)=>{let l=a;return i===2?l=s:i===4&&(l=r),[o[0],o[1],l]})};async function E8(e,t,n,s){if(!tr)return n.debug&&se("face mesh iris detection requested, but model is not loaded"),e;let{box:r,boxSize:a,crop:o}=C8(e,t,Ic.leftBounds[0],Ic.leftBounds[1],s,!0),{box:i,boxSize:l,crop:c}=C8(e,t,Ic.rightBounds[0],Ic.rightBounds[1],s,!0),u=vt([o,c]);ne(o),ne(c);let d=tr.execute(u);ne(u);let p=await d.data();ne(d);let h=p.slice(0,Cc.numCoordinates*3),{rawCoords:f,iris:m}=T8(h,r,a,!0),g=p.slice(Cc.numCoordinates*3),{rawCoords:A,iris:x}=T8(g,i,l),y=qge(e);Math.abs(y)<30?(l0(e,f,"left",null),l0(e,A,"right",null)):y<1?l0(e,f,"left",["EyeUpper0","EyeLower0"]):l0(e,A,"right",["EyeUpper0","EyeLower0"]);let b=N8(e,m,"left"),w=N8(e,x,"right");return e.concat(b).concat(w)}var Tc=[],nr=null,la=0,mb=Number.MAX_SAFE_INTEGER,R8=0,$8=1.6;async function _8(e,t){var i,l,c,u,d,p,h,f;let n=(((i=t.face.detector)==null?void 0:i.skipTime)||0)>ce()-R8,s=mb<(((l=t.face.detector)==null?void 0:l.skipFrames)||0);if(!t.skipAllowed||!n||!s||Tc.length===0){let m=await h8(e,t);R8=ce(),Tc=[];for(let g of m.boxes){let A={startPoint:g.box.startPoint,endPoint:g.box.endPoint,landmarks:g.landmarks,confidence:g.confidence};Tc.push(_p($p(s8(A,m.scaleFactor),Math.sqrt($8))))}mb=0}else mb++;let r=[],a=[],o=0;for(let m=0;m<Tc.length;m++){let g=Tc[m],A=0,x,y={id:o++,mesh:[],meshRaw:[],box:[0,0,0,0],boxRaw:[0,0,0,0],score:0,boxScore:0,faceScore:0,annotations:{}};if([A,x,y.tensor]=qx(!1,g,e,la),(u=t==null?void 0:t.filter)==null?void 0:u.equalization){let b=await t0(y.tensor);ne(y.tensor),y.tensor=b}if(y.boxScore=Math.round(100*g.confidence)/100,(d=t.face.mesh)==null?void 0:d.enabled)if(!nr)t.debug&&se("face mesh detection requested, but model is not loaded");else{let[b,w,k]=nr.execute(y.tensor),C=await w.data();y.faceScore=Math.round(100*C[0])/100;let N=G(k,[-1,3]),R=await N.array();if(ne([k,N,w,b]),y.faceScore<(((p=t.face.detector)==null?void 0:p.minConfidence)||1))g.confidence=y.faceScore;else{((h=t.face.iris)==null?void 0:h.enabled)&&(R=await E8(R,y.tensor,t,la)),y.mesh=l8(R,g,A,x,la),y.meshRaw=y.mesh.map(F=>[F[0]/(e.shape[2]||0),F[1]/(e.shape[1]||0),(F[2]||0)/la]);for(let F of Object.keys(Wr))y.annotations[F]=Wr[F].map(_=>y.mesh[_]);g=_p({...$p(a0(y.mesh),$8),confidence:g.confidence}),y.box=Ux(g,e),y.boxRaw=Gx(g,e),y.score=y.faceScore,a.push(g),ne(y.tensor),[A,x,y.tensor]=qx((f=t.face.detector)==null?void 0:f.rotation,g,e,la)}}else{y.box=Ux(g,e),y.boxRaw=Gx(g,e),y.score=y.boxScore,y.mesh=g.landmarks.map(b=>[(g.startPoint[0]+g.endPoint[0])/2+(g.endPoint[0]+g.startPoint[0])*b[0]/Xx(),(g.startPoint[1]+g.endPoint[1])/2+(g.endPoint[1]+g.startPoint[1])*b[1]/Xx()]),y.meshRaw=y.mesh.map(b=>[b[0]/(e.shape[2]||0),b[1]/(e.shape[1]||0),(b[2]||0)/la]);for(let b of Object.keys(Np))y.annotations[b]=[y.mesh[Np[b]]]}r.push(y)}return Tc=[...a],r}async function D8(e){var t,n;return ge.initial&&(nr=null),nr?e.debug&&se("cached model:",nr.modelUrl):(nr=await Xe(Ze(e.modelBasePath,((t=e.face.mesh)==null?void 0:t.modelPath)||"")),!nr||!nr.modelUrl?se("load model failed:",(n=e.face.mesh)==null?void 0:n.modelPath):e.debug&&se("load model:",nr.modelUrl)),la=nr.inputs[0].shape?nr.inputs[0].shape[2]:0,la===-1&&(la=64),nr}var P8=Fl,F8=Ep;var xs,u0=[],O8=0,M8=0,gb=Number.MAX_SAFE_INTEGER;async function z8(e){var n,s;let t=Ze(e.modelBasePath,((n=e.face.description)==null?void 0:n.modelPath)||"");return ge.initial&&(xs=null),xs?e.debug&&se("cached model:",t):(xs=await Xe(t),xs?e.debug&&se("load model:",t):se("load model failed:",((s=e.face.description)==null?void 0:s.modelPath)||"")),xs}function Ab(e){let t=e.image||e.tensor||e;if(!(xs==null?void 0:xs.inputs[0].shape))return t;let n=$e.resizeBilinear(t,[xs.inputs[0].shape[2],xs.inputs[0].shape[1]],!1),s=L(n,255);return ne(n),s}async function yb(e,t,n,s){var o,i,l,c;if(!xs)return null;let r=gb<(((o=t.face.description)==null?void 0:o.skipFrames)||0),a=(((i=t.face.description)==null?void 0:i.skipTime)||0)>ce()-O8;return t.skipAllowed&&r&&a&&M8===s&&((l=u0[n])==null?void 0:l.age)&&((c=u0[n])==null?void 0:c.age)>0?(gb++,u0[n]):(gb=0,new Promise(async u=>{var p,h;let d={age:0,gender:"unknown",genderScore:0,descriptor:[]};if((p=t.face.description)==null?void 0:p.enabled){let f=Ab(e),m=xs==null?void 0:xs.execute(f);O8=ce(),ne(f);let A=await(await m.find(R=>R.shape[1]===1)).data(),x=Math.trunc(200*Math.abs(A[0]-.5))/100;x>(((h=t.face.description)==null?void 0:h.minConfidence)||0)&&(d.gender=A[0]<=.5?"female":"male",d.genderScore=Math.min(.99,x));let y=js(m.find(R=>R.shape[1]===100),1),b=(await y.data())[0];ne(y);let k=await m.find(R=>R.shape[1]===100).data();d.age=Math.round(k[b-1]>k[b+1]?10*b-100*k[b-1]:10*b+100*k[b+1])/10;let C=m.find(R=>R.shape[1]===1024),N=C?await C.data():[];d.descriptor=Array.from(N),m.forEach(R=>ne(R))}u0[n]=d,M8=s,u(d)}))}function c0(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function Dp(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function L8(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 $e.cropAndResize(t,a,[0],n)}function B8(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],s=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]],r=e.palmLandmarks.map(a=>[a[0]*t[0],a[1]*t[1]]);return{startPoint:n,endPoint:s,palmLandmarks:r,confidence:e.confidence}}function d0(e,t=1.5){let n=Dp(e),s=c0(e),r=[t*s[0]/2,t*s[1]/2],a=[n[0]-r[0],n[1]-r[1]],o=[n[0]+r[0],n[1]+r[1]];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function p0(e){let t=Dp(e),n=c0(e),r=Math.max(...n)/2,a=[t[0]-r,t[1]-r],o=[t[0]+r,t[1]+r];return{startPoint:a,endPoint:o,palmLandmarks:e.palmLandmarks}}function Xge(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function W8(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Xge(n)}var V8=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function Jo(e,t){let n=0;for(let s=0;s<e.length;s++)n+=e[s]*t[s];return n}function Kge(e,t){let n=[];for(let s=0;s<e.length;s++)n.push(e[s][t]);return n}function U8(e,t){let n=[],s=e.length;for(let r=0;r<s;r++){n.push([]);for(let a=0;a<s;a++)n[r].push(Jo(e[r],Kge(t,a)))}return n}function xb(e,t){let n=Math.cos(e),s=Math.sin(e),r=[[n,-s,0],[s,n,0],[0,0,1]],a=V8(t[0],t[1]),o=U8(a,r),i=V8(-t[0],-t[1]);return U8(o,i)}function G8(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],s=[-Jo(t[0],n),-Jo(t[1],n)];return[t[0].concat(s[0]),t[1].concat(s[1]),[0,0,1]]}function bb(e,t){return[Jo(e,t[0]),Jo(e,t[1])]}var 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s={};s.reshape=G(t,[-1,7,2]),s.div=he(s.reshape,this.inputSizeTensor),s.landmarks=le(s.div,this.anchors[n]);let r=L(s.landmarks,this.inputSizeTensor);return Object.keys(s).forEach(a=>ne(s[a])),r}async predict(t,n){let s={};s.resize=$e.resizeBilinear(t,[this.inputSize,this.inputSize]),s.div=he(s.resize,127.5),s.image=me(s.div,1),s.batched=this.model.execute(s.image),s.predictions=ot(s.batched),s.slice=De(s.predictions,[0,0],[-1,1]),s.sigmoid=ds(s.slice),s.scores=ot(s.sigmoid);let r=await s.scores.data();s.boxes=De(s.predictions,[0,1],[-1,4]),s.norm=this.normalizeBoxes(s.boxes),s.nms=await $e.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=De(s.norm,[i,0],[1,-1]),l.slice=De(s.predictions,[i,5],[1,14]),l.norm=this.normalizeLandmarks(l.slice,i),l.palmLandmarks=G(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=B8(h,[t.shape[2]/this.inputSize,t.shape[1]/this.inputSize]);o.push(f),Object.keys(l).forEach(m=>ne(l[m]))}return Object.keys(s).forEach(i=>ne(s[i])),o}};var Zge=5,j8=1.65,q8=[0,5,9,13,17,1,2],Yge=0,Jge=2,X8=0,wb=class{constructor(t,n){de(this,"handDetector");de(this,"handPoseModel");de(this,"inputSize");de(this,"storedBoxes");de(this,"skipped");de(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=>bb([...a,1],n)),r=this.calculateLandmarksBoundingBox(s);return d0(p0(r),Zge)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),s=d0(p0(n),j8);s.palmLandmarks=[];for(let r=0;r<q8.length;r++)s.palmLandmarks.push(t[q8[r]].slice(0,2));return s}transformRawCoords(t,n,s,r){let a=c0(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=xb(s,[0,0]),c=i.map(h=>[...bb(h,l),h[2]]),u=G8(r),d=[...Dp(n),1],p=[Jo(d,u[0]),Jo(d,u[1])];return c.map(h=>[Math.trunc(h[0]+p[0]),Math.trunc(h[1]+p[1]),Math.trunc(h[2])])}async estimateHands(t,n){let s=!1,r,a=(n.hand.skipTime||0)>ce()-X8,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?W8(c.palmLandmarks[Yge],c.palmLandmarks[Jge]):0,d=Dp(c),p=[d[0]/t.shape[2],d[1]/t.shape[1]],h=n.hand.rotation&&ge.kernels.includes("rotatewithoffset")?$e.rotateWithOffset(t,u,0,p):t.clone(),f=xb(-u,d),m=s?this.getBoxForPalmLandmarks(c.palmLandmarks,f):c,g=L8(m,h,[this.inputSize,this.inputSize]),A=he(g,255);ne(g),ne(h);let[x,y]=this.handPoseModel.execute(A);X8=ce(),ne(A);let b=(await x.data())[0];if(ne(x),b>=n.hand.minConfidence/4){let w=G(y,[-1,3]),k=await w.array();ne(y),ne(w);let C=this.transformRawCoords(k,m,u,f),N=this.getBoxForHandLandmarks(C);this.storedBoxes[l]={...N,confidence:b};let R={landmarks:C,confidence:b,boxConfidence:c.confidence,fingerConfidence:b,box:{topLeft:N.startPoint,bottomRight:N.endPoint}};i.push(R)}else this.storedBoxes[l]=null;ne(y)}else{let u=d0(p0(c),j8),d={confidence:c.confidence,boxConfidence:c.confidence,fingerConfidence:0,box:{topLeft:u.startPoint,bottomRight:u.endPoint},landmarks:[]};i.push(d)}}return this.storedBoxes=this.storedBoxes.filter(l=>l!==null),this.detectedHands=i.length,i.length>n.hand.maxDetected&&(i.length=n.hand.maxDetected),i}};var Jn={thumb:0,index:1,middle:2,ring:3,pinky:4,all:[0,1,2,3,4],nameMapping:{0:"thumb",1:"index",2:"middle",3:"ring",4:"pinky"},pointsMapping:{0:[[0,1],[1,2],[2,3],[3,4]],1:[[0,5],[5,6],[6,7],[7,8]],2:[[0,9],[9,10],[10,11],[11,12]],3:[[0,13],[13,14],[14,15],[15,16]],4:[[0,17],[17,18],[18,19],[19,20]]},getName:e=>Jn.nameMapping[e],getPoints:e=>Jn.pointsMapping[e]},Qo={none:0,half:1,full:2,nameMapping:{0:"none",1:"half",2:"full"},getName:e=>Qo.nameMapping[e]},zt={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=>zt.nameMapping[e]},zl=class{constructor(t){de(this,"name");de(this,"curls");de(this,"directions");de(this,"weights");de(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:xr,index:ua,middle:ca,ring:Ll,pinky:Bl}=Jn,{none:br,half:Qge,full:vr}=Qo,{verticalUp:Nc,verticalDown:AAe,horizontalLeft:kb,horizontalRight:e2e,diagonalUpRight:t2e,diagonalUpLeft:Ec,diagonalDownRight:yAe,diagonalDownLeft:xAe}=zt,ei=new zl("thumbs 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g=Math.sqrt(r*r+i*i),A=Math.sqrt(a*a+l*l),x=Math.sqrt(o*o+c*c),y=Math.max(g,A,x),b=e[0],w=e[1],k=n[0],C=n[1];y===g?(k=n[0],C=n[1]):y===x&&(b=t[0],w=t[1]);let F=Y8([b,w],[k,C]),_=J8(F,Wl.TOTAL_ANGLE_VOTE_POWER);p+=_[0],h+=_[1],f+=_[2];for(let T of s){let M=J8(T,Wl.SINGLE_ANGLE_VOTE_POWER);p+=M[0],h+=M[1],f+=M[2]}let P;return p===Math.max(p,h,f)?P=eT(l,i,c,d):f===Math.max(h,f)?P=Q8(a,r,o,u):P=r2e(l,i,c,d,a,r,o,u),P}function tT(e){let t=[],n=[],s=[],r=[];if(!e)return{curls:s,directions:r};for(let a of Jn.all){let o=Jn.getPoints(a),i=[],l=[];for(let c of o){let u=e[c[0]],d=e[c[1]],p=Y8(u,d),h=p[0],f=p[1];i.push(h),l.push(f)}t.push(i),n.push(l)}for(let a of Jn.all){let o=a===Jn.thumb?1:0,i=Jn.getPoints(a),l=e[i[o][0]],c=e[i[o+1][1]],u=e[i[3][1]],d=s2e(l,c,u),p=a2e(l,c,u,t[a].slice(o));s[a]=d,r[a]=p}return{curls:s,directions:r}}function h0(e){if(!e||e.length===0)return null;let t=tT(e),n={};for(let s of Jn.all)n[Jn.getName(s)]={curl:Qo.getName(t.curls[s]),direction:zt.getName(t.directions[s])};return n}function nT(e){let t=[];if(!e||e.length===0)return t;let n=tT(e);for(let s of K8){let r=s.matchAgainst(n.curls,n.directions);r>=n2e&&t.push({name:s.name,confidence:r})}return t}var sT={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]},da,pa,rT;async function Sb(e,t){let n=await rT.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(sT))a[u]=sT[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=h0(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 Ib(e){var n,s,r,a,o,i;ge.initial&&(da=null,pa=null),!da||!pa?([da,pa]=await Promise.all([e.hand.enabled?Xe(Ze(e.modelBasePath,((n=e.hand.detector)==null?void 0:n.modelPath)||""),{fromTFHub:(((s=e.hand.detector)==null?void 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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 f0(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 Cb(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 St=[null,null],o2e=["StatefulPartitionedCall/Postprocessor/Slice","StatefulPartitionedCall/Postprocessor/ExpandDims_1"],si=[[0,0],[0,0]],i2e=["hand","fist","pinch","point","face","tip","pinchtip"],oT=4,iT=1.6,l2e=512,u2e=1.4,m0=Number.MAX_SAFE_INTEGER,Tb=0,ha=[0,0],jt={boxes:[],hands:[]},lT={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]};async function uT(e){var t,n;if(ge.initial&&(St[0]=null),St[0])e.debug&&se("cached model:",St[0].modelUrl);else{g0(["tensorlistreserve","enter","tensorlistfromtensor","merge","loopcond","switch","exit","tensorliststack","nextiteration","tensorlistsetitem","tensorlistgetitem","reciprocal","shape","split","where"],e),St[0]=await Xe(Ze(e.modelBasePath,((t=e.hand.detector)==null?void 0:t.modelPath)||""));let s=Object.values(St[0].modelSignature.inputs);si[0][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,si[0][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!St[0]||!St[0].modelUrl?se("load model failed:",(n=e.hand.detector)==null?void 0:n.modelPath):e.debug&&se("load model:",St[0].modelUrl)}return St[0]}async function cT(e){var t,n;if(ge.initial&&(St[1]=null),St[1])e.debug&&se("cached model:",St[1].modelUrl);else{St[1]=await Xe(Ze(e.modelBasePath,((t=e.hand.skeleton)==null?void 0:t.modelPath)||""));let s=Object.values(St[1].modelSignature.inputs);si[1][0]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[1].size):0,si[1][1]=Array.isArray(s)?parseInt(s[0].tensorShape.dim[2].size):0,!St[1]||!St[1].modelUrl?se("load model failed:",(n=e.hand.skeleton)==null?void 0:n.modelPath):e.debug&&se("load model:",St[1].modelUrl)}return St[1]}async function c2e(e,t){let n=[];if(!e||!St[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,l2e),o=Math.round(a*r/8)*8;s.resize=$e.resizeBilinear(e,[a,o]),s.cast=pe(s.resize,"int32"),[s.rawScores,s.rawBoxes]=await St[0].executeAsync(s.cast,o2e),s.boxes=ot(s.rawBoxes,[0,2]),s.scores=ot(s.rawScores,[0]);let i=rs(s.scores,1);ne(i[oT]),i.splice(oT,1),s.filtered=yn(i,1),ne(i),s.max=An(s.filtered,1),s.argmax=js(s.filtered,1);let l=0;s.nms=await $e.nonMaxSuppressionAsync(s.boxes,s.max,t.hand.maxDetected,t.hand.iouThreshold,t.hand.minConfidence);let c=await s.nms.data(),u=await s.max.data(),d=await s.argmax.data();for(let p of Array.from(c)){let h=De(s.boxes,p,1),f=await h.data();ne(h);let m=[f[1],f[0],f[3]-f[1],f[2]-f[0]],g=f0(m,u2e),A=Cb(g),x=[Math.trunc(m[0]*ha[0]),Math.trunc(m[1]*ha[1]),Math.trunc(m[2]*ha[0]),Math.trunc(m[3]*ha[1])],y=u[p],b=i2e[d[p]],w={id:l++,score:y,box:x,boxRaw:g,boxCrop:A,label:b};n.push(w)}return Object.keys(s).forEach(p=>ne(s[p])),n.sort((p,h)=>h.score-p.score),n.length>(t.hand.maxDetected||1)&&(n.length=t.hand.maxDetected||1),n}async function Nb(e,t,n){let s={id:t.id,score:Math.round(100*t.score)/100,boxScore:Math.round(100*t.score)/100,fingerScore:0,box:t.box,boxRaw:t.boxRaw,label:t.label,keypoints:[],landmarks:{},annotations:{}};if(e&&St[1]&&n.hand.landmarks&&t.score>(n.hand.minConfidence||0)){let r={};r.crop=$e.cropAndResize(e,[t.boxCrop],[0],[si[1][0],si[1][1]],"bilinear"),r.cast=pe(r.crop,"float32"),r.div=he(r.cast,255),[r.score,r.keypoints]=St[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=G(r.keypoints,[-1,3]);let c=(await r.reshaped.array()).map(u=>[u[0]/si[1][1],u[1]/si[1][0],u[2]||0]).map(u=>[u[0]*t.boxRaw[2],u[1]*t.boxRaw[3],u[2]||0]);s.keypoints=c.map(u=>[ha[0]*(u[0]+t.boxRaw[0]),ha[1]*(u[1]+t.boxRaw[1]),u[2]||0]),s.landmarks=h0(s.keypoints);for(let u of Object.keys(lT))s.annotations[u]=lT[u].map(d=>s.landmarks&&s.keypoints[d]?s.keypoints[d]:null)}Object.keys(r).forEach(i=>ne(r[i]))}return s}async function Eb(e,t){var r,a;if(!St[0]||!St[1]||!((r=St[0])==null?void 0:r.inputs[0].shape)||!((a=St[1])==null?void 0:a.inputs[0].shape))return[];ha=[e.shape[2]||0,e.shape[1]||0],m0++;let n=(t.hand.skipTime||0)>ce()-Tb,s=m0<(t.hand.skipFrames||0);return t.skipAllowed&&n&&s?jt.hands:new Promise(async o=>{let i=3*(t.hand.skipTime||0)>ce()-Tb,l=m0<3*(t.hand.skipFrames||0);t.skipAllowed&&jt.hands.length===t.hand.maxDetected?jt.hands=await Promise.all(jt.boxes.map(u=>Nb(e,u,t))):t.skipAllowed&&i&&l&&jt.hands.length>0?jt.hands=await Promise.all(jt.boxes.map(u=>Nb(e,u,t))):(jt.boxes=await c2e(e,t),Tb=ce(),jt.hands=await Promise.all(jt.boxes.map(u=>Nb(e,u,t))),m0=0);let c=[...jt.boxes];if(jt.boxes.length=0,t.cacheSensitivity>0)for(let u=0;u<jt.hands.length;u++){let d=aT(jt.hands[u].keypoints,ha);if(d.box[2]/(e.shape[2]||1)>.05&&d.box[3]/(e.shape[1]||1)>.05&&jt.hands[u].fingerScore&&jt.hands[u].fingerScore>(t.hand.minConfidence||0)){let p=f0(d.box,iT),h=f0(d.boxRaw,iT),f=Cb(h);jt.boxes.push({...c[u],box:p,boxRaw:h,boxCrop:f})}}for(let u=0;u<jt.hands.length;u++){let d=Vl(jt.hands[u].keypoints,ha);jt.hands[u].box=d.box,jt.hands[u].boxRaw=d.boxRaw}o(jt.hands)})}var pn,A0=[],Rb=Number.MAX_SAFE_INTEGER,dT=0,pT=0;async function hT(e){var t,n;return ge.initial&&(pn=null),pn?e.debug&&se("cached model:",pn.modelUrl):(pn=await Xe(Ze(e.modelBasePath,((t=e.face.liveness)==null?void 0:t.modelPath)||"")),!pn||!pn.modelUrl?se("load model failed:",(n=e.face.liveness)==null?void 0:n.modelPath):e.debug&&se("load model:",pn.modelUrl)),pn}async function $b(e,t,n,s){var o,i;if(!pn)return null;let r=(((o=t.face.liveness)==null?void 0:o.skipTime)||0)>ce()-pT,a=Rb<(((i=t.face.liveness)==null?void 0:i.skipFrames)||0);return t.skipAllowed&&r&&a&&dT===s&&A0[n]?(Rb++,A0[n]):(Rb=0,new Promise(async l=>{let c=$e.resizeBilinear(e,[(pn==null?void 0:pn.inputs[0].shape)?pn.inputs[0].shape[2]:0,(pn==null?void 0:pn.inputs[0].shape)?pn.inputs[0].shape[1]:0],!1),u=pn==null?void 0:pn.execute(c),d=(await u.data())[0];A0[n]=Math.round(100*d)/100,dT=s,pT=ce(),ne([c,u]),l(A0[n])}))}var Fb={};jc(Fb,{connected:()=>x0,horizontal:()=>_b,kpt:()=>y0,relative:()=>Pb,vertical:()=>Db});var y0=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],_b=[["leftEye","rightEye"],["leftEar","rightEar"],["leftShoulder","rightShoulder"],["leftElbow","rightElbow"],["leftWrist","rightWrist"],["leftHip","rightHip"],["leftKnee","rightKnee"],["leftAnkle","rightAnkle"]],Db=[["leftKnee","leftShoulder"],["rightKnee","rightShoulder"],["leftAnkle","leftKnee"],["rightAnkle","rightKnee"]],Pb=[[["leftHip","rightHip"],["leftShoulder","rightShoulder"]],[["leftElbow","rightElbow"],["leftShoulder","rightShoulder"]]],x0={leftLeg:["leftHip","leftKnee","leftAnkle"],rightLeg:["rightHip","rightKnee","rightAnkle"],torso:["leftShoulder","rightShoulder","rightHip","leftHip","leftShoulder"],leftArm:["leftShoulder","leftElbow","leftWrist"],rightArm:["rightShoulder","rightElbow","rightWrist"],head:[]};var fT=.005,bs={keypoints:[],padding:[[0,0],[0,0],[0,0],[0,0]]};function Ob(e){for(let t of _b){let n=e.keypoints.findIndex(r=>r.part===t[0]),s=e.keypoints.findIndex(r=>r.part===t[1]);if(e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[0]<e.keypoints[s].position[0]){let r=e.keypoints[n];e.keypoints[n]=e.keypoints[s],e.keypoints[s]=r}}for(let t of Db){let n=e.keypoints.findIndex(r=>r&&r.part===t[0]),s=e.keypoints.findIndex(r=>r&&r.part===t[1]);e.keypoints[n]&&e.keypoints[s]&&e.keypoints[n].position[1]<e.keypoints[s].position[1]&&e.keypoints.splice(n,1)}for(let[t,n]of Pb){let s=e.keypoints.findIndex(c=>c&&c.part===t[0]),r=e.keypoints.findIndex(c=>c&&c.part===t[1]),a=e.keypoints.findIndex(c=>c&&c.part===n[0]),o=e.keypoints.findIndex(c=>c&&c.part===n[1]);if(!e.keypoints[a]||!e.keypoints[o])continue;let i=e.keypoints[s]?[Math.abs(e.keypoints[a].position[0]-e.keypoints[s].position[0]),Math.abs(e.keypoints[o].position[0]-e.keypoints[s].position[0])]:[0,0],l=e.keypoints[r]?[Math.abs(e.keypoints[o].position[0]-e.keypoints[r].position[0]),Math.abs(e.keypoints[a].position[0]-e.keypoints[r].position[0])]:[0,0];if(i[0]>i[1]||l[0]>l[1]){let c=e.keypoints[s];e.keypoints[s]=e.keypoints[r],e.keypoints[r]=c}}}function mT(e){for(let t=0;t<e.length;t++)if(e[t]&&bs.keypoints[t]){let n=[Math.abs(e[t].positionRaw[0]-bs.keypoints[t].positionRaw[0]),Math.abs(e[t].positionRaw[1]-bs.keypoints[t].positionRaw[1])];n[0]<fT&&n[1]<fT?e[t]=bs.keypoints[t]:bs.keypoints[t]=e[t]}else bs.keypoints[t]=e[t];return e}function gT(e,t){let n={};if(!e.shape||!e.shape[1]||!e.shape[2])return e;bs.padding=[[0,0],[e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0,e.shape[2]>e.shape[1]?Math.trunc((e.shape[2]-e.shape[1])/2):0],[e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0,e.shape[1]>e.shape[2]?Math.trunc((e.shape[1]-e.shape[2])/2):0],[0,0]],n.pad=Xs(e,bs.padding),n.resize=$e.resizeBilinear(n.pad,[t,t]);let s=pe(n.resize,"int32");return Object.keys(n).forEach(r=>ne(n[r])),s}function AT(e,t){e.keypoints=e.keypoints.filter(s=>s&&s.position);for(let s of e.keypoints)s.position=[s.position[0]*(t[0]+bs.padding[2][0]+bs.padding[2][1])/t[0]-bs.padding[2][0],s.position[1]*(t[1]+bs.padding[1][0]+bs.padding[1][1])/t[1]-bs.padding[1][0]],s.positionRaw=[s.position[0]/t[0],s.position[1]/t[1]];let n=Vl(e.keypoints.map(s=>s.position),t);return e.box=n.box,e.boxRaw=n.boxRaw,e}var Pn,b0=0,Mb=Number.MAX_SAFE_INTEGER,Ul={boxes:[],bodies:[],last:0};async function yT(e){return ge.initial&&(Pn=null),Pn?e.debug&&se("cached model:",Pn.modelUrl):(g0(["size"],e),Pn=await Xe(Ze(e.modelBasePath,e.body.modelPath||"")),!Pn||!Pn.modelUrl?se("load model failed:",e.body.modelPath):e.debug&&se("load model:",Pn.modelUrl)),b0=Pn.inputs[0].shape?Pn.inputs[0].shape[2]:0,b0===-1&&(b0=256),Pn}async function d2e(e,t,n,s){let r=e[0][0],a=[],o=0;for(let d=0;d<r.length;d++)if(o=r[d][2],o>t.body.minConfidence){let p=[(s[3]-s[1])*r[d][1]+s[1],(s[2]-s[0])*r[d][0]+s[0]];a.push({score:Math.round(100*o)/100,part:y0[d],positionRaw:p,position:[Math.round((n.shape[2]||0)*p[0]),Math.round((n.shape[1]||0)*p[1])]})}o=a.reduce((d,p)=>p.score>d?p.score:d,0);let i=[],l=Vl(a.map(d=>d.position),[n.shape[2],n.shape[1]]),c={};for(let[d,p]of Object.entries(x0)){let h=[];for(let f=0;f<p.length-1;f++){let m=a.find(A=>A.part===p[f]),g=a.find(A=>A.part===p[f+1]);m&&g&&m.score>(t.body.minConfidence||0)&&g.score>(t.body.minConfidence||0)&&h.push([m.position,g.position])}c[d]=h}let u={id:0,score:o,box:l.box,boxRaw:l.boxRaw,keypoints:a,annotations:c};return Ob(u),i.push(u),i}async function p2e(e,t,n,s){let r=[];for(let a=0;a<e[0].length;a++){let o=e[0][a],i=Math.round(100*o[51+4])/100;if(i>t.body.minConfidence){let l=[];for(let p=0;p<17;p++){let h=o[3*p+2];if(h>t.body.minConfidence){let f=[(s[3]-s[1])*o[3*p+1]+s[1],(s[2]-s[0])*o[3*p+0]+s[0]];l.push({part:y0[p],score:Math.round(100*h)/100,positionRaw:f,position:[Math.round((n.shape[2]||0)*f[0]),Math.round((n.shape[1]||0)*f[1])]})}}let c=Vl(l.map(p=>p.position),[n.shape[2],n.shape[1]]),u={};for(let[p,h]of Object.entries(x0)){let f=[];for(let m=0;m<h.length-1;m++){let g=l.find(x=>x.part===h[m]),A=l.find(x=>x.part===h[m+1]);g&&A&&g.score>(t.body.minConfidence||0)&&A.score>(t.body.minConfidence||0)&&f.push([g.position,A.position])}u[p]=f}let d={id:a,score:i,box:c.box,boxRaw:c.boxRaw,keypoints:[...l],annotations:u};Ob(d),r.push(d)}}return r.sort((a,o)=>o.score-a.score),r.length>t.body.maxDetected&&(r.length=t.body.maxDetected),r}async function zb(e,t){if(!Pn||!(Pn==null?void 0:Pn.inputs[0].shape))return[];t.skipAllowed||(Ul.boxes.length=0),Mb++;let n=(t.body.skipTime||0)>ce()-Ul.last,s=Mb<(t.body.skipFrames||0);return t.skipAllowed&&n&&s?Ul.bodies:new Promise(async r=>{let a={};Mb=0,a.input=gT(e,b0),a.res=Pn==null?void 0:Pn.execute(a.input),Ul.last=ce();let o=await a.res.array();Ul.bodies=a.res.shape[2]===17?await d2e(o,t,e,[0,0,1,1]):await p2e(o,t,e,[0,0,1,1]);for(let i of Ul.bodies)AT(i,[e.shape[2]||1,e.shape[1]||1]),mT(i.keypoints);Object.keys(a).forEach(i=>ne(a[i])),r(Ul.bodies)})}var vs,v0=[],xT=0,Lb=Number.MAX_SAFE_INTEGER,w0=2.5;async function bT(e){if(!vs||ge.initial){vs=await Xe(Ze(e.modelBasePath,e.object.modelPath||""));let t=Object.values(vs.modelSignature.inputs);if(vs.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!vs.inputSize)throw new Error(`cannot determine model inputSize: ${e.object.modelPath}`);!vs||!vs.modelUrl?se("load model failed:",e.object.modelPath):e.debug&&se("load model:",vs.modelUrl)}else e.debug&&se("cached model:",vs.modelUrl);return vs}async function h2e(e,t,n,s){let r=0,a=[];for(let c of[1,2,4])q(async()=>{var g,A;let u=c*13,d=(g=e.find(x=>x.shape[1]===u**2&&x.shape[2]===Sc.length))==null?void 0:g.squeeze(),p=(A=e.find(x=>x.shape[1]===u**2&&x.shape[2]<Sc.length))==null?void 0:A.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 y=0;y<d.shape[1];y++){let b=m[x][y];if(b>s.object.minConfidence&&y!==61){let w=(.5+Math.trunc(x%u))/u,k=(.5+Math.trunc(x/u))/u,C=f[x].map(U=>U*(u/c/t)),[N,R]=[w-w0/c*C[0],k-w0/c*C[1]],[F,_]=[w+w0/c*C[2]-N,k+w0/c*C[3]-R],P=[N,R,F,_];P=P.map(U=>Math.max(0,Math.min(U,1)));let T=[P[0]*n[0],P[1]*n[1],P[2]*n[0],P[3]*n[1]],M={id:r++,score:Math.round(100*b)/100,class:y+1,label:Sc[y].label,box:T.map(U=>Math.trunc(U)),boxRaw:P};a.push(M)}}});e.forEach(c=>ne(c));let o=a.map(c=>[c.boxRaw[1],c.boxRaw[0],c.boxRaw[3],c.boxRaw[2]]),i=a.map(c=>c.score),l=[];if(o&&o.length>0){let c=await $e.nonMaxSuppressionAsync(o,i,s.object.maxDetected,s.object.iouThreshold,s.object.minConfidence);l=await c.data(),ne(c)}return a=a.filter((c,u)=>l.includes(u)).sort((c,u)=>u.score-c.score),a}async function Bb(e,t){let n=(t.object.skipTime||0)>ce()-xT,s=Lb<(t.object.skipFrames||0);return t.skipAllowed&&n&&s&&v0.length>0?(Lb++,v0):(Lb=0,!ge.kernels.includes("mod")||!ge.kernels.includes("sparsetodense")?v0:new Promise(async r=>{let a=[e.shape[2],e.shape[1]],o=$e.resizeBilinear(e,[vs.inputSize,vs.inputSize],!1),i=he(o,255),l=i.transpose([0,3,1,2]);ne(i),ne(o);let c;t.object.enabled&&(c=vs.execute(l)),xT=ce(),ne(l);let u=await h2e(c,vs.inputSize,a,t);v0=u,r(u)}))}var Pp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],f2e=Pp.length,Fp=Pp.reduce((e,t,n)=>(e[t]=n,e),{}),m2e=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],oye=m2e.map(([e,t])=>[Fp[e],Fp[t]]),vT=[["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 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e.config.deallocate!="undefined"&&e.config.deallocate&&(se("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),Ir.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0)),Nr().getGPGPUContext)){let s=await Nr().getGPGPUContext().gl;e.config.debug&&se(`gl version:${s.getParameter(s.VERSION)} renderer:${s.getParameter(s.RENDERER)}`)}Cs()==="webgpu",F3(),await nf(),e.performance.initBackend=Math.trunc(ce()-n),e.config.backend=Cs(),await ge.updateBackend(),S2e()}return!0}function g0(e,t){for(let n of e){let s={kernelName:n,backendName:t.backend,kernelFunc:()=>{t.debug&&se("kernelFunc",n,t.backend)}};ar(s)}ge.kernels=Xr(Cs()).map(n=>n.kernelName.toLowerCase())}var fa={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 16px "Segoe UI"',lineHeight:18,lineWidth:4,pointSize:2,roundRect:8,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawGestures:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1},Yb=0,Gl=e=>{if(e&&e.getContext)return e.getContext("2d");throw new Error("invalid canvas")},_c=e=>Math.round(e*180/Math.PI);function Jb(e,t,n,s,r){s=s||0,e.fillStyle=r.useDepth&&s?`rgba(${127.5+2*s}, ${127.5-2*s}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function Op(e,t,n,s,r,a){if(e.beginPath(),a.useCurves){let o=(t+t+s)/2,i=(n+n+r)/2;e.ellipse(o,i,s/2,r/2,0,0,2*Math.PI)}else e.lineWidth=a.lineWidth,e.moveTo(t+a.roundRect,n),e.lineTo(t+s-a.roundRect,n),e.quadraticCurveTo(t+s,n,t+s,n+a.roundRect),e.lineTo(t+s,n+r-a.roundRect),e.quadraticCurveTo(t+s,n+r,t+s-a.roundRect,n+r),e.lineTo(t+a.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-a.roundRect),e.lineTo(t,n+a.roundRect),e.quadraticCurveTo(t,n,t+a.roundRect,n),e.closePath();e.stroke()}function _T(e,t,n){if(!(t.length<2)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let s of t){let r=s[2]||0;e.strokeStyle=n.useDepth&&r!==0?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r!==0?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(s[0],Math.round(s[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function I2e(e,t,n){if(!(t.length<2)){if(!n.useCurves||t.length<=2){_T(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let s=0;s<t.length-2;s++){let r=(t[s][0]+t[s+1][0])/2,a=(t[s][1]+t[s+1][1])/2;e.quadraticCurveTo(t[s][0],t[s][1],r,a)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function DT(e,t,n,s=5){let r,a,o;e.beginPath(),e.moveTo(t[0],t[1]),e.lineTo(n[0],n[1]),r=Math.atan2(n[1]-t[1],n[0]-t[0]),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.moveTo(a,o),r+=1/3*(2*Math.PI),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.lineTo(a,o),r+=1/3*(2*Math.PI),a=s*Math.cos(r)+n[0],o=s*Math.sin(r)+n[1],e.lineTo(a,o),e.closePath(),e.stroke(),e.fill()}async function Qb(e,t,n){let s=$n(fa,n);if(!(!t||!e)&&s.drawGestures){let r=Gl(e);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}: ${l[1]}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(u,8,2+a*s.lineHeight)),r.fillStyle=s.labelColor,r.fillText(u,6,0+a*s.lineHeight),a+=1}}}}async function e5(e,t,n){var a,o,i,l,c;let s=$n(fa,n);if(!t||!e)return;let r=Gl(e);for(let u of t){if(r.font=s.font,r.strokeStyle=s.color,r.fillStyle=s.color,s.drawBoxes&&Op(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: ${_c(u.rotation.angle.roll)}\xB0 yaw:${_c(u.rotation.angle.yaw)}\xB0 pitch:${_c(u.rotation.angle.pitch)}\xB0`),u.rotation.gaze.bearing&&d.push(`gaze: ${_c(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)Jb(r,d[0],d[1],d[2],s);if(s.drawPolygons){if(r.lineWidth=1,u.mesh.length>450)for(let d=0;d<Fl.length/3;d++){let p=[Fl[d*3+0],Fl[d*3+1],Fl[d*3+2]].map(h=>u.mesh[h]);_T(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]*_c(u.rotation.angle.yaw)/90,p=u.box[1]+u.box[3]/2+u.box[2]*_c(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]} ${u.box[1]+u.box[3]/2}
|
|
`);r.stroke(f),r.stroke(h)}if(s.drawGaze&&((i=(o=u.rotation)==null?void 0:o.gaze)==null?void 0:i.strength)&&((c=(l=u.rotation)==null?void 0:l.gaze)==null?void 0:c.bearing)&&u.annotations.leftEyeIris&&u.annotations.rightEyeIris&&u.annotations.leftEyeIris[0]&&u.annotations.rightEyeIris[0]){r.strokeStyle="pink",r.fillStyle="pink";let d=[u.annotations.leftEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.leftEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];DT(r,[u.annotations.leftEyeIris[0][0],u.annotations.leftEyeIris[0][1]],[d[0],d[1]],4);let p=[u.annotations.rightEyeIris[0][0]+Math.sin(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[3],u.annotations.rightEyeIris[0][1]+Math.cos(u.rotation.gaze.bearing)*u.rotation.gaze.strength*u.box[2]];DT(r,[u.annotations.rightEyeIris[0][0],u.annotations.rightEyeIris[0][1]],[p[0],p[1]],4)}}}}}async function t5(e,t,n){var a;let s=$n(fa,n);if(!t||!e)return;let r=Gl(e);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&&(Op(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 ${100*t[o].score}%`,t[o].box[0]+2,0+t[o].box[1]+s.lineHeight,t[o].box[2]))),s.drawPoints&&t[o].keypoints)for(let i=0;i<t[o].keypoints.length;i++)r.fillStyle=s.useDepth&&t[o].keypoints[i].position[2]?`rgba(${127.5+2*(t[o].keypoints[i].position[2]||0)}, ${127.5-2*(t[o].keypoints[i].position[2]||0)}, 255, 0.5)`:s.color,Jb(r,t[o].keypoints[i].position[0],t[o].keypoints[i].position[1],0,s);if(s.drawLabels&&t[o].keypoints){r.font=s.font;for(let i of t[o].keypoints)r.fillStyle=s.useDepth&&i.position[2]?`rgba(${127.5+2*i.position[2]}, ${127.5-2*i.position[2]}, 255, 0.5)`:s.color,r.fillText(`${i.part} ${Math.trunc(100*i.score)}%`,i.position[0]+4,i.position[1]+4)}if(s.drawPolygons&&t[o].keypoints&&t[o].annotations)for(let i of Object.values(t[o].annotations))for(let l of i)I2e(r,l,s)}}async function n5(e,t,n){let s=$n(fa,n);if(!t||!e)return;let r=Gl(e);r.lineJoin="round",r.font=s.font;for(let a of t){if(s.drawBoxes&&(r.strokeStyle=s.color,r.fillStyle=s.color,Op(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)}, ${127.5-2*(o[2]||0)}, 255, 0.5)`:s.color,Jb(r,o[0],o[1],0,s);if(s.drawLabels&&a.annotations){let o=(i,l)=>{!i||i.length===0||!i[0]||(r.fillStyle=s.useDepth?`rgba(${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]}, ${127.5-l*i[l][2]}, 255, 0.5)`:s.color,r.moveTo(i[l>0?l-1:0][0],i[l>0?l-1:0][1]),r.lineTo(i[l][0],i[l][1]),r.stroke()};r.lineWidth=s.lineWidth,o(a.annotations.index),o(a.annotations.middle),o(a.annotations.ring),o(a.annotations.pinky),o(a.annotations.thumb)}}}async function s5(e,t,n){let s=$n(fa,n);if(!t||!e)return;let r=Gl(e);r.lineJoin="round",r.font=s.font;for(let a of t)if(s.drawBoxes){if(r.strokeStyle=s.color,r.fillStyle=s.color,Op(r,a.box[0],a.box[1],a.box[2],a.box[3],s),s.drawLabels){let o=`${a.label} ${Math.round(100*a.score)}%`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,a.box[0]+3,1+a.box[1]+s.lineHeight,a.box[2])),r.fillStyle=s.labelColor,r.fillText(o,a.box[0]+2,0+a.box[1]+s.lineHeight,a.box[2])}r.stroke()}}async function PT(e,t,n){let s=$n(fa,n);if(!t||!e)return;let r=Gl(e);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,Op(r,t[a].box[0],t[a].box[1],t[a].box[2],t[a].box[3],s),s.drawLabels){let o=`person #${a}`;s.shadowColor&&s.shadowColor!==""&&(r.fillStyle=s.shadowColor,r.fillText(o,t[a].box[0]+3,1+t[a].box[1]+s.lineHeight,t[a].box[2])),r.fillStyle=s.labelColor,r.fillText(o,t[a].box[0]+2,0+t[a].box[1]+s.lineHeight,t[a].box[2])}r.stroke()}}async function FT(e,t){if(!e||!t)return;Gl(t).drawImage(e,0,0)}async function OT(e,t,n){if(!t||!t.performance||!t||!e)return null;let s=ce(),r=$n(fa,n),a=Promise.all([e5(e,t.face,r),t5(e,t.body,r),n5(e,t.hand,r),s5(e,t.object,r),Qb(e,t.gesture,r)]);return Yb=ge.perfadd?Yb+Math.round(ce()-s):Math.round(ce()-s),t.performance.draw=Yb,a}var C2e=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}},MT=(e,t)=>{let n=g=>{let A=Math.sqrt(g[0]*g[0]+g[1]*g[1]+g[2]*g[2]);return g[0]/=A,g[1]/=A,g[2]/=A,g},s=(g,A)=>{let x=g[0]-A[0],y=g[1]-A[1],b=g[2]-A[2];return[x,y,b]},r=(g,A)=>{let x=g[1]*A[2]-g[2]*A[1],y=g[2]*A[0]-g[0]*A[2],b=g[0]*A[1]-g[1]*A[0];return[x,y,b]},a=g=>{let[A,x,y,b,w,k,C,N,R]=g,F,_,P;return b<1?b>-1?(P=Math.asin(b),_=Math.atan2(-C,A),F=Math.atan2(-k,w)):(P=-Math.PI/2,_=-Math.atan2(N,R),F=0):(P=Math.PI/2,_=Math.atan2(N,R),F=0),isNaN(F)&&(F=0),isNaN(_)&&(_=0),isNaN(P)&&(P=0),{pitch:2*-F,yaw:2*-_,roll:2*-P}},o=g=>{let A=(y,b,w,k)=>Math.atan2(k-b,w-y);return{pitch:A(g[10][1],g[10][2],g[152][1],g[152][2]),yaw:A(g[33][0],g[33][2],g[263][0],g[263][2]),roll:A(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?C2e(e):{bearing:0,strength:0};return{angle:f,matrix:h,gaze:m}};var r5=async(e,t)=>{var h,f,m,g;let n,s,r,a,o,i,l,c,u,d=[];e.state="run:face",n=ce();let p=await _8(t,e.config);if(e.performance.face=ge.perfadd?(e.performance.face||0)+Math.trunc(ce()-n):Math.trunc(ce()-n),!t.shape||t.shape.length!==4)return[];if(!p)return[];for(let A=0;A<p.length;A++){if(e.analyze("Get Face"),!p[A].tensor||p[A].tensor.isDisposedInternal){se("Face object is disposed:",p[A].tensor);continue}let x=MT(p[A],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?o=e.config.face.emotion.enabled?pb(p[A].tensor||Pt([]),e.config,A,p.length):null:(e.state="run:emotion",n=ce(),o=e.config.face.emotion.enabled?await pb(p[A].tensor||Pt([]),e.config,A,p.length):null,e.performance.emotion=ge.perfadd?(e.performance.emotion||0)+Math.trunc(ce()-n):Math.trunc(ce()-n)),e.analyze("End Emotion:"),e.analyze("Start AntiSpoof:"),e.config.async?l=e.config.face.antispoof.enabled?Bx(p[A].tensor||Pt([]),e.config,A,p.length):null:(e.state="run:antispoof",n=ce(),l=e.config.face.antispoof.enabled?await Bx(p[A].tensor||Pt([]),e.config,A,p.length):null,e.performance.antispoof=ge.perfadd?(e.performance.antispoof||0)+Math.trunc(ce()-n):Math.trunc(ce()-n)),e.analyze("End AntiSpoof:"),e.analyze("Start Liveness:"),e.config.async?c=e.config.face.liveness.enabled?$b(p[A].tensor||Pt([]),e.config,A,p.length):null:(e.state="run:liveness",n=ce(),c=e.config.face.liveness.enabled?await $b(p[A].tensor||Pt([]),e.config,A,p.length):null,e.performance.antispoof=ge.perfadd?(e.performance.antispoof||0)+Math.trunc(ce()-n):Math.trunc(ce()-n)),e.analyze("End Liveness:"),e.analyze("Start Description:"),e.config.async?u=e.config.face.description.enabled?yb(p[A].tensor||Pt([]),e.config,A,p.length):null:(e.state="run:description",n=ce(),u=e.config.face.description.enabled?await yb(p[A].tensor||Pt([]),e.config,A,p.length):null,e.performance.description=ge.perfadd?(e.performance.description||0)+Math.trunc(ce()-n):Math.trunc(ce()-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:"),!e.config.face.iris.enabled&&((f=(h=p[A])==null?void 0:h.annotations)==null?void 0:f.leftEyeIris)&&((g=(m=p[A])==null?void 0:m.annotations)==null?void 0:g.rightEyeIris)&&(delete p[A].annotations.leftEyeIris,delete p[A].annotations.rightEyeIris);let y=p[A].annotations&&p[A].annotations.leftEyeIris&&p[A].annotations.leftEyeIris[0]&&p[A].annotations.rightEyeIris&&p[A].annotations.rightEyeIris[0]&&p[A].annotations.leftEyeIris.length>0&&p[A].annotations.rightEyeIris.length>0&&p[A].annotations.leftEyeIris[0]!==null&&p[A].annotations.rightEyeIris[0]!==null?Math.max(Math.abs(p[A].annotations.leftEyeIris[3][0]-p[A].annotations.leftEyeIris[1][0]),Math.abs(p[A].annotations.rightEyeIris[4][1]-p[A].annotations.rightEyeIris[2][1]))/t.shape[2]:0,b=e.config.face.detector.return?ot(p[A].tensor):null;ne(p[A].tensor),p[A].tensor&&delete p[A].tensor,d.push({...p[A],id:A,age:u==null?void 0:u.age,gender:u==null?void 0:u.gender,genderScore:u==null?void 0:u.genderScore,embedding:u==null?void 0:u.descriptor,emotion:o,real:l,live:c,iris:y!==0?Math.trunc(500/y/11.7)/100:0,rotation:x,tensor:b}),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 zT=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},LT=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]-e[n].mesh[263][2];Math.abs(s)<10?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 o=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]));o>10&&t.push({face:n,gesture:`mouth ${Math.trunc(o)}% open`});let i=e[n].mesh[152][2];Math.abs(i)>10&&t.push({face:n,gesture:`head ${i<0?"up":"down"}`})}return t},BT=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){if(!e[n].annotations||!e[n].annotations.leftEyeIris||!e[n].annotations.leftEyeIris[0]||!e[n].annotations.rightEyeIris||!e[n].annotations.rightEyeIris[0])continue;let s=e[n].annotations.leftEyeIris[3][0]-e[n].annotations.leftEyeIris[1][0],r=e[n].annotations.leftEyeIris[4][1]-e[n].annotations.leftEyeIris[2][1],a=Math.abs(s*r),o=e[n].annotations.rightEyeIris[3][0]-e[n].annotations.rightEyeIris[1][0],i=e[n].annotations.rightEyeIris[4][1]-e[n].annotations.rightEyeIris[2][1],l=Math.abs(o*i),c=!1;Math.abs(a-l)/Math.max(a,l)<.25&&(c=!0,t.push({iris:n,gesture:"facing center"}));let d=Math.abs(e[n].mesh[263][0]-e[n].annotations.leftEyeIris[0][0])/e[n].box[2],p=Math.abs(e[n].mesh[33][0]-e[n].annotations.rightEyeIris[0][0])/e[n].box[2];(d>.06||p>.06)&&(c=!1),d>p?d>.05&&t.push({iris:n,gesture:"looking right"}):p>.05&&t.push({iris:n,gesture:"looking left"});let h=Math.abs(e[n].mesh[145][1]-e[n].annotations.rightEyeIris[0][1])/e[n].box[3],f=Math.abs(e[n].mesh[374][1]-e[n].annotations.leftEyeIris[0][1])/e[n].box[3];(f<.01||h<.01||f>.022||h>.022)&&(c=!1),(f<.01||h<.01)&&t.push({iris:n,gesture:"looking down"}),(f>.022||h>.022)&&t.push({iris:n,gesture:"looking up"}),c&&t.push({iris:n,gesture:"looking center"})}return t},WT=e=>{if(!e)return[];let t=[];for(let n=0;n<e.length;n++){let s=[];if(e[n].annotations)for(let[r,a]of Object.entries(e[n].annotations))r!=="palmBase"&&Array.isArray(a)&&a[0]&&s.push({name:r.toLowerCase(),position:a[0]});if(s&&s.length>0){let r=s.reduce((o,i)=>o.position[2]<i.position[2]?o:i);t.push({hand:n,gesture:`${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=nT(e[n].keypoints);for(let a of r)t.push({hand:n,gesture:a.name})}}return t};var Pe={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0},a5=0;function VT(e,t){var o,i,l,c,u,d,p,h,f,m,g,A,x,y,b,w,k,C,N,R,F,_,P,T,M,U,j;let n=ce();if(!e)return{face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};let s=Date.now()-e.timestamp,r=s<1e3?8-Math.log(s+1):1;if(Pe.canvas=e.canvas,!Pe.body||e.body.length!==Pe.body.length)Pe.body=JSON.parse(JSON.stringify(e.body));else for(let z=0;z<e.body.length;z++){let X=e.body[z].box.map((Q,te)=>((r-1)*Pe.body[z].box[te]+Q)/r),Z=e.body[z].boxRaw.map((Q,te)=>((r-1)*Pe.body[z].boxRaw[te]+Q)/r),J=e.body[z].keypoints.map((Q,te)=>({score:Q.score,part:Q.part,position:[Pe.body[z].keypoints[te]?((r-1)*Pe.body[z].keypoints[te].position[0]+Q.position[0])/r:Q.position[0],Pe.body[z].keypoints[te]?((r-1)*Pe.body[z].keypoints[te].position[1]+Q.position[1])/r:Q.position[1]],positionRaw:[Pe.body[z].keypoints[te]?((r-1)*Pe.body[z].keypoints[te].positionRaw[0]+Q.positionRaw[0])/r:Q.position[0],Pe.body[z].keypoints[te]?((r-1)*Pe.body[z].keypoints[te].positionRaw[1]+Q.positionRaw[1])/r:Q.position[1]]})),ee={},re={connected:{}};((i=(o=t.body)==null?void 0:o.modelPath)==null?void 0:i.includes("efficientpose"))?re=ob:((c=(l=t.body)==null?void 0:l.modelPath)==null?void 0:c.includes("blazepose"))?re=Yx:((d=(u=t.body)==null?void 0:u.modelPath)==null?void 0:d.includes("movenet"))&&(re=Fb);for(let[Q,te]of Object.entries(re.connected)){let oe=[];for(let fe=0;fe<te.length-1;fe++){let be=J.find(Ce=>Ce.part===te[fe]),we=J.find(Ce=>Ce.part===te[fe+1]);be&&we&&be.score>(t.body.minConfidence||0)&&we.score>(t.body.minConfidence||0)&&oe.push([be.position,we.position])}ee[Q]=oe}Pe.body[z]={...e.body[z],box:X,boxRaw:Z,keypoints:J,annotations:ee}}if(!Pe.hand||e.hand.length!==Pe.hand.length)Pe.hand=JSON.parse(JSON.stringify(e.hand));else for(let z=0;z<e.hand.length;z++){let X=e.hand[z].box.map((re,Q)=>((r-1)*Pe.hand[z].box[Q]+re)/r),Z=e.hand[z].boxRaw.map((re,Q)=>((r-1)*Pe.hand[z].boxRaw[Q]+re)/r);Pe.hand[z].keypoints.length!==e.hand[z].keypoints.length&&(Pe.hand[z].keypoints=e.hand[z].keypoints);let J=e.hand[z].keypoints&&e.hand[z].keypoints.length>0?e.hand[z].keypoints.map((re,Q)=>re.map((te,oe)=>((r-1)*(Pe.hand[z].keypoints[Q][oe]||1)+(te||0))/r)):[],ee={};if(Object.keys(Pe.hand[z].annotations).length!==Object.keys(e.hand[z].annotations).length)Pe.hand[z].annotations=e.hand[z].annotations,ee=Pe.hand[z].annotations;else if(e.hand[z].annotations)for(let re of Object.keys(e.hand[z].annotations))ee[re]=e.hand[z].annotations[re]&&e.hand[z].annotations[re][0]?e.hand[z].annotations[re].map((Q,te)=>Q.map((oe,fe)=>((r-1)*Pe.hand[z].annotations[re][te][fe]+oe)/r)):null;Pe.hand[z]={...e.hand[z],box:X,boxRaw:Z,keypoints:J,annotations:ee}}if(!Pe.face||e.face.length!==Pe.face.length)Pe.face=JSON.parse(JSON.stringify(e.face));else for(let z=0;z<e.face.length;z++){let X=e.face[z].box.map((ee,re)=>((r-1)*Pe.face[z].box[re]+ee)/r),Z=e.face[z].boxRaw.map((ee,re)=>((r-1)*Pe.face[z].boxRaw[re]+ee)/r),J={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};J.matrix=(p=e.face[z].rotation)==null?void 0:p.matrix,J.angle={roll:((r-1)*(((f=(h=Pe.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=(A=Pe.face[z].rotation)==null?void 0:A.angle)==null?void 0:x.yaw)||0)+(((b=(y=e.face[z].rotation)==null?void 0:y.angle)==null?void 0:b.yaw)||0))/r,pitch:((r-1)*(((k=(w=Pe.face[z].rotation)==null?void 0:w.angle)==null?void 0:k.pitch)||0)+(((N=(C=e.face[z].rotation)==null?void 0:C.angle)==null?void 0:N.pitch)||0))/r},J.gaze={bearing:((r-1)*(((F=(R=Pe.face[z].rotation)==null?void 0:R.gaze)==null?void 0:F.bearing)||0)+(((P=(_=e.face[z].rotation)==null?void 0:_.gaze)==null?void 0:P.bearing)||0))/r,strength:((r-1)*(((M=(T=Pe.face[z].rotation)==null?void 0:T.gaze)==null?void 0:M.strength)||0)+(((j=(U=e.face[z].rotation)==null?void 0:U.gaze)==null?void 0:j.strength)||0))/r},Pe.face[z]={...e.face[z],rotation:J,box:X,boxRaw:Z}}if(!Pe.object||e.object.length!==Pe.object.length)Pe.object=JSON.parse(JSON.stringify(e.object));else for(let z=0;z<e.object.length;z++){let X=e.object[z].box.map((J,ee)=>((r-1)*Pe.object[z].box[ee]+J)/r),Z=e.object[z].boxRaw.map((J,ee)=>((r-1)*Pe.object[z].boxRaw[ee]+J)/r);Pe.object[z]={...e.object[z],box:X,boxRaw:Z}}if(e.persons){let z=e.persons;if(!Pe.persons||z.length!==Pe.persons.length)Pe.persons=JSON.parse(JSON.stringify(z));else for(let X=0;X<z.length;X++)Pe.persons[X].box=z[X].box.map((Z,J)=>((r-1)*Pe.persons[X].box[J]+Z)/r)}e.gesture&&(Pe.gesture=e.gesture);let a=ce();return a5=ge.perfadd?a5+Math.round(a-n):Math.round(a-n),e.performance&&(Pe.performance={...e.performance,interpolate:a5}),Pe}function I0(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 UT=(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 GT(e,t,n={order:2,multiplier:25,min:.2,max:.8}){let s=I0(e,t,n);return UT(s,n.order||2,n.min||0,n.max||1)}function HT(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=I0(e,t[o],n);if(i<s&&(s=i,r=o),s<(n.threshold||0))break}let a=UT(s,n.order||2,n.min||0,n.max||1);return{index:r,distance:s,similarity:a}}function jT(e,t,n,s,r){var i,l,c,u,d,p,h,f,m,g,A,x,y,b,w,k;let a=0,o=[];for(let C of e){let N={id:a++,face:C,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let M of t)C.box[0]>M.box[0]&&C.box[0]<M.box[0]+M.box[2]&&C.box[1]+C.box[3]>M.box[1]&&C.box[1]+C.box[3]<M.box[1]+M.box[3]&&(N.body=M);if(N.body)for(let M of n)M.box[0]+M.box[2]>N.body.box[0]&&M.box[0]+M.box[2]<N.body.box[0]+N.body.box[2]&&M.box[1]+M.box[3]>N.body.box[1]&&M.box[1]+M.box[3]<N.body.box[1]+N.body.box[3]&&N.hands&&(N.hands.left=M),M.box[0]<N.body.box[0]+N.body.box[2]&&M.box[0]>N.body.box[0]&&M.box[1]+M.box[3]>N.body.box[1]&&M.box[1]+M.box[3]<N.body.box[1]+N.body.box[3]&&N.hands&&(N.hands.right=M);for(let M of s)M.face!==void 0&&M.face===C.id?(i=N.gestures)==null||i.push(M):M.iris!==void 0&&M.iris===C.id?(l=N.gestures)==null||l.push(M):M.body!==void 0&&M.body===((c=N.body)==null?void 0:c.id)?(u=N.gestures)==null||u.push(M):M.hand!==void 0&&M.hand===((p=(d=N.hands)==null?void 0:d.left)==null?void 0:p.id)?(h=N.gestures)==null||h.push(M):M.hand!==void 0&&M.hand===((m=(f=N.hands)==null?void 0:f.right)==null?void 0:m.id)&&((g=N.gestures)==null||g.push(M));let R=[],F=[],_=M=>{M&&M.length===4&&(R.push(M[0],M[0]+M[2]),F.push(M[1],M[1]+M[3]))};_((A=N.face)==null?void 0:A.box),_((x=N.body)==null?void 0:x.box),_((b=(y=N.hands)==null?void 0:y.left)==null?void 0:b.box),_((k=(w=N.hands)==null?void 0:w.right)==null?void 0:k.box);let P=Math.min(...R),T=Math.min(...F);N.box=[P,T,Math.max(...R)-P,Math.max(...F)-T],r&&r[1]&&r[2]&&(N.boxRaw=[N.box[0]/r[2],N.box[1]/r[1],N.box[2]/r[2],N.box[3]/r[1]]),o.push(N)}return o}var C0=`
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2Q==`;async function T2e(e){let t=(r,a="application/octet-stream")=>fetch(`data:${a};base64,${r}`).then(o=>o.blob()),n,s;switch(e.config.warmup){case"face":n=await t(C0);break;case"body":case"full":n=await t(T0);break;default:n=null}if(n){let r=await createImageBitmap(n);s=await e.detect(r,e.config),r.close()}return s}async function N2e(e){return new Promise(t=>{let n;switch(e.config.warmup){case"face":n="data:image/jpeg;base64,"+C0;break;case"full":case"body":n="data:image/jpeg;base64,"+T0;break;default:n=null}let s;typeof Image!="undefined"?s=new Image:ge.Image&&(s=new ge.Image),s.onload=async()=>{let r=Kn(s.naturalWidth,s.naturalHeight);if(!r)se("Warmup: Canvas not found"),t({});else{let a=r.getContext("2d");a&&a.drawImage(s,0,0);let o=await e.image(r),i=await e.detect(o.tensor,e.config);t(i)}},n?s.src=n:t(null)})}async function E2e(e){let t=r=>Buffer.from(r,"base64"),n;if(e.config.warmup==="face"&&(n=t(C0)),(e.config.warmup==="body"||e.config.warmup==="full")&&(n=t(T0)),!n)return 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0);de(this,"gl");de(this,"analyze",(...t)=>{if(!qc(this,Mp))return;let n=this.tf.engine().state.numTensors,s=qc(this,Dc);Kc(this,Dc,n);let r=n-s;r!==0&&se(...t,r)});Xc(this,N0,t=>{if(!qc(this,zp))return null;if(!t)return"input is not defined";if(this.env.node&&!(t instanceof Je))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});de(this,"similarity",GT);de(this,"distance",I0);de(this,"match",HT);de(this,"emit",t=>{var n;this.events&&this.events.dispatchEvent&&((n=this.events)==null||n.dispatchEvent(new Event(t)))});this.env=ge,ka.wasmPath=Tp.includes("-")?"https://vladmandic.github.io/tfjs/dist/":`https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-backend-wasm@${Tp}/dist/`,ka.modelBasePath=ge.browser?"../models/":"file://models/",ka.backend=ge.browser?"humangl":"tensorflow",this.version=zx,Object.defineProperty(this,"version",{value:zx}),this.config=JSON.parse(JSON.stringify(ka)),Object.seal(this.config),t&&(this.config=$n(this.config,t)),this.tf=Pl,this.state="idle",Kc(this,Dc,0),Kc(this,Mp,!1),Kc(this,zp,!1),this.performance={},this.events=typeof EventTarget!="undefined"?new EventTarget:void 0,this.models=new Kb,this.draw={options:fa,canvas:(n,s)=>FT(n,s),face:(n,s,r)=>e5(n,s,r),body:(n,s,r)=>t5(n,s,r),hand:(n,s,r)=>n5(n,s,r),gesture:(n,s,r)=>Qb(n,s,r),object:(n,s,r)=>s5(n,s,r),person:(n,s,r)=>PT(n,s,r),all:(n,s,r)=>OT(n,s,r)},this.result={face:[],body:[],hand:[],gesture:[],object:[],performance:{},timestamp:0,persons:[]},this.process={tensor:null,canvas:null},this.faceTriangulation=P8,this.faceUVMap=F8,this.gl=It,this.emit("create")}reset(){let t=this.config.backend;this.config=JSON.parse(JSON.stringify(ka)),this.config.backend=t}validate(t){return Jg(ka,t||this.config)}now(){return ce()}image(t,n=!0){return kc(t,this.config,n)}async segmentation(t,n){return NT(t,n,this.config)}enhance(t){return Ab(t)}compare(t,n){return Y6(this.config,t,n)}async init(){await S0(this,!0),await this.tf.ready()}async load(t){this.state="load";let n=ce(),s=Object.values(this.models).filter(o=>o).length;t&&(this.config=$n(this.config,t)),this.env.initial&&(this.config.debug&&se(`version: ${this.version}`),this.config.debug&&se(`tfjs version: ${this.tf.version_core}`),await S0(this)||se("error: backend check failed"),await nf(),this.env.browser&&(this.config.debug&&se("configuration:",this.config),this.config.debug&&se("environment:",this.env),this.config.debug&&se("tf flags:",this.tf.ENV.flags))),await ET(this),this.env.initial&&this.config.debug&&se("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),this.env.initial=!1,Object.values(this.models).filter(o=>o).length!==s&&(await RT(this),this.emit("load"));let a=Math.trunc(ce()-n);a>(this.performance.loadModels||0)&&(this.performance.loadModels=this.env.perfadd?(this.performance.loadModels||0)+a:a)}next(t=this.result){return VT(t,this.config)}async warmup(t){let n=ce(),s=await qT(this,t),r=ce();return this.performance.warmup=Math.trunc(r-n),s}async profile(t,n){let s=await this.tf.profile(()=>this.detect(t,n)),r={};for(let i of s.kernels)r[i.name]?r[i.name]+=i.kernelTimeMs:r[i.name]=i.kernelTimeMs;let a=[];Object.entries(r).forEach(i=>a.push({name:i[0],ms:i[1]})),a.sort((i,l)=>l.ms-i.ms),a.length=20;let o={};for(let i of a)o[i.name]=i.ms;return o}async detect(t,n){return this.state="detect",new Promise(async s=>{var g,A,x,y,b,w,k,C,N,R,F,_,P,T,M,U,j,z,X,Z,J,ee;this.state="config";let r;this.config=$n(this.config,n),this.state="check";let a=qc(this,N0).call(this,t);a&&(se(a,t),s({error:a}));let o=ce();await S0(this),await this.load(),r=ce(),this.state="image";let i=await kc(t,this.config);if(this.process=i,this.performance.inputProcess=this.env.perfadd?(this.performance.inputProcess||0)+Math.trunc(ce()-r):Math.trunc(ce()-r),this.analyze("Get Image:"),!i.tensor){this.config.debug&&se("could not convert input to tensor"),s({error:"could not convert input to tensor"});return}this.emit("image"),r=ce(),this.config.skipAllowed=await Z6(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(ce()-r):Math.trunc(ce()-r),this.analyze("Check Changed:");let l=[],c=[],u=[],d=[];this.state="detect:face",this.config.async?(l=this.config.face.enabled?r5(this,i.tensor):[],this.performance.face&&delete this.performance.face):(r=ce(),l=this.config.face.enabled?await r5(this,i.tensor):[],this.performance.face=this.env.perfadd?(this.performance.face||0)+Math.trunc(ce()-r):Math.trunc(ce()-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?$n(this.config,{body:{maxDetected:this.config.face.enabled?1*l.length:1}}):this.config;this.config.async?(((g=this.config.body.modelPath)==null?void 0:g.includes("posenet"))?c=this.config.body.enabled?jb(i.tensor,p):[]:((A=this.config.body.modelPath)==null?void 0:A.includes("blazepose"))?c=this.config.body.enabled?eb(i.tensor,p):[]:((x=this.config.body.modelPath)==null?void 0:x.includes("efficientpose"))?c=this.config.body.enabled?ub(i.tensor,p):[]:((y=this.config.body.modelPath)==null?void 0:y.includes("movenet"))&&(c=this.config.body.enabled?zb(i.tensor,p):[]),this.performance.body&&delete this.performance.body):(r=ce(),((b=this.config.body.modelPath)==null?void 0:b.includes("posenet"))?c=this.config.body.enabled?await jb(i.tensor,p):[]:((w=this.config.body.modelPath)==null?void 0:w.includes("blazepose"))?c=this.config.body.enabled?await eb(i.tensor,p):[]:((k=this.config.body.modelPath)==null?void 0:k.includes("efficientpose"))?c=this.config.body.enabled?await ub(i.tensor,p):[]:((C=this.config.body.modelPath)==null?void 0:C.includes("movenet"))&&(c=this.config.body.enabled?await zb(i.tensor,p):[]),this.performance.body=this.env.perfadd?(this.performance.body||0)+Math.trunc(ce()-r):Math.trunc(ce()-r)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.state="detect:hand";let h=this.config.hand.maxDetected===-1?$n(this.config,{hand:{maxDetected:this.config.face.enabled?2*l.length:1}}):this.config;this.config.async?(((R=(N=this.config.hand.detector)==null?void 0:N.modelPath)==null?void 0:R.includes("handdetect"))?u=this.config.hand.enabled?Sb(i.tensor,h):[]:((_=(F=this.config.hand.detector)==null?void 0:F.modelPath)==null?void 0:_.includes("handtrack"))&&(u=this.config.hand.enabled?Eb(i.tensor,h):[]),this.performance.hand&&delete this.performance.hand):(r=ce(),((T=(P=this.config.hand.detector)==null?void 0:P.modelPath)==null?void 0:T.includes("handdetect"))?u=this.config.hand.enabled?await Sb(i.tensor,h):[]:((U=(M=this.config.hand.detector)==null?void 0:M.modelPath)==null?void 0:U.includes("handtrack"))&&(u=this.config.hand.enabled?await Eb(i.tensor,h):[]),this.performance.hand=this.env.perfadd?(this.performance.hand||0)+Math.trunc(ce()-r):Math.trunc(ce()-r)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.state="detect:object",this.config.async?(((j=this.config.object.modelPath)==null?void 0:j.includes("nanodet"))?d=this.config.object.enabled?Bb(i.tensor,this.config):[]:((z=this.config.object.modelPath)==null?void 0:z.includes("centernet"))&&(d=this.config.object.enabled?sb(i.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(r=ce(),((X=this.config.object.modelPath)==null?void 0:X.includes("nanodet"))?d=this.config.object.enabled?await Bb(i.tensor,this.config):[]:((Z=this.config.object.modelPath)==null?void 0:Z.includes("centernet"))&&(d=this.config.object.enabled?await sb(i.tensor,this.config):[]),this.performance.object=this.env.perfadd?(this.performance.object||0)+Math.trunc(ce()-r):Math.trunc(ce()-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=ce(),f=[...LT(l),...zT(c),...WT(u),...BT(l)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=this.env.perfadd?(this.performance.gesture||0)+Math.trunc(ce()-r):Math.trunc(ce()-r)),this.performance.total=this.env.perfadd?(this.performance.total||0)+Math.trunc(ce()-o):Math.trunc(ce()-o);let m=((ee=(J=this.process)==null?void 0:J.tensor)==null?void 0:ee.shape)||[];this.result={face:l,body:c,hand:u,gesture:f,object:d,performance:this.performance,canvas:this.process.canvas,timestamp:Date.now(),get persons(){return jT(l,c,u,f,m)}},ne(i.tensor),this.emit("detect"),this.state="idle",s(this.result)})}};Dc=new WeakMap,Mp=new WeakMap,zp=new WeakMap,N0=new WeakMap;return R2e;})();
|
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/**
|
|
* @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.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* Human main module
|
|
* @default Human Library
|
|
* @summary <https://github.com/vladmandic/human>
|
|
* @author <https://github.com/vladmandic>
|
|
* @copyright <https://github.com/vladmandic>
|
|
* @license MIT
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/** @license See the LICENSE file. */
|