4224 lines
1023 KiB
JavaScript
4224 lines
1023 KiB
JavaScript
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/*
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Face-API
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homepage: <https://github.com/vladmandic/face-api>
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author: <https://github.com/vladmandic>'
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*/
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`)}function ZB(r,e,t,o){let n=ct(e),s=o[o.length-1],a=new Array(s).fill(0),i=e.length,l=t==="complex64"?dm(r):r;if(i>1)for(let u=0;u<n/s;u++){let c=u*s;for(let p=0;p<s;p++)a[p]=Math.max(a[p],fm(l[c+p],0,t).length)}return a}function fm(r,e,t){let o;return Array.isArray(r)?o=`${parseFloat(r[0].toFixed(vb))} + ${parseFloat(r[1].toFixed(vb))}j`:is(r)?o=`'${r}'`:t==="bool"?o=NI(r):o=parseFloat(r.toFixed(vb)).toString(),Ol(o,e)}function NI(r){return r===0?"false":"true"}function Ph(r,e,t,o,n,s=!0){let a=t==="complex64"?2:1,i=e[0],l=e.length;if(l===0){if(t==="complex64"){let h=dm(r);return[fm(h[0],0,t)]}return t==="bool"?[NI(r[0])]:[r[0].toString()]}if(l===1){if(i>CI){let g=mm*a,y=Array.from(r.slice(0,g)),b=Array.from(r.slice((i-mm)*a,i*a));return t==="complex64"&&(y=dm(y),b=dm(b)),["["+y.map((w,_)=>fm(w,n[_],t)).join(", ")+", ..., "+b.map((w,_)=>fm(w,n[i-mm+_],t)).join(", ")+"]"]}let h=t==="complex64"?dm(r):Array.from(r);return["["+h.map((g,y)=>fm(g,n[y],t)).join(", ")+"]"]}let u=e.slice(1),c=o.slice(1),p=o[0]*a,m=[];if(i>CI){for(let h=0;h<mm;h++){let g=h*p,y=g+p;m.push(...Ph(r.slice(g,y),u,t,c,n,!1))}m.push("...");for(let h=i-mm;h<i;h++){let g=h*p,y=g+p;m.push(...Ph(r.slice(g,y),u,t,c,n,h===i-1))}}else for(let h=0;h<i;h++){let g=h*p,y=g+p;m.push(...Ph(r.slice(g,y),u,t,c,n,h===i-1))}let f=l===2?",":"";m[0]="["+m[0]+f;for(let h=1;h<m.length-1;h++)m[h]=" "+m[h]+f;let d=`,
|
|
`;for(let h=2;h<l;h++)d+=`
|
|
`;return m[m.length-1]=" "+m[m.length-1]+"]"+(s?"":d),m}function dm(r){let e=[];for(let t=0;t<r.length;t+=2)e.push([r[t],r[t+1]]);return e}var lt=class{constructor(e,t,o){if(this.dtype=t,this.shape=e.slice(),this.size=ct(e),o!=null){let n=o.length;A(n===this.size,()=>`Length of values '${n}' does not match the size inferred by the shape '${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=o||pb(t,this.size),this.strides=Us(e)}set(e,...t){t.length===0&&(t=[0]),A(t.length===this.rank,()=>`The number of provided coordinates (${t.length}) must match the rank (${this.rank})`);let o=this.locToIndex(t);this.values[o]=e}get(...e){e.length===0&&(e=[0]);let t=0;for(let n of e){if(n<0||n>=this.shape[t]){let s=`Requested out of range element at ${e}. Buffer shape=${this.shape}`;throw new Error(s)}t++}let o=e[e.length-1];for(let n=0;n<e.length-1;++n)o+=this.strides[n]*e[n];return this.values[o]}locToIndex(e){if(this.rank===0)return 0;if(this.rank===1)return e[0];let t=e[e.length-1];for(let o=0;o<e.length-1;++o)t+=this.strides[o]*e[o];return t}indexToLoc(e){if(this.rank===0)return[];if(this.rank===1)return[e];let t=new Array(this.shape.length);for(let o=0;o<t.length-1;++o)t[o]=Math.floor(e/this.strides[o]),e-=t[o]*this.strides[o];return t[t.length-1]=e,t}get rank(){return this.shape.length}toTensor(){return Di().makeTensor(this.values,this.shape,this.dtype)}},Di=null,Mc=null,JB=null;function SI(r){Di=r}function TI(r){Mc=r}function AI(r){JB=r}var Ve=class{constructor(e,t,o,n){this.kept=!1,this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=ct(e),this.strides=Us(e),this.dataId=o,this.id=n,this.rankType=this.rank<5?this.rank.toString():"higher"}get rank(){return this.shape.length}async buffer(){let e=await this.data();return Mc.buffer(this.shape,this.dtype,e)}bufferSync(){return Mc.buffer(this.shape,this.dtype,this.dataSync())}async array(){let e=await this.data();return Pl(this.shape,e)}arraySync(){return Pl(this.shape,this.dataSync())}async data(){this.throwIfDisposed();let e=Di().read(this.dataId);if(this.dtype==="string"){let t=await e;try{return t.map(o=>Pc(o))}catch(o){throw new Error("Failed to decode the string bytes into utf-8. To get the original bytes, call tensor.bytes().")}}return e}dataSync(){this.throwIfDisposed();let e=Di().readSync(this.dataId);if(this.dtype==="string")try{return e.map(t=>Pc(t))}catch(t){throw new Error("Failed to decode the string bytes into utf-8. 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n=++this.pendingBackendInitId,s=o.then(a=>n<this.pendingBackendInitId?!1:(this.registry[e]=a,this.pendingBackendInit=null,!0)).catch(a=>(n<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(a.stack||a.message)),!1));return this.pendingBackendInit=s,{success:s,asyncInit:!0}}else return this.registry[e]=o,{success:!0,asyncInit:!1}}catch(o){return console.warn(`Initialization of backend ${e} failed`),console.warn(o.stack||o.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${e} backend not found in registry`);this.backendName===e&&this.pendingBackendInit!=null&&this.pendingBackendInitId++,e in this.registry&&(this.disposeRegisteredKernels(e),this.registry[e].dispose(),delete this.registry[e]),delete 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b=this.backend.numDataIds();l=y.kernelFunc({inputs:h,attrs:g,backend:this.backend});let w=Array.isArray(l)?l:[l];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(d,b,w);let _=w.map(C=>{if(C.rank!=null)return C;let{dataId:D,shape:T,dtype:R}=C;return this.makeTensorFromDataId(D,T,R)});if(n){let C=this.getTensorsForGradient(d,h,_);o=this.saveTensorsForBackwardMode(C)}return _}}else{let{forwardFunc:d}=e,h=g=>{!n||(o=g.map(y=>this.keep(this.clone(y))))};i=()=>{let g=this.backend.numDataIds();l=this.tidy(()=>d(this.backend,h));let y=Array.isArray(l)?l:[l];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,g,y),y}}let{inputs:c,attrs:p}=e,m=Eb(e)?null:e.backwardsFunc,f;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(f=this.profiler.profileKernel(u,c,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(f),t=f.outputs)}),n&&this.addTapeNode(u,c,t,m,o,p),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-s,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-a,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(c).map(d=>c[d]!=null?c[d].shape:null),outputShapes:t.map(d=>d.shape),kernelTimeMs:f.timeMs,extraInfo:f.extraInfo}),Array.isArray(l)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(o=>this.keep(this.clone(o)))}getTensorsForGradient(e,t,o){let n=Oh(e);if(n!=null){let s=n.inputsToSave||[],a=n.outputsToSave||[],i;n.saveAllInputs?(A(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=s.map(u=>t[u]);let 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o=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(o=e.size*Rh(e.dtype)),this.state.numBytes+=o,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:o})),e instanceof el||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 o=e.size*Rh(e.dtype);this.state.numBytes-=o}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let t=this.state.registeredVariables[e];this.disposeVariable(t)}}disposeVariable(e){this.disposeTensor(e),this.state.registeredVariables[e.name]!=null&&delete this.state.registeredVariables[e.name]}memory(){let e=this.backend.memory();return e.numTensors=this.state.numTensors,e.numDataBuffers=this.state.numDataBuffers,e.numBytes=this.state.numBytes,this.state.numStringTensors>0&&(e.unreliable=!0,e.reasons==null&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e}async profile(e){this.state.profiling=!0;let t=this.state.numBytes,o=this.state.numTensors;this.state.activeProfile.kernels=[],this.state.activeProfile.result=await e(),this.state.profiling=!1,this.state.activeProfile.peakBytes=Math.max(...this.state.activeProfile.kernels.map(n=>n.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-o;for(let n of 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Actual: ${n}.
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|
Expected: ${s}.`);for(let a=0;a<s.length;++a){let i=n[a],l=s[a];if(!t(i,l))throw new Error(`Arrays differ: actual[${a}] = ${i}, expected[${a}] = ${l}.
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Actual: ${n}.
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Expected: ${s}.`)}}function q3(r,e){r().then(()=>e.fail(),()=>e())}function K3(r,e){let t=typeof e=="string"||typeof e=="number"||typeof e=="boolean"?[e]:e;return is(r)||is(r[0])||is(e)||is(e[0])?Yb(r,t,(o,n)=>o==n):Yb(r,e,(o,n)=>Zb(o,n,0))}function X3(r,e,t){if(t==null&&(t=Xb()),!Zb(r,e,t))throw new Error(`Numbers differ: actual === ${r}, expected === ${e}`)}function Zb(r,e,t){return!isFinite(r)&&!isFinite(e)?!0:!(isNaN(r)||isNaN(e)||Math.abs(r-e)>t)}function Y3(r,e,t){for(let o=0;o<r.length;o++)if(r[o]<e||r[o]>t)throw new Error(`Value out of range:${r[o]} low: ${e}, high: ${t}`)}function Z3(r,e){expect(new Float32Array(r)).toEqual(new Float32Array(e))}function IN(r){for(let e=0;e<r.length;e++){let t=r[e];Array.isArray(t)?IN(t):r[e]=Qa(t)}return r}var Jb="3.2.0";function J3(){W().set("PROD",!0)}function Q3(){W().set("DEBUG",!0)}function eV(){W().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function 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Got strides ${t} and dilations '${s}'`);let m={x:u,filter:l},f={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},d=E.runKernel(Zo,m,f);return c?M(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var Kr=S({conv2d_:tG});function rG(r,e,t,o,n="NWC",s=1,a){let i=k(r,"x","conv1d"),l=k(e,"filter","conv1d"),u=i,c=!1;i.rank===2&&(c=!0,u=M(i,[1,i.shape[0],i.shape[1]])),A(u.rank===3,()=>`Error in conv1d: input must be rank 3, but got rank ${u.rank}.`),A(l.rank===3,()=>`Error in conv1d: filter must be rank 3, but got rank ${l.rank}.`),a!=null&&A(ot(o),()=>`Error in conv1d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`),A(u.shape[2]===l.shape[1],()=>`Error in conv1d: depth of input (${u.shape[2]}) must match input depth for filter ${l.shape[1]}.`),A(wr(t,s),()=>`Error in conv1D: Either stride or dilation must be 1. Got stride ${t} and dilation '${s}'`),A(n==="NWC",()=>`Error in conv1d: got dataFormat of ${n} but only NWC is currently supported.`);let p=M(l,[1,l.shape[0],l.shape[1],l.shape[2]]),m=M(u,[u.shape[0],1,u.shape[1],u.shape[2]]),g=Kr(m,p,[1,t],o,"NHWC",[1,s],a);return c?M(g,[g.shape[2],g.shape[3]]):M(g,[g.shape[0],g.shape[2],g.shape[3]])}var wu=S({conv1d_:rG});function oG(r,e,t,o,n,s="NHWC",a){A(r.length===e.rank,()=>`Length of inShape (${r.length}) and rank of dy (${e.rank}) must match`);let i=r,l=e,u=!1;e.rank===3&&(u=!0,l=M(e,[1,e.shape[0],e.shape[1],e.shape[2]]),i=[1,r[0],r[1],r[2]]),A(i.length===4,()=>`Error in conv2dDerInput: inShape must be length 4, but got length ${i.length}.`),A(l.rank===4,()=>`Error in conv2dDerInput: dy must be rank 4, but got rank ${l.rank}`),A(t.rank===4,()=>`Error in conv2dDerInput: filter must be rank 4, but got rank ${t.rank}`);let c=s==="NHWC"?i[3]:i[1],p=s==="NHWC"?l.shape[3]:l.shape[1];A(c===t.shape[2],()=>`Error in conv2dDerInput: depth of input (${c}) must match input depth for filter ${t.shape[2]}.`),A(p===t.shape[3],()=>`Error in conv2dDerInput: depth of output (${p}) must match output depth for filter ${t.shape[3]}.`),a!=null&&A(ot(n),()=>`Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode ${a} but got pad ${n}.`);let m={dy:l,filter:t},f={strides:o,pad:n,dataFormat:s,dimRoundingMode:a,inputShape:i},d=E.runKernel(Jo,m,f);return u?M(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var jc=S({conv2DBackpropInput_:oG});function nG(r,e,t,o,n,s){let a=k(r,"x","conv2dTranspose"),i=k(e,"filter","conv2dTranspose");return jc(t,a,i,o,n,"NHWC",s)}var _u=S({conv2dTranspose_:nG});function sG(r,e,t,o,n="NDHWC",s=[1,1,1]){let a=k(r,"x","conv3d"),i=k(e,"filter","conv3d"),l=a,u=!1;a.rank===4&&(u=!0,l=M(a,[1,a.shape[0],a.shape[1],a.shape[2],a.shape[3]])),A(l.rank===5,()=>`Error in conv3d: input must be rank 5, but got rank ${l.rank}.`),A(i.rank===5,()=>`Error in conv3d: filter must be rank 5, but got rank ${i.rank}.`),A(l.shape[4]===i.shape[3],()=>`Error in conv3d: depth of input (${l.shape[4]}) must match input depth for filter ${i.shape[3]}.`),A(wr(t,s),()=>`Error in conv3D: Either strides or dilations must be 1. 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${n} and ${e} for depthToSpace with input shape
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${o.shape}`),A(s*e>=0,()=>`Negative dimension size caused by overflow when multiplying
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${s} and ${e} for depthToSpace with input shape
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${o.shape}`),A(a%(e*e)==0,()=>`Dimension size must be evenly divisible by ${e*e} but is ${a} for depthToSpace with input shape ${o.shape}`);let i={x:o},l={blockSize:e,dataFormat:t};return E.runKernel(ei,i,l)}var Tm=S({depthToSpace_:fG});function dG(r,e,t,o,n="NHWC",s=[1,1],a){let i=k(r,"x","depthwiseConv2d"),l=k(e,"filter","depthwiseConv2d"),u=i,c=!1;i.rank===3&&(c=!0,u=M(i,[1,i.shape[0],i.shape[1],i.shape[2]])),A(u.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${u.rank}.`),A(l.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${l.rank}.`),A(u.shape[3]===l.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${u.shape[3]}) must match the inChannels dimension in filter ${l.shape[2]}.`),a!=null&&A(ot(o),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${a} but got pad ${o}.`);let p={x:u,filter:l},m={strides:t,pad:o,dataFormat:n,dilations:s,dimRoundingMode:a},f=E.runKernel(tn,p,m);return c?M(f,[f.shape[1],f.shape[2],f.shape[3]]):f}var Cs=S({depthwiseConv2d_:dG});function hG(r){let t={x:k(r,"x","diag")};return E.runKernel(Xl,t)}var gG=S({diag_:hG});function xG(r,e,t,o,n=[1,1],s="NHWC"){let a=k(r,"x","dilation2d"),i=k(e,"filter","dilation2d");A(a.rank===3||a.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${a.rank}.`),A(i.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${i.rank}.`),A(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let l=a,u=!1;a.rank===3&&(l=M(a,[1,a.shape[0],a.shape[1],a.shape[2]]),u=!0);let c={x:l,filter:i},p={strides:t,pad:o,dilations:n},m=E.runKernel(sa,c,p);return u?M(m,[m.shape[1],m.shape[2],m.shape[3]]):m}var Am=S({dilation2d_:xG});function yG(r,e){let t=r.length,o=[];for(let n=0;n<t;n++){let s=t-1-n,a=r[s]||1;(e[e.length-1-n]||1)>1&&a===1&&o.unshift(s)}return o}function _t(r,e){let t=[];for(let o=0;o<e.length;o++){let n=r[r.length-o-1],s=e.length-o-1,a=e[s];(n==null||n===1&&a>1)&&t.unshift(s)}return t}function Be(r,e){let t=[],o=Math.max(r.length,e.length);for(let n=0;n<o;n++){let s=r[r.length-n-1];s==null&&(s=1);let a=e[e.length-n-1];if(a==null&&(a=1),s===1)t.unshift(a);else if(a===1)t.unshift(s);else if(s!==a){let i=`Operands could not be broadcast together with shapes ${r} and ${e}.`;throw Error(i)}else t.unshift(s)}return t}function bG(r,e){let t=k(r,"a","equal"),o=k(e,"b","equal");[t,o]=Ge(t,o),Be(t.shape,o.shape);let n={a:t,b:o};return E.runKernel(oi,n)}var vo=S({equal_:bG});function wG(r,e,t){let o=k(e,"a","where"),n=k(t,"b","where"),s=k(r,"condition","where","bool"),a=Be(o.shape,n.shape),i=sl(o,a),l=sl(n,a);s.rank===1&&A(s.shape[0]===o.shape[0],()=>"The first dimension of `a` must match the size of `condition`."),s.rank!==1&&vt(s.shape,l.shape,"Error in where: ");let u={condition:s,t:i,e:l};return E.runKernel(hs,u)}var Dt=S({where_:wG});function _G(r){let t={x:k(r,"x","zerosLike")};return E.runKernel(bs,t)}var Ce=S({zerosLike_:_G});function kG(r,e){let t=k(r,"a","div"),o=k(e,"b","div");[t,o]=Ge(t,o);let n=me(t,o),s=Ce(n),a=vo(o,s);return Dt(a,s,n)}var Em=S({divNoNan_:kG});function vG(r,e){let t=k(r,"t1","dot"),o=k(e,"t2","dot");A((t.rank===1||t.rank===2)&&(o.rank===1||o.rank===2),()=>`Error in dot: inputs must all be rank 1 or 2, but got ranks ${t.rank} and ${o.rank}.`);let n=t.rank===1?t.size:t.shape[1],s=o.rank===1?o.size:o.shape[0];if(A(n===s,()=>`Error in dot: inner dimensions of inputs must match, but got ${n} and ${s}.`),t.rank===1&&o.rank===1){let a=M(t,[1,-1]),i=M(o,[-1,1]),l=We(a,i);return M(l,[])}else if(t.rank===1&&o.rank===2){let a=M(t,[1,-1]),i=M(o,[o.shape[0],o.shape[1]]),l=We(a,i);return M(l,[l.size])}else if(t.rank===2&&o.rank===1){let a=M(o,[-1,1]),i=We(t,a);return M(i,[i.size])}else{let a=M(o,[o.shape[0],o.shape[1]]);return We(t,a)}}var fw=S({dot_:vG});function CG(r){let t={x:k(r,"x","elu")};return E.runKernel(ti,t)}var Is=S({elu_:CG});function IG(r){let e=k(r,"x","erf");A(e.dtype==="int32"||e.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),e.dtype==="int32"&&(e=ne(e,"float32"));let t={x:e};return E.runKernel(ri,t)}var Dm=S({erf_:IG});function NG(r){let t={x:k(r,"x","exp")};return E.runKernel(on,t)}var Yt=S({exp_:NG});function SG(r,e=0){let t=k(r,"x","expandDims","string_or_numeric");A(e<=t.rank,()=>"Axis must be <= rank of the tensor");let o={input:t},n={dim:e};return E.runKernel(us,o,n)}var ir=S({expandDims_:SG});function TG(r){let t={x:k(r,"x","expm1")};return E.runKernel(ni,t)}var $m=S({expm1_:TG});function AG(r,e){let t=k(r,"x","tile","string_or_numeric");A(t.rank===e.length,()=>`Error in transpose: rank of input ${t.rank} must match length of reps ${e}.`);let o={x:t},n={reps:e};return E.runKernel(_o,o,n)}var Lo=S({tile_:AG});function EG(r,e,t,o="float32"){e==null&&(e=r);let n=ve([r,e],o),s=r<=e?r:e;for(let i=0;i<s;++i)n.set(1,i,i);let a=M(n.toTensor(),[r,e]);if(t==null)return a;if(t.length===1)return Lo(ir(a,0),[t[0],1,1]);if(t.length===2)return Lo(ir(ir(a,0),0),[t[0],t[1],1,1]);if(t.length===3)return Lo(ir(ir(ir(a,0),0),0),[t[0],t[1],t[2],1,1]);throw new Error(`eye() currently supports only 1D and 2D batchShapes, but received ${t.length}D.`)}var Hc=S({eye_:EG});function va(r,e,t){let o={shape:r,value:e,dtype:t};return E.runKernel(ia,{},o)}function DG(r){let t={x:k(r,"x","floor")};return E.runKernel(nn,t)}var Ns=S({floor_:DG});function $G(r,e,t=0,o=0){let n=k(r,"x","gather"),s=k(e,"indices","gather","int32"),a={x:n,indices:s},i={axis:t,batchDims:o};return E.runKernel(cs,a,i)}var zn=S({gather_:$G});function RG(r,e){let t=k(r,"a","greater"),o=k(e,"b","greater");[t,o]=Ge(t,o),Be(t.shape,o.shape);let n={a:t,b:o};return E.runKernel(ai,n)}var Qt=S({greater_:RG});function FG(r,e){let t=k(r,"a","greaterEqual"),o=k(e,"b","greaterEqual");[t,o]=Ge(t,o),Be(t.shape,o.shape);let n={a:t,b:o};return E.runKernel(ln,n)}var io=S({greaterEqual_:FG});function OG(r){let t={input:k(r,"input","imag")};return E.runKernel(Ql,t)}var Cu=S({imag_:OG});function PG(r){let t={x:k(r,"x","isFinite")};return E.runKernel(li,t)}var dw=S({isFinite_:PG});function MG(r){let t={x:k(r,"x","isInf")};return E.runKernel(ui,t)}var hw=S({isInf_:MG});function LG(r){let t={x:k(r,"x","isNaN")};return E.runKernel(ci,t)}var gw=S({isNaN_:LG});function zG(r,e=.2){let o={x:k(r,"x","leakyRelu")},n={alpha:e};return E.runKernel(un,o,n)}var Ca=S({leakyRelu_:zG});function BG(r,e){let t=k(r,"a","less"),o=k(e,"b","less");[t,o]=Ge(t,o),Be(t.shape,o.shape);let n={a:t,b:o};return E.runKernel(pi,n)}var Iu=S({less_:BG});function VG(r,e){let t=k(r,"a","lessEqual"),o=k(e,"b","lessEqual");[t,o]=Ge(t,o),Be(t.shape,o.shape);let n={a:t,b:o};return E.runKernel(mi,n)}var zo=S({lessEqual_:VG});function xw(r,e,t){if(t<=0)throw new Error("The number of values should be positive.");let o={start:r,stop:e,num:t};return E.runKernel(eu,{},o)}function GG(r,e=5,t=1,o=1,n=.5){let s=k(r,"x","localResponseNormalization");A(s.rank===4||s.rank===3,()=>`Error in localResponseNormalization: x must be rank 3 or 4 but got
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rank ${s.rank}.`),A(ot(e),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${e}.`);let a=s,i=!1;s.rank===3&&(i=!0,a=M(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let l={x:a},u={depthRadius:e,bias:t,alpha:o,beta:n},c=E.runKernel(aa,l,u);return i?M(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var Rm=S({localResponseNormalization_:GG});function WG(r){let t={x:k(r,"x","log")};return E.runKernel(cn,t)}var ar=S({log_:WG});function UG(r){let t={x:k(r,"x","log1p")};return E.runKernel(fi,t)}var Nu=S({log1p_:UG});function jG(r){return A(Ws(r),()=>"The f passed in grad(f) must be a function"),(e,t)=>{let o=k(e,"x","tf.grad","string_or_numeric"),n=t!=null?k(t,"dy","tf.grad"):null;return E.tidy(()=>{let{value:s,grads:a}=E.gradients(()=>r(o),[o],n);return n!=null&&vt(s.shape,n.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Qh(a),a[0]})}}function HG(r){return A(Ws(r),()=>"The f passed in grads(f) must be a function"),(e,t)=>{A(Array.isArray(e),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let o=ha(e,"args","tf.grads","string_or_numeric"),n=t!=null?k(t,"dy","tf.grads"):null;return E.tidy(()=>{let{value:s,grads:a}=E.gradients(()=>r(...o),o,n);return n!=null&&vt(s.shape,n.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Qh(a),a})}}function qG(r){return A(Ws(r),()=>"The f passed in valueAndGrad(f) must be a function"),(e,t)=>{A(e instanceof Ve,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),A(t==null||t instanceof Ve,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:o,value:n}=E.gradients(()=>r(e),[e],t);return Qh(o),{grad:o[0],value:n}}}function KG(r){return A(Ws(r),()=>"The f passed in valueAndGrads(f) must be a function"),(e,t)=>{A(Array.isArray(e)&&e.every(n=>n instanceof Ve),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),A(t==null||t instanceof Ve,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let o=E.gradients(()=>r(...e),e,t);return t!=null&&vt(o.value.shape,t.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Qh(o.grads),o}}function eg(r,e){A(Ws(r),()=>"The f passed in variableGrads(f) must be a function"),A(e==null||Array.isArray(e)&&e.every(u=>u instanceof el),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let t=e!=null;if(!t){e=[];for(let u in E.registeredVariables)e.push(E.registeredVariables[u])}let o=t?e.filter(u=>!u.trainable):null,n=e.length;e=e.filter(u=>u.trainable),A(e.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${n} variables is trainable.`);let s=!0,{value:a,grads:i}=E.gradients(r,e,null,s);A(i.some(u=>u!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),A(a.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${a.rank} tensor`);let l={};return e.forEach((u,c)=>{i[c]!=null&&(l[u.name]=i[c])}),o!=null&&o.forEach(u=>l[u.name]=null),{value:a,grads:l}}function Xr(r){return E.customGrad(r)}function Qh(r){if(r.filter(t=>t==null).length>0)throw new Error(`Cannot compute gradient of y=f(x) with respect to x. Make sure that
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K4={fft:Ea,ifft:Pi,rfft:Da,irfft:Pu},X4={hammingWindow:XN,hannWindow:ag,frame:lg,stft:YN},Ds={flipLeftRight:JN,resizeNearestNeighbor:cg,resizeBilinear:ug,rotateWithOffset:QN,cropAndResize:ZN,nonMaxSuppression:e0,nonMaxSuppressionAsync:o0,nonMaxSuppressionWithScore:n0,nonMaxSuppressionWithScoreAsync:s0,nonMaxSuppressionPadded:i0,nonMaxSuppressionPaddedAsync:a0},jw={bandPart:l0,gramSchmidt:u0,qr:p0},Y4={absoluteDifference:m0,computeWeightedLoss:Sr,cosineDistance:f0,hingeLoss:d0,huberLoss:h0,logLoss:g0,meanSquaredError:x0,sigmoidCrossEntropy:y0,softmaxCrossEntropy:b0};var Or=class extends Xh{minimize(e,t=!1,o){let{value:n,grads:s}=this.computeGradients(e,o);if(o!=null){let a=o.map(i=>({name:i.name,tensor:s[i.name]}));this.applyGradients(a)}else this.applyGradients(s);return Te(s),t?n:(n.dispose(),null)}get iterations(){return this.iterations_==null&&(this.iterations_=0),this.iterations_}incrementIterations(){this.iterations_=this.iterations+1}computeGradients(e,t){return 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setWeights(e){e=await this.extractIterations(e);let t=e.length/2,o=!1;this.accumulatedGrads=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedUpdates=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}getConfig(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.rho,t.epsilon)}};Qc.className="Adadelta";so(Qc);var ep=class extends Or{constructor(e,t=.1){super();this.learningRate=e,this.initialAccumulatorValue=t,this.accumulatedGrads=[]}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=E.registeredVariables[o];if(this.accumulatedGrads[n]==null){let l=!1;this.accumulatedGrads[n]={originalName:`${o}/accumulator`,variable:V(()=>va(s.shape,this.initialAccumulatorValue).variable(l))}}let a=Array.isArray(e)?e[n].tensor:e[o];if(a==null)return;let i=this.accumulatedGrads[n].variable;V(()=>{let 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Or{constructor(e,t,o,n=null){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.accumulatedFirstMoment=[],this.accumulatedSecondMoment=[],V(()=>{this.accBeta1=ue(t).variable(),this.accBeta2=ue(o).variable()}),n==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);V(()=>{let o=ce(1,this.accBeta1),n=ce(1,this.accBeta2);t.forEach((s,a)=>{let i=E.registeredVariables[s],l=!1;this.accumulatedFirstMoment[a]==null&&(this.accumulatedFirstMoment[a]={originalName:`${s}/m`,variable:V(()=>Ce(i).variable(l))}),this.accumulatedSecondMoment[a]==null&&(this.accumulatedSecondMoment[a]={originalName:`${s}/v`,variable:V(()=>Ce(i).variable(l))});let u=Array.isArray(e)?e[a].tensor:e[s];if(u==null)return;let c=this.accumulatedFirstMoment[a].variable,p=this.accumulatedSecondMoment[a].variable,m=Q(O(c,this.beta1),O(u,1-this.beta1)),f=Q(O(p,this.beta2),O(Oe(u),1-this.beta2)),d=me(m,o),h=me(f,n);c.assign(m),p.assign(f);let g=Q(O(me(d,Q(gt(h),this.epsilon)),-this.learningRate),i);i.assign(g)}),this.accBeta1.assign(O(this.accBeta1,this.beta1)),this.accBeta2.assign(O(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Te(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Te(this.accumulatedSecondMoment.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedFirstMoment,...this.accumulatedSecondMoment];return[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e),V(()=>{this.accBeta1.assign(Fr(this.beta1,this.iterations_+1)),this.accBeta2.assign(Fr(this.beta2,this.iterations_+1))});let t=e.length/2,o=!1;this.accumulatedFirstMoment=e.slice(0,t).map(n=>({originalName:n.name,variable:n.tensor.variable(o)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(n=>({originalName:n.name,variable:n.tensor.variable(o)}))}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)}};tp.className="Adam";so(tp);var rp=class extends Or{constructor(e,t,o,n=null,s=0){super();this.learningRate=e,this.beta1=t,this.beta2=o,this.epsilon=n,this.decay=s,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=ue(0).variable(),this.accBeta1=ue(t).variable()}),n==null&&(this.epsilon=E.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(o=>o.name):Object.keys(e);V(()=>{let o=ce(1,this.accBeta1),n=me(-this.learningRate,Q(O(this.iteration,this.decay),1));t.forEach((s,a)=>{let 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Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};rp.className="Adamax";so(rp);var al=class extends Or{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(o=>o.name):Object.keys(e)).forEach((o,n)=>{let s=Array.isArray(e)?e[n].tensor:e[o];if(s==null)return;let a=E.registeredVariables[o];V(()=>{let i=Q(O(this.c,s),a);a.assign(i)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Et(ue(-e))}dispose(){this.c.dispose()}async getWeights(){return[await this.saveIterations()]}async setWeights(e){if(e=await this.extractIterations(e),e.length!==0)throw new Error("SGD optimizer does 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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}}},pH=0,Pe=class extends J.Serializable{constructor(e={}){super();this._callHook=null,this._addedWeightNames=[],this._stateful=!1,this.id=pH++,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 o=this.getClassName();t=jn(o)+"_"+cl(o)}if(this.name=t,this.trainable_=e.trainable==null?!0:e.trainable,e.inputShape!=null||e.batchInputShape!=null){let o;if(e.batchInputShape!=null)o=e.batchInputShape;else if(e.inputShape!=null){let s=null;e.batchSize!=null&&(s=e.batchSize),o=[s].concat(e.inputShape)}this.batchInputShape=o;let 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Hence the notion of "output shape" is ill-defined for the layer.`)}countParams(){if(!this.built)throw new Pr(`You tried to call countParams() on ${this.name}, but the layer is not built yet. Build it first by calling build(batchInputShape).`);return xp(this.weights)}build(e){this.built=!0}getWeights(e=!1){return xf(e?this.trainableWeights:this.weights)}setWeights(e){V(()=>{let t=this.weights;if(t.length!==e.length)throw new L(`You called setWeights(weights) on layer "${this.name}" with a weight list of length ${e.length}, but the layer was expecting ${t.length} weights. 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Found: ${this.outputs.map(b=>b.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let b of this.outputs){let w=b.sourceLayer,_=b.nodeIndex,C=b.tensorIndex;this.outputLayers.push(w),this.outputLayersNodeIndices.push(_),this.outputLayersTensorIndices.push(C)}for(let b of this.inputs){let w=b.sourceLayer,_=b.nodeIndex,C=b.tensorIndex;Bo(_===0,"input layer has >1 nodes"),Bo(C===0,"input layer has >1 tensors"),this.inputLayers.push(w),this.inputLayersNodeIndices.push(_),this.inputLayersTensorIndices.push(C)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let b=0;b<this.inputLayers.length;b++){let w=this.inputLayers[b];if(!(w instanceof Bi))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${b} (0-based) originates from layer type ${w.getClassName()}.`);this.inputNames.push(w.name),this.feedInputShapes.push(w.batchInputShape),this.feedInputNames.push(w.name)}for(let b of this.outputLayers)this.outputNames.push(b.name);this.internalInputShapes=this.inputs.map(b=>b.shape),this.internalOutputShapes=this.outputs.map(b=>b.shape);let t={},o={},n={},s={},a={},i=[],l=(b,w,_,C,D,T)=>{(C==null||D==null||T==null)&&(C=b.sourceLayer,D=b.nodeIndex,T=b.tensorIndex);let R=C.inboundNodes[D];if(_.indexOf(R)!==-1)throw new Pr(`The tensor ${b.name} at layer "${C.name}" is part of a cycle.`);if(w.indexOf(R)!==-1)return;this.containerNodes.add(Vo.nodeKey(C,D)),C.id in a||(a[C.id]=Object.keys(a).length),_.indexOf(R)===-1&&_.push(R);let P=R.inboundLayers.length;for(let B=0;B<P;B++){let G=R.inputTensors[B],U=R.inboundLayers[B],j=R.nodeIndices[B],H=R.tensorIndices[B];l(G,w,_,U,j,H)}for(w.push(R);_.indexOf(R)>=0;)_.splice(_.indexOf(R),1);i.push(R)},u=[],c=[];for(let b of this.outputs)l(b,u,c);let p=i.slice().reverse();for(let b of p){o[b.id]=b,b.id in t||(t[b.id]=0);let w=t[b.id],_=n[b.outboundLayer.id]==null?0:n[b.outboundLayer.id];w=Math.max(w,_),n[b.outboundLayer.id]=w,s[b.outboundLayer.id]=b.outboundLayer,t[b.id]=w;for(let C=0;C<b.inboundLayers.length;C++){let D=b.inboundLayers[C],T=b.nodeIndices[C],R=D.inboundNodes[T],P=t[R.id]==null?0:t[R.id];t[R.id]=Math.max(w+1,P),o[R.id]=R}}let m={};for(let b in t){let w=t[b];w in m||(m[w]=[]),m[w].push(o[b])}let f={};for(let b in n){let w=n[b];w in f||(f[w]=[]),f[w].push(s[b])}let d=Object.keys(f).map(b=>parseInt(b,10)).sort(tf);this.layers=[];for(let b of d){let w=f[b];w.sort((_,C)=>{let D=a[_.id],T=a[C.id];return D<T?-1:D>T?1:0});for(let _ of w)_ instanceof Vo&&this.internalContainerRefs.push(_),this.layers.push(_)}this.layersByDepth=f,d=Object.keys(m).map(b=>parseInt(b,10)).sort(tf);let h=this.inputs.slice(),g=[];for(let b of d)for(let w of m[b]){let _=w.outboundLayer;if(_!=null){for(let C of w.inputTensors)if(h.indexOf(C)===-1)throw new Pr(`Graph disconnected: cannot obtain value for tensor ${C} at layer "${_.name}". The following previous layers were accessed without issue: ${g}`);for(let C of w.outputTensors)h.push(C);g.push(_.name)}}this.nodesByDepth=m;let y=this.layers.map(b=>b.name);for(let b of y){let w=y.filter(_=>_===b).length;if(w!==1)throw new Pr(`The name "${b}" is used ${w} times in the model. All layer names should be unique. 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Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let o of this.layers)t.push(...o.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let o={},n=0;for(let a of this.layers)for(let i of a.weights){if(o[i.originalName]!=null)throw new L(`Duplicate weight name: ${i.originalName}`);o[i.originalName]=i,n++}let s=[];for(let a in e){let i=a;if(o[a]==null){let l=a.split("/");i=l.slice(0,-2).concat([l[l.length-1]]).join("/")}if(o[i]!=null)s.push([o[i],e[a]]);else if(t)throw new L(`Provided weight data has no target variable: ${a}`);delete o[i]}if(t){let a=[];for(let i in o)a.push(i);if(a.length>0)throw new L(`${a.length} of ${n} weights are not set: ${a}`)}yp(s)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${dl}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let o=Pg(this.updatedConfig());return t?JSON.stringify(o):o}call(e,t){return V(()=>{e=xt(e);let o=new Fs;for(let n=0;n<this.inputs.length;++n)o.add(this.inputs[n],e[n]);return Xu(this.outputs,o,t)})}computeMask(e,t){return V(()=>{e=xt(e);let o;return t==null?o=Un(null,e.length):o=xt(t),this.runInternalGraph(e,o)[1]})}computeOutputShape(e){let t=gp(e);if(t.length!==this.inputLayers.length)throw new L(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let o={};for(let i=0;i<t.length;i++){let l=this.inputLayers[i],u=t[i],c=l.name+"_0_0";o[c]=u}let n=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(tf);if(n.length>1)for(let i of n){let l=this.nodesByDepth[i];for(let u of l){let c=u.outboundLayer;if(this.inputLayers.map(h=>h.id).indexOf(c.id)!==-1)continue;let p=[];for(let h=0;h<u.inboundLayers.length;h++){let g=u.inboundLayers[h],y=u.nodeIndices[h],b=u.tensorIndices[h],w=`${g.name}_${y}_${b}`,_=o[w];p.push(_)}let m=c.computeOutputShape(hr(p)),f=gp(m),d=c.inboundNodes.indexOf(u);for(let h=0;h<f.length;h++){let g=`${c.name}_${d}_${h}`;o[g]=f[h]}}}let s=[],a=[];for(let i=0;i<this.outputLayers.length;i++){let l=this.outputLayers[i],u=this.outputLayersNodeIndices[i],c=this.outputLayersTensorIndices[i],p=`${l.name}_${u}_${c}`;a.push(p)}for(let i=0;i<a.length;i++){let l=a[i];Bo(l in o),s.push(o[l])}return hr(s)}runInternalGraph(e,t){t==null&&(t=Un(null,e.length));let o={};for(let l=0;l<this.inputs.length;++l){let u=this.inputs[l],c=e[l],p=t[l];o[u.id]=[c,p]}let n=Object.keys(this.nodesByDepth).map(l=>parseInt(l,10)).sort(tf);for(let l of n){let u=this.nodesByDepth[l];for(let c of u){let p=c.outboundLayer,m=c.inputTensors,f=c.outputTensors,d=new Array;for(let h of m)h.id in o&&d.push(o[h.id]);if(d.length===m.length){let h={},g,y,b,w;if(c.callArgs!=null&&(h=c.callArgs),d.length===1){let[_,C]=d[0];h.mask==null&&(h.mask=C),b=xt(p.call(_,h)),w=xt(p.computeMask(_,C)),g=[_],y=[C]}else g=d.map(_=>_[0]),y=d.map(_=>_[1]),h.mask==null&&(h.mask=y),b=xt(p.call(g,h)),w=xt(p.computeMask(g,y));if(p.activityRegularizer)throw new Ne("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let _=0;_<f.length;++_){let C=f[_],D=b[_],T=w[_];o[C.id]=[D,T]}}}}let s=[],a=[],i=[];for(let l of this.outputs){Bo(l.id in o,`Could not compute output ${l.name} : ${l.id}`);let[u,c]=o[l.id];i.push(u.shape),s.push(u),a.push(c)}return[s,a,i]}buildNodeConversionMap(e){let t={},o;for(let n of this.layers){o=n instanceof Vo?1:0;for(let s=0;s<n.inboundNodes.length;s++){let a=Vo.nodeKey(n,s);this.containerNodes.has(a)&&(t[a]=o,o+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new L(`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 L("Provide either a layer name or layer index");for(let o of this.layers)if(o.name===e)return o;throw new L(`No such layer: ${e}`)}calculateLosses(){return V(()=>{let e=[];for(let t of this.layers)for(let o=0;o<t.inboundNodes.length;++o){let n=Vo.nodeKey(t,o);this.containerNodes.has(n)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),o=[];for(let a of this.layers){let i=a.getClassName(),l=a.getConfig(),u=[];for(let p=0;p<a.inboundNodes.length;p++){let m=a.inboundNodes[p],f=Vo.nodeKey(a,p),d={};if(this.containerNodes.has(f)){if(m.callArgs)try{JSON.stringify(m.callArgs),d=m.callArgs}catch(h){console.warn(`Layer ${a.name} was passed non-serializable keyword arguments: ${m.callArgs}. 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Provided ${t} not understood: ${JSON.stringify(r)}`)}function Mg(r,e){return BH(r,e,"classWeight")}async function Lg(r,e,t,o){if(e!=null||o!=null)throw new Error("Support sampleWeight is not implemented yet");if(t!=null){let n=V(()=>{if(r.shape.length===1)return r.clone();if(r.shape.length===2)if(r.shape[1]>1){let i=1;return r.argMax(i)}else{if(r.shape[1]===1)return r.reshape([r.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${r.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${r.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await n.data());Te(n);let a=[];return s.forEach(i=>{if(t[i]==null)throw new Error(`classWeight must contain all classes in the training data. 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(Expected output keys: ${JSON.stringify(r.outputNames)})`);for(let l=0;l<s.length;l++)x.assert(s[l].shape[0]===i,()=>`Batch size mismatch: input ${r.inputNames[l]} has ${s[l].shape[0]}; expected ${i} based on input ${r.inputNames[0]}.`);for(let l=0;l<a.length;l++)x.assert(a[l].shape[0]===i,()=>`Batch size mismatch: output ${r.outputNames[l]} has ${a[l].shape[0]}; expected ${i} based on input ${r.inputNames[0]}.`);return{xs:s,ys:a}}function e1(r,e,t){if(t instanceof Ve)return[t];if(Array.isArray(t))return x.assert(t.length===e.length,()=>`Received an array of ${t.length} Tensors, but expected ${e.length} to match the ${r} keys ${e}.`),t;{let o=[];for(let n of e){if(t[n]==null)throw new L(`The feature data generated by the dataset lacks the required ${r} key '${n}'.`);o.push(t[n])}return o}}function GH(r){if(r.length===3)throw new Ne("Validation with sample weights is not implemented yet.");return{xs:r[0],ys:r[1]}}async function o1(r,e,t){let o=t.batchesPerEpoch!=null;if(x.assert(r.optimizer!=null,()=>"You must compile a model before training/testing. 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Use validationData instead."),r.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");r.isTraining=!0;try{let n=t.validationData!=null,s,a;if(n)if(r1(t.validationData))x.assert(t.validationBatches==null||t.validationBatches>0&&Number.isInteger(t.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${t.validationBatches}`);else{let g=GH(t.validationData);s=g.xs,a=g.ys}let i=r.makeTrainFunction(),l=r.getDedupedMetricsNames(),u;n?u=l.slice().concat(l.map(g=>"val_"+g)):u=l.slice();let c=Ag(t.callbacks,t.yieldEvery),p=t.verbose==null?1:t.verbose,{callbackList:m,history:f}=Eg(c,p,t.epochs,null,null,WH(e,t),null,n,u);m.setModel(r),r.history=f,await m.onTrainBegin(),r.stopTraining_=!1;let d=t.initialEpoch==null?0:t.initialEpoch,h=await e.iterator();for(;d<t.epochs;){let g={};await m.onEpochBegin(d);let y=0,b=0;for(o||(h=await e.iterator());o?y<t.batchesPerEpoch:!0;){let w=await h.next();if(o&&w.done){console.warn(`You provided \`batchesPerEpoch\` as ${t.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. 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instead`);if(s==="channelsFirst"&&(r=je(r,[0,2,1])),n==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let i=wu(r,e,o,n==="same"?"same":"valid","NWC",a);return t!=null&&(i=ao(i,t)),i})}function b1(r,e,t,o=[1,1],n="valid",s,a,i=null){return V(()=>{if(s==null&&(s=Zr()),$t(s),r.rank!==3&&r.rank!==4)throw new L(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${r.rank}.`);if(e.rank!==3&&e.rank!==4)throw new L(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${r.rank}.`);let l=Rf(r,s);if(n==="causal")throw new Ne("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return l=Gn.conv2d({x:l,filter:e,strides:o,pad:n==="same"?"same":"valid",dilations:a,dataFormat:"NHWC",bias:t,activation:i}),s==="channelsFirst"&&(l=je(l,[0,3,1,2])),l})}function eq(r,e,t,o=[1,1,1],n="valid",s,a){return V(()=>{if(s==null&&(s=Zr()),$t(s),r.rank!==4&&r.rank!==5)throw new L(`conv3dWithBias expects input to be of rank 4 or 5, but received ${r.rank}.`);if(e.rank!==4&&e.rank!==5)throw new L(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${r.rank}.`);let i=$_(r,s);if(n==="causal")throw new Ne("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return i=Sm(i,e,o,n==="same"?"same":"valid","NDHWC",a),t!=null&&(i=ao(i,t)),s==="channelsFirst"&&(i=je(i,[0,4,1,2,3])),i})}var vp=class extends Pe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",vp.verifyArgs(t),this.rank=e,Wt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Ne(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=gl(t.kernelSize,e,"kernelSize"),this.strides=gl(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,Jr(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,$t(this.dataFormat),this.activation=Ps(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=pt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ft(t.biasConstraint),this.biasRegularizer=yt(t.biasRegularizer),this.activityRegularizer=yt(t.activityRegularizer),this.dilationRate=gl(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new L(`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 L(`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 L(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Bo("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!dg(e.kernelSize,"number",1,3))throw new L(`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:Os(this.activation),useBias:this.useBias,biasInitializer:kt(this.biasInitializer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),biasConstraint:Rt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Zu=class extends vp{constructor(e,t){super(e,t);this.kernel=null,Zu.verifyArgs(t),this.filters=t.filters,Wt(this.filters,"filters"),this.kernelInitializer=pt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ft(t.kernelConstraint),this.kernelRegularizer=yt(t.kernelRegularizer)}build(e){e=Ze(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new L(`The channel dimension of the input should be defined. Found ${e[t]}`);let o=e[t],n=this.kernelSize.concat([o,this.filters]);this.kernel=this.addWeight("kernel",n,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]:o}}],this.built=!0}call(e,t){return V(()=>{e=Fe(e);let o,n=this.bias==null?null:this.bias.read(),s=hg(this.activation.getClassName());if(s!=null&&this.rank===2)o=b1(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate,s);else{if(this.rank===1)o=QH(e,this.kernel.read(),n,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)o=b1(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)o=eq(e,this.kernel.read(),n,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Ne("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(o=this.activation.apply(o))}return o})}computeOutputShape(e){e=Ze(e);let t=[],o=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let s=0;s<o.length;++s){let a=po(o[s],this.kernelSize[s],this.padding,this.strides[s],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[s]);t.push(a)}let n=[e[0]];return this.dataFormat==="channelsLast"?(n=n.concat(t),n.push(this.filters)):(n.push(this.filters),n=n.concat(t)),n}getConfig(){let e={filters:this.filters,kernelInitializer:kt(this.kernelInitializer),kernelRegularizer:st(this.kernelRegularizer),kernelConstraint:Rt(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 L(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},xl=class extends Zu{constructor(e){super(2,e);xl.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!dg(e.kernelSize,"number",1,2))throw new L(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};xl.className="Conv2D";J.registerClass(xl);var Ju=class extends Zu{constructor(e){super(3,e);Ju.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 L(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};Ju.className="Conv3D";J.registerClass(Ju);var Ff=class extends xl{constructor(e){super(e);if(this.inputSpec=[new Nt({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new L(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=Ze(e),e.length!==4)throw new L("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new L("The channel dimension of the inputs should be defined. Found `None`.");let o=e[t],n=this.kernelSize.concat([this.filters,o]);this.kernel=this.addWeight("kernel",n,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Nt({ndim:4,axes:{[t]:o}})],this.built=!0}call(e,t){return V(()=>{let o=Fe(e);if(o.shape.length!==4)throw new L(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${o.shape.length}`);let n=o.shape,s=n[0],a,i;this.dataFormat==="channelsFirst"?(a=2,i=3):(a=1,i=2);let l=n[a],u=n[i],c=this.kernelSize[0],p=this.kernelSize[1],m=this.strides[0],f=this.strides[1],d=$f(l,m,c,this.padding),h=$f(u,f,p,this.padding),g=[s,d,h,this.filters];this.dataFormat!=="channelsLast"&&(o=je(o,[0,2,3,1]));let y=_u(o,this.kernel.read(),g,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(y=je(y,[0,3,1,2])),this.bias!=null&&(y=ao(y,this.bias.read(),this.dataFormat)),this.activation!=null&&(y=this.activation.apply(y)),y})}computeOutputShape(e){e=Ze(e);let t=e.slice(),o,n,s;this.dataFormat==="channelsFirst"?(o=1,n=2,s=3):(o=3,n=1,s=2);let a=this.kernelSize[0],i=this.kernelSize[1],l=this.strides[0],u=this.strides[1];return t[o]=this.filters,t[n]=$f(t[n],l,a,this.padding),t[s]=$f(t[s],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Ff.className="Conv2DTranspose";J.registerClass(Ff);var R_=class extends Zu{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 L("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new L("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 L(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=pt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=yt(t.depthwiseRegularizer),this.depthwiseConstraint=Ft(t.depthwiseConstraint),this.pointwiseInitializer=pt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=yt(t.pointwiseRegularizer),this.pointwiseConstraint=Ft(t.pointwiseConstraint)}build(e){if(e=Ze(e),e.length<this.rank+2)throw new L(`Inputs to SeparableConv${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 L(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let o=e[t],n=this.kernelSize.concat([o,this.depthMultiplier]),s=[];for(let i=0;i<this.rank;++i)s.push(1);s.push(o*this.depthMultiplier,this.filters);let a=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",n,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,a,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",s,"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 Nt({ndim:this.rank+2,axes:{[t]:o}})],this.built=!0}call(e,t){return V(()=>{e=Fe(e);let o;if(this.rank===1)throw new Ne("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=je(e,[0,2,3,1])),o=Gm(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(o=ao(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),this.dataFormat==="channelsFirst"&&(o=je(o,[0,3,1,2])),o})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.pointwiseInitializer=kt(this.pointwiseInitializer),e.depthwiseRegularizer=st(this.depthwiseRegularizer),e.pointwiseRegularizer=st(this.pointwiseRegularizer),e.depthwiseConstraint=Rt(this.depthwiseConstraint),e.pointwiseConstraint=Rt(this.pointwiseConstraint),e}};R_.className="SeparableConv";var Of=class extends R_{constructor(e){super(2,e)}};Of.className="SeparableConv2D";J.registerClass(Of);var Qu=class extends Zu{constructor(e){super(1,e);Qu.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"&&!dg(e.kernelSize,"number",1,1))throw new L(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};Qu.className="Conv1D";J.registerClass(Qu);var Pf=class extends Pe{constructor(e){super(e);typeof e.cropping=="number"?this.cropping=[[e.cropping,e.cropping],[e.cropping,e.cropping]]:typeof e.cropping[0]=="number"?this.cropping=[[e.cropping[0],e.cropping[0]],[e.cropping[1],e.cropping[1]]]:this.cropping=e.cropping,this.dataFormat=e.dataFormat===void 0?"channelsLast":e.dataFormat,this.inputSpec=[{ndim:4}]}computeOutputShape(e){return this.dataFormat==="channelsFirst"?[e[0],e[1],e[2]-this.cropping[0][0]-this.cropping[0][1],e[3]-this.cropping[1][0]-this.cropping[1][1]]:[e[0],e[1]-this.cropping[0][0]-this.cropping[0][1],e[2]-this.cropping[1][0]-this.cropping[1][1],e[3]]}call(e,t){return V(()=>{if(e=Fe(e),this.dataFormat==="channelsLast"){let o=lf(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return lf(o,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let o=lf(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return lf(o,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Pf.className="Cropping2D";J.registerClass(Pf);var Mf=class extends Pe{constructor(e){super(e);this.DEFAULT_SIZE=[2,2],this.inputSpec=[{ndim:4}],this.size=e.size==null?this.DEFAULT_SIZE:e.size,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,NT(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],o=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,o]}else{let t=e[1]==null?null:this.size[0]*e[1],o=e[2]==null?null:this.size[1]*e[2];return[e[0],t,o,e[3]]}}call(e,t){return V(()=>{let o=Fe(e),n=o.shape;if(this.dataFormat==="channelsFirst"){o=je(o,[0,2,3,1]);let s=this.size[0]*n[2],a=this.size[1]*n[3],i=this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a]);return je(i,[0,3,1,2])}else{let s=this.size[0]*n[1],a=this.size[1]*n[2];return this.interpolation==="nearest"?o.resizeNearestNeighbor([s,a]):o.resizeBilinear([s,a])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Mf.className="UpSampling2D";J.registerClass(Mf);function tq(r,e,t=[1,1],o="valid",n,s){return V(()=>{n==null&&(n=Zr()),$t(n);let a=Rf(r,n);if(r.rank!==4)throw new L(`Input for depthwiseConv2d is required to be 4-D, but is instead ${r.rank}-D`);if(e.rank!==4)throw new L(`depthwiseKernel is required to be 4-D, but is instead ${e.rank}-D`);return a=Cs(a,e,t,o==="same"?"same":"valid","NHWC",s),n==="channelsFirst"&&(a=je(a,[0,3,1,2])),a})}var Lf=class extends vp{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=pt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ft(e.depthwiseConstraint),this.depthwiseRegularizer=yt(e.depthwiseRegularizer)}build(e){if(e=Ze(e),e.length<4)throw new L(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new L(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let o=e[t],n=[this.kernelSize[0],this.kernelSize[1],o,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",n,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[o*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Fe(e);let o=tq(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(o=ao(o,this.bias.read(),this.dataFormat)),this.activation!=null&&(o=this.activation.apply(o)),o})}computeOutputShape(e){e=Ze(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,s=po(t,this.kernelSize[0],this.padding,this.strides[0]),a=po(o,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],n,s,a]:[e[0],s,a,n]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=kt(this.depthwiseInitializer),e.depthwiseRegularizer=st(this.depthwiseRegularizer),e.depthwiseConstraint=Rt(this.depthwiseRegularizer),e}};Lf.className="DepthwiseConv2D";J.registerClass(Lf);function F_(r,e,t,o){if(Array.isArray(r)){if(e!=null||t!=null)throw new L("When inputs is an array, neither initialState or constants should be provided");o!=null&&(t=r.slice(r.length-o,r.length),r=r.slice(0,r.length-o)),r.length>1&&(e=r.slice(1,r.length)),r=r[0]}function n(s){return s==null||Array.isArray(s)?s:[s]}return e=n(e),t=n(t),{inputs:r,initialState:e,constants:t}}function O_(r,e,t,o=!1,n,s,a=!1,i=!1){return V(()=>{let l=e.shape.length;if(l<3)throw new L(`Input should be at least 3D, but is ${l}D.`);let u=[1,0].concat(Mr(2,l));if(e=je(e,u),s!=null)throw new Ne("The rnn() functoin of the deeplearn.js backend does not support constants yet.");a&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),n!=null&&(n=n.asType("bool").asType("float32"),n.rank===l-1&&(n=ir(n,-1)),n=je(n,u)),o&&(e=Ht(e,0),n!=null&&(n=Ht(n,0)));let c=[],p,m=t,f=e.shape[0],d=cr(e),h;n!=null&&(h=cr(n));for(let y=0;y<f;++y){let b=d[y],w=V(()=>r(b,m));if(n==null)p=w[0],m=w[1];else{let _=V(()=>{let C=h[y],D=er(C).sub(C),T=w[0].mul(C).add(m[0].mul(D)),R=m.map((P,B)=>w[1][B].mul(C).add(P.mul(D)));return{output:T,newStates:R}});p=_.output,m=_.newStates}i&&c.push(p)}let g;return i&&(g=Bt(c,1)),[p,g,m]})}var mo=class extends Pe{constructor(e){super(e);let t;if(e.cell==null)throw new L("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new Cp({cells:e.cell}):t=e.cell,t.stateSize==null)throw new L("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 Nt({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 Mr(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){vg(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let o=t[0],n;if(this.returnSequences?n=[e[0],e[1],o]:n=[e[0],o],this.returnState){let s=[];for(let a of t)s.push([e[0],a]);return[n].concat(s)}else return n}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let o=this.returnSequences?t:null;if(this.returnState){let n=this.states.map(s=>null);return[o].concat(n)}else return o})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let o=0;o<e;++o)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 Ne("Constants support is not implemented in RNN yet.");vg(e)&&(e=e[0]),e=e;let o=this.stateful?e[0]:null,n=e.slice(2);this.inputSpec[0]=new Nt({shape:[o,null,...n]});let s=[e[0]].concat(e.slice(2));if(t!=null)throw new Ne("Constants support is not implemented in RNN yet.");this.cell.build(s);let a;if(Array.isArray(this.cell.stateSize)?a=this.cell.stateSize:a=[this.cell.stateSize],this.stateSpec!=null){if(!x.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),a))throw new L(`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(i=>new Nt({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Io("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape[0];if(o==null)throw new L("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(n=>ht([o,n])):this.states_=[ht([o,this.cell.stateSize])];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(n=>ht([o,n])):this.states_[0]=ht([o,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new L(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t===!0?this.keptStates.push(this.states_.slice()):Te(this.states_);for(let n=0;n<this.states_.length;++n){let s=e[n],a=Array.isArray(this.cell.stateSize)?this.cell.stateSize[n]:this.cell.stateSize,i=[o,a];if(!x.arraysEqual(s.shape,i))throw new L(`State ${n} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${s.shape}`);this.states_[n]=s}}this.states_=this.states_.map(n=>Et(n.clone()))})}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=F_(e,o,n,this.numConstants);e=s.inputs,o=s.initialState,n=s.constants;let a=[],i=[];if(o!=null){t.initialState=o,a=a.concat(o),this.stateSpec=[];for(let u of o)this.stateSpec.push(new Nt({shape:u.shape}));i=i.concat(this.stateSpec)}if(n!=null&&(t.constants=n,a=a.concat(n),this.numConstants=n.length),a[0]instanceof zr){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;e=Fe(e),s==null&&(this.stateful?s=this.states_:s=this.getInitialState(e));let a=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(s.length!==a)throw new L(`RNN Layer has ${a} state(s) but was passed ${s.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:n},u=O_((d,h)=>{let g=this.cell.call([d].concat(h),i);return[g[0],g.slice(1)]},e,s,this.goBackwards,o,null,this.unroll,this.returnSequences),c=u[0],p=u[1],m=u[2];this.stateful&&this.resetStates(m,n);let f=this.returnSequences?p:c;return this.returnState?[f].concat(m):f})}getInitialState(e){return V(()=>{let t=ht(e.shape);return t=ge(t,[1,2]),t=Oa(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(o=>o>1?yg(t,[1,o]):t):this.cell.stateSize>1?[yg(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 o=this.cell.getConfig();return this.getClassName()===mo.className&&(t.cell={className:this.cell.getClassName(),config:o}),Object.assign({},o,e,t)}static fromConfig(e,t,o={}){let n=t.cell,s=Qr(n,o);return new e(Object.assign(t,{cell:s}))}};mo.className="RNN";J.registerClass(mo);var yl=class extends Pe{},Ip=class extends yl{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,Wt(this.units,"units"),this.activation=Ps(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=Ft(e.kernelConstraint),this.recurrentConstraint=Ft(e.recurrentConstraint),this.biasConstraint=Ft(e.biasConstraint),this.dropout=Uu([1,Rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Uu([1,Rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Ze(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 V(()=>{if(e=e,e.length!==2)throw new L(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let o=e[1];e=e[0];let n=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ma({ones:()=>er(e),rate:this.dropout,training:n})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ma({ones:()=>er(o),rate:this.recurrentDropout,training:n}));let s,a=this.dropoutMask,i=this.recurrentDropoutMask;a!=null?s=Xn(O(e,a),this.kernel.read()):s=Xn(e,this.kernel.read()),this.bias!=null&&(s=ao(s,this.bias.read())),i!=null&&(o=O(o,i));let l=Q(s,Xn(o,this.recurrentKernel.read()));return this.activation!=null&&(l=this.activation.apply(l)),[l,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Rt(this.kernelConstraint),recurrentConstraint:Rt(this.recurrentConstraint),biasConstraint:Rt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};Ip.className="SimpleRNNCell";J.registerClass(Ip);var zf=class extends mo{constructor(e){e.cell=new Ip(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return new e(t)}};zf.className="SimpleRNN";J.registerClass(zf);var Np=class extends yl{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 L("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Wt(this.units,"units"),this.activation=Ps(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ps(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=Ft(e.kernelConstraint),this.recurrentConstraint=Ft(e.recurrentConstraint),this.biasConstraint=Ft(e.biasConstraint),this.dropout=Uu([1,Rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Uu([1,Rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=Ze(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 V(()=>{if(e=e,e.length!==2)throw new L(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training==null?!1:t.training,n=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ma({ones:()=>er(e),rate:this.dropout,training:o,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ma({ones:()=>er(n),rate:this.recurrentDropout,training:o,count:3}));let s=this.dropoutMask,a=this.recurrentDropoutMask,i,l,u;0<this.dropout&&this.dropout<1&&(e=O(e,s[0]));let c=Xn(e,this.kernel.read());this.useBias&&(c=ao(c,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(n=O(n,a[0]));let p=this.recurrentKernel.read(),[m,f]=ur(p,[2*this.units,this.units],p.rank-1),d=Xn(n,m),[h,g,y]=ur(c,3,c.rank-1),[b,w]=ur(d,2,d.rank-1);i=this.recurrentActivation.apply(Q(h,b)),l=this.recurrentActivation.apply(Q(g,w));let _=Xn(O(l,n),f);u=this.activation.apply(Q(y,_));let C=Q(O(i,n),O(Q(1,He(i)),u));return[C,C]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),recurrentActivation:Os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Rt(this.kernelConstraint),recurrentConstraint:Rt(this.recurrentConstraint),biasConstraint:Rt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};Np.className="GRUCell";J.registerClass(Np);var Bf=class extends mo{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 Np(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Bf.className="GRU";J.registerClass(Bf);var bl=class extends yl{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,Wt(this.units,"units"),this.activation=Ps(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=Ps(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=pt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=yt(e.kernelRegularizer),this.recurrentRegularizer=yt(e.recurrentRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.kernelConstraint=Ft(e.kernelConstraint),this.recurrentConstraint=Ft(e.recurrentConstraint),this.biasConstraint=Ft(e.biasConstraint),this.dropout=Uu([1,Rs([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Uu([1,Rs([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=[this.units,this.units],this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){var t;e=Ze(e);let o=e[e.length-1];this.kernel=this.addWeight("kernel",[o,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 n;if(this.useBias){if(this.unitForgetBias){let s=this.biasInitializer,a=this.units;n=new(t=class extends lo{apply(l,u){let c=s.apply([a]),p=new Hu().apply([a]),m=s.apply([a*2]);return s_(s_(c,p),m)}},t.className="CustomInit",t)}else n=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,n,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let o=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new L(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=e[1],s=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ma({ones:()=>er(e),rate:this.dropout,training:o,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ma({ones:()=>er(n),rate:this.recurrentDropout,training:o,count:4}));let a=this.dropoutMask,i=this.recurrentDropoutMask,l,u,c,p;0<this.dropout&&this.dropout<1&&(e=O(e,a[0]));let m=Xn(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(n=O(n,i[0])),m=Q(m,Xn(n,this.recurrentKernel.read())),this.useBias&&(m=ao(m,this.bias.read()));let[f,d,h,g]=ur(m,4,m.rank-1);l=this.recurrentActivation.apply(f),u=this.recurrentActivation.apply(d),c=Q(O(u,s),O(l,this.activation.apply(h))),p=this.recurrentActivation.apply(g);let y=O(p,this.activation.apply(c));return[y,y,c]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:Os(this.activation),recurrentActivation:Os(this.recurrentActivation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),recurrentInitializer:kt(this.recurrentInitializer),biasInitializer:kt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:st(this.kernelRegularizer),recurrentRegularizer:st(this.recurrentRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Rt(this.kernelConstraint),recurrentConstraint:Rt(this.recurrentConstraint),biasConstraint:Rt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};bl.className="LSTMCell";J.registerClass(bl);var Vf=class extends mo{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 bl(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};Vf.className="LSTM";J.registerClass(Vf);var Cp=class extends yl{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 V(()=>{e=e;let o=e.slice(1),n=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?n.push(o.splice(0,i.stateSize.length)):n.push(o.splice(0,1));n.reverse();let s=[],a;for(let i=0;i<this.cells.length;++i){let l=this.cells[i];o=n[i],i===0?a=[e[0]].concat(o):a=[a[0]].concat(o),a=l.call(a,t),s.push(a.slice(1))}o=[];for(let i of s.slice().reverse())o.push(...i);return[a[0]].concat(o)})}build(e){vg(e)&&(e=e[0]),e=e;let t;this.cells.forEach((o,n)=>{$s(`RNNCell_${n}`,()=>{o.build(e),Array.isArray(o.stateSize)?t=o.stateSize[0]:t=o.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=s=>({className:s.getClassName(),config:s.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,o={}){let n=[];for(let s of t.cells)n.push(Qr(s,o));return new e({cells:n})}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 o of this.cells)t.push(...o.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return xf(e)}setWeights(e){let t=[];for(let o of this.cells){let n=o.weights.length,s=e.splice(n);for(let a=0;a<o.weights.length;++a)t.push([o.weights[a],s[a]])}yp(t)}};Cp.className="StackedRNNCells";J.registerClass(Cp);function Ma(r){let{ones:e,rate:t,training:o=!1,count:n=1}=r,s=()=>wg(e(),t),a=()=>ul(s,e,o);return!n||n<=1?Et(a().clone()):Array(n).fill(void 0).map(a).map(l=>Et(l.clone()))}var rq=function(r,e){var t={};for(var o in r)Object.prototype.hasOwnProperty.call(r,o)&&e.indexOf(o)<0&&(t[o]=r[o]);if(r!=null&&typeof Object.getOwnPropertySymbols=="function")for(var n=0,o=Object.getOwnPropertySymbols(r);n<o.length;n++)e.indexOf(o[n])<0&&Object.prototype.propertyIsEnumerable.call(r,o[n])&&(t[o[n]]=r[o[n]]);return t};var P_=class extends mo{constructor(e){if(e.unroll)throw new Ne("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Ne("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Nt({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(Te(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Te(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new L("ConvRNN2D cell does not support constants");let o=t==null?null:t.mask,n=t==null?null:t.training,s=t==null?null:t.initialState;return super.call(e,{mask:o,training:n,initialState:s})})}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 V(()=>{let{stateSize:t}=this.cell,o=e.shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)],a=ht(s);return Array.isArray(t)?Array(t.length).fill(a):[a]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new Io("Cannot call resetStates() on an RNN Layer that is not stateful.");let o=this.inputSpec[0].shape,n=this.computeSingleOutputShape(o),s=[n[0],...n.slice(2)];if(o[0]==null)throw new L("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(()=>ht(s)):this.states_=[ht(s)];else if(e==null)Te(this.states_),this.keptStates!=null&&(Te(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>ht(s)):this.states_[0]=ht(s);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new L(`Layer ${this.name} expects ${this.states_.length} state(s), but it received ${e.length} state value(s). Input received: ${e}`);t?this.keptStates.push(this.states_.slice()):Te(this.states_);for(let i=0;i<this.states_.length;++i){let l=e[i],u=s;if(!x.arraysEqual(l.shape,u))throw new L(`State ${i} is incompatible with layer ${this.name}: expected shape=${u}, received shape=${l.shape}`);this.states_[i]=l}}this.states_=this.states_.map(i=>Et(i.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:o,kernelSize:n,padding:s,strides:a,dilationRate:i}=this.cell,l=t==="channelsFirst",u=e[l?3:2],c=e[l?4:3],p=po(u,n[0],s,a[0],i[0]),m=po(c,n[1],s,a[1],i[1]);return[...e.slice(0,2),...l?[o,p,m]:[p,m,o]]}};P_.className="ConvRNN2D";var Sp=class extends bl{constructor(e){let{filters:t,kernelSize:o,strides:n,padding:s,dataFormat:a,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Wt(this.filters,"filters"),this.kernelSize=gl(o,2,"kernelSize"),this.kernelSize.forEach(l=>Wt(l,"kernelSize")),this.strides=gl(n||1,2,"strides"),this.strides.forEach(l=>Wt(l,"strides")),this.padding=s||"valid",Jr(this.padding),this.dataFormat=a||"channelsLast",$t(this.dataFormat),this.dilationRate=gl(i||1,2,"dilationRate"),this.dilationRate.forEach(l=>Wt(l,"dilationRate"))}build(e){var t;e=Ze(e);let o=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[o]==null)throw new L(`The channel dimension of the input should be defined. Found ${e[o]}`);let n=e[o],s=4,a=this.kernelSize.concat([n,this.filters*s]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*s]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let l;if(this.unitForgetBias){let u=this.biasInitializer,c=this.filters;l=new(t=class extends lo{apply(m,f){let d=u.apply([c]),h=Ir([c]),g=u.apply([c*2]);return lp([d,h,g])}},t.className="CustomInit",t)}else l=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*s],null,l,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new L(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let o=t.training||!1,n=e[0],s=e[1],a=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=Ma({ones:()=>er(n),rate:this.dropout,training:o,count:i}));let l=this.dropoutMask,u=(te,ie,le)=>!ie||!ie[le]?te:O(ie[le],te),c=u(n,l,0),p=u(n,l,1),m=u(n,l,2),f=u(n,l,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=Ma({ones:()=>er(s),rate:this.recurrentDropout,training:o,count:i}));let d=this.recurrentDropoutMask,h=u(s,d,0),g=u(s,d,1),y=u(s,d,2),b=u(s,d,3),w=3,[_,C,D,T]=ur(this.kernel.read(),i,w),[R,P,B,G]=this.useBias?ur(this.bias.read(),i):[null,null,null,null];c=this.inputConv(c,_,R,this.padding),p=this.inputConv(p,C,P,this.padding),m=this.inputConv(m,D,B,this.padding),f=this.inputConv(f,T,G,this.padding);let[U,j,H,K]=ur(this.recurrentKernel.read(),i,w);h=this.recurrentConv(h,U),g=this.recurrentConv(g,j),y=this.recurrentConv(y,H),b=this.recurrentConv(b,K);let X=this.recurrentActivation.apply(Q(c,h)),oe=this.recurrentActivation.apply(Q(p,g)),Y=Q(O(oe,a),O(X,this.activation.apply(Q(m,y)))),re=O(this.recurrentActivation.apply(Q(f,b)),this.activation.apply(Y));return[re,re,Y]})}getConfig(){let e=super.getConfig(),{units:t}=e,o=rq(e,["units"]),n={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},o,n)}inputConv(e,t,o,n){let s=Kr(e,t,this.strides,n||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return o?ao(s,o,this.dataFormat):s}recurrentConv(e,t){return Kr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};Sp.className="ConvLSTM2DCell";J.registerClass(Sp);var Gf=class extends P_{constructor(e){let t=new Sp(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};Gf.className="ConvLSTM2D";J.registerClass(Gf);var Tp=class extends Pe{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,o=[];for(let n=0;n<this.noiseShape.length;++n)o.push(this.noiseShape[n]==null?t[n]:this.noiseShape[n]);return o}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);if(0<this.rate&&this.rate<1){let n=t.training==null?!1:t.training,s=this.getNoiseShape(o);return ul(()=>wg(o,this.rate,s,this.seed),()=>o,n)}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()}};Tp.className="Dropout";J.registerClass(Tp);var Wf=class extends Tp{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};Wf.className="SpatialDropout1D";J.registerClass(Wf);var Uf=class extends Pe{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,Wt(this.units,"units"),this.activation=Ps(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=pt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=pt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ft(e.kernelConstraint),this.biasConstraint=Ft(e.biasConstraint),this.kernelRegularizer=yt(e.kernelRegularizer),this.biasRegularizer=yt(e.biasRegularizer),this.activityRegularizer=yt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=Ze(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=Ze(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e),n=hg(this.activation.getClassName()),s;return n!=null?s=Xn(o,this.kernel.read(),n,this.bias?this.bias.read():null):(s=Xn(o,this.kernel.read()),this.bias!=null&&(s=ao(s,this.bias.read())),this.activation!=null&&(s=this.activation.apply(s))),s})}getConfig(){let e={units:this.units,activation:Os(this.activation),useBias:this.useBias,kernelInitializer:kt(this.kernelInitializer),biasInitializer:kt(this.biasInitializer),kernelRegularizer:st(this.kernelRegularizer),biasRegularizer:st(this.biasRegularizer),activityRegularizer:st(this.activityRegularizer),kernelConstraint:Rt(this.kernelConstraint),biasConstraint:Rt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Uf.className="Dense";J.registerClass(Uf);var jf=class extends Pe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=Ze(e);for(let t of e.slice(1))if(t==null)throw new L(`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],Kn(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);if(this.dataFormat==="channelsFirst"&&o.rank>1){let n=[0];for(let s=2;s<o.rank;++s)n.push(s);n.push(1),o=o.transpose(n)}return RT(o)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};jf.className="Flatten";J.registerClass(jf);var Hf=class extends Pe{constructor(e){super(e);this.supportsMasking=!0,this.activation=Ps(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);return this.activation.apply(o)})}getConfig(){let e={activation:Os(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};Hf.className="Activation";J.registerClass(Hf);var qf=class extends Pe{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 V(()=>(e=Fe(e),DT(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};qf.className="RepeatVector";J.registerClass(qf);var Kf=class extends Pe{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 o="Total size of new array must be unchanged.",n=t.slice(),s=1,a=null;for(let l=0;l<n.length;++l){let u=n[l];if(this.isUnknown(u))if(a===null)a=l;else throw new L("Can only specifiy one unknown dimension.");else s*=u}let i=Kn(e);if(a!==null){if(s===0||i%s!=0)throw new L(o);n[a]=i/s}else if(i!==s)throw new L(o);return n}computeOutputShape(e){let t=!1;for(let o=0;o<e.length;++o)if(this.isUnknown(e[o])){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 V(()=>{this.invokeCallHook(e,t);let o=Fe(e),n=o.shape,s=n.slice(0,1).concat(this.fixUnknownDimension(n.slice(1),this.targetShape));return o.reshape(s)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};Kf.className="Reshape";J.registerClass(Kf);var Xf=class extends Pe{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=Mr(1,e.dims.length+1);if(!x.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 Nt({ndim:this.dims.length+1})]}computeOutputShape(e){e=Ze(e);let t=e.slice();return this.dims.forEach((o,n)=>{t[n+1]=e[o]}),t}call(e,t){return je(Fe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};Xf.className="Permute";J.registerClass(Xf);var Yf=class extends Pe{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 o=Fe(e),n=-1;return ol(Vn(o,this.maskValue),n)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e),n=-1,s=!0,a=ol(Vn(o,this.maskValue),n,s);return o.mul(a.asType(o.dtype))})}};Yf.className="Masking";J.registerClass(Yf);var Zf=class extends Pe{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(xt(e.inputLength))}this.inputDim=e.inputDim,Wt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Wt(this.outputDim,"outputDim"),this.embeddingsInitializer=pt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=yt(e.embeddingsRegularizer),this.activityRegularizer=yt(e.activityRegularizer),this.embeddingsConstraint=Ft(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 V(()=>this.maskZero?(e=Fe(e),Vn(e,Ce(e))):null)}computeOutputShape(e){if(e=Ze(e),this.inputLength==null)return[...e,this.outputDim];let t=xt(this.inputLength);if(t.length!==e.length-1)throw new L(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let o=0;for(let n=0;n<t.length;++n){let s=t[n],a=e[n+1];if(s!=null&&a!=null&&s!==a)throw new L(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);s==null&&(t[o]=a),o++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let o=Fe(e);return o.dtype!=="int32"&&(o=Fa(o,"int32")),bg(this.embeddings.read(),o.as1D()).reshape(Ze(this.computeOutputShape(o.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:kt(this.embeddingsInitializer),embeddingsRegularizer:st(this.embeddingsRegularizer),activityRegularizer:st(this.activityRegularizer),embeddingsConstraint:Rt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};Zf.className="Embedding";J.registerClass(Zf);var wl=class extends Pe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Ne}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 o=e.slice(0,e.length-t.length);for(let n=0;n<t.length;++n){let s=e[e.length-t.length+n],a=t[n];if(s==null||a==null||s<0||a<0)o.push(null);else if(s===1)o.push(a);else if(a===1)o.push(s);else{if(s!==a)throw new L("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));o.push(s)}}return o}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[Ze(e)]),e=e,e.length<2)throw new L(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let s of e)s!=null&&s[0]!==null&&t.push(s[0]);if(t=qn(t),t.length>1)throw new L(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let o=e[0]==null?null:e[0].slice(1);for(let s=1;s<e.length;++s){let a=e[s]==null?null:e[s].slice(1);o=this.computeElementwiseOpOutputShape(o,a)}let n=e.map(s=>s.length);e.indexOf(null)===-1&&qn(n).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let o=[],n=e.map(s=>s.rank);if(n.indexOf(null)===-1){let s=Rs(n);for(let a of e){let i=a.rank;for(let l=0;l<s-i;++l)a=Oa(a,1);o.push(a)}return this.mergeFunction(o)}else{let s=!1;for(let l of e){let u=l.rank;if(u==null){let c=l.shape,p=c[0],m=c.slice(1).concat([p]),f=l.reshape([p].concat(Kn(c.slice(1))));f=je(f,[1,0]),f=f.reshape(m),o.push(f),s=!0}else if(u>1){let c=Mr(1,u).concat([0]);o.push(je(l,c)),s=!0}else o.push(l)}let a=this.mergeFunction(o),i=a.rank;if(s){if(i==null){let l=a.shape,u=l.length,c=l[u-1],p=[c].concat(l.slice(0,l.length-1));a=je(a.reshape([-1,c]),[1,0]).reshape(p)}else if(i>1){let l=[i-1].concat(Mr(0,i-1));a=je(a,l)}}return a}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let n=1;n<e.length;++n){let s=e[n]==null?null:e[n].slice(1);t=this.computeElementwiseOpOutputShape(t,s)}let o=[];for(let n of e)n!=null&&n[0]!==null&&o.push(n[0]);return o=qn(o),o.length===1?t=o.concat(t):t=[null].concat(t),t}computeMask(e,t){return V(()=>{if(t==null)return null;if(!Array.isArray(t))throw new L("`mask` should be an Array");if(!Array.isArray(e))throw new L("`inputs` should be an Array");if(t.length!==e.length)throw new L(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(n=>n==null))return null;t=t.map(n=>n==null?n:ir(n,0));let o=t[0];for(let n=1;n<t.length-1;++n)o=dr(o,t[n]);return o})}},Jf=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=Q(t,e[o]);return t})}};Jf.className="Add";J.registerClass(Jf);var Qf=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=O(t,e[o]);return t})}};Qf.className="Multiply";J.registerClass(Qf);var ed=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let o=1;o<e.length;++o)t=Q(t,e[o]);return O(1/e.length,t)})}};ed.className="Average";J.registerClass(ed);var td=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=Yr(t,e[o]);return t})}};td.className="Maximum";J.registerClass(td);var rd=class extends wl{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let o=1;o<e.length;++o)t=Ts(t,e[o]);return t})}};rd.className="Minimum";J.registerClass(rd);var od=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 L("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let n of e)if(n!=null){t=!1;break}if(t)return;let o=[];for(let n=0;n<e.length;++n){let s=e[n].slice();s.splice(this.axis,1);let a=!1;for(let i of o)if(x.arraysEqual(i,s)){a=!0;break}a||o.push(s)}if(o.length>1)throw new L("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return V(()=>lp(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new L("A `Concatenate` layer should be called on a list of inputs.");let t=e,o=t[0].slice(),n=this.axis<0?o.length+this.axis:this.axis;for(let s of t.slice(1)){if(o[n]==null||s[n]==null){o[n]=null;break}o[n]+=s[n]}return o}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new L("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new L("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new L(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return V(()=>{let o=!0;if(t.forEach(a=>{if(a!=null){o=!1;return}}),o)return null;let n=[];for(let a=0;a<e.length;++a)t[a]==null?n.push(er(e[a]).asType("bool")):t[a].rank<e[a].rank?n.push(ir(t[a],-1)):n.push(t[a]);let s=Ye(n,this.axis);return xu(s,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};od.className="Concatenate";J.registerClass(od);function nd(r,e){for(;r<0;)r+=e;return r}function oq(r,e,t){if(r.shape.length>3||e.shape.length>3)throw new Ne("batchDot is not implemented for tensors of 4D or higher rank yet");if(x.assert(r.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${r.shape.length}`),x.assert(r.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${e.shape.length}`),typeof t=="number"&&(t=[t,t]),r.dtype==="complex64"||e.dtype==="complex64")throw new Ne("batchDot is not implemented for complex64-type Tensors yet.");let o=r.shape.length,n=e.shape.length;t==null&&(t=[o-1,n-2]);let s=t;return V(()=>{let a;if(o>n){a=o-n;let l=[];for(let u=0;u<a;++u)l.push(1);e=e.reshape(e.shape.concat(l))}else if(n>o){a=n-o;let l=[];for(let u=0;u<a;++u)l.push(1);r=r.reshape(r.shape.concat(l))}else a=0;let i;if(r.shape.length===2&&e.shape.length===2)s[0]===s[1]?i=r.mul(e).sum(s[0]):i=r.transpose([1,0]).mul(e).sum(s[1]);else{let l=s[0]!==r.shape.length-1,u=s[1]===e.shape.length-1;i=r.matMul(e,l,u)}if(a>0){let l;o>n?l=o+n-3:l=o-1;let u=[];for(let c=l;c<l+a;++c)u.push(c);i=i.squeeze(u)}return i.shape.length===1&&(i=i.expandDims(1)),i})}var sd=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){x.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],o=e[1];if(t.length>3||o.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);if(t[n[0]]!==o[n[1]])throw new L(`Dimension incompatibility: ${t[n[0]]} !== ${o[n[1]]}`)}mergeFunction(e){if(e.length!==2)throw new L(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],o=e[1],n;return Array.isArray(this.axes)?n=this.axes.map((s,a)=>nd(s,e[a].shape.length)):n=[nd(this.axes,t.shape.length),nd(this.axes,o.shape.length)],this.normalize&&(t=yf(t,n[0]),o=yf(o,n[1])),oq(t,o,n)}interpretAxes(e,t){let o;return Array.isArray(this.axes)?o=this.axes:o=[nd(this.axes,e.length),nd(this.axes,t.length)],o}computeOutputShape(e){x.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(),o=e[1].slice();if(t.length>3||o.length>3)throw new Ne("Dot layer does not support tensors of 4D or higher rank yet.");let n=this.interpretAxes(t,o);t.splice(n[0],1),o.splice(n[1],1),o.splice(0,1);let s=t.concat(o);return s.length===1&&s.push(1),s}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};sd.className="Dot";J.registerClass(sd);var id=class extends Pe{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 V(()=>{this.invokeCallHook(e,t);let o=Fe(e);return ul(()=>up(o.shape,0,this.stddev).add(o),()=>o,t.training||!1)})}};id.className="GaussianNoise";J.registerClass(id);var ad=class extends Pe{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 V(()=>{this.invokeCallHook(e,t);let o=Fe(e);return this.rate>0&&this.rate<1?ul(()=>{let s=Math.sqrt(this.rate/(1-this.rate));return o.mul(up(o.shape,1,s))},()=>o,t.training||!1):o})}};ad.className="GaussianDropout";J.registerClass(ad);var ld=class extends Pe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Fe(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 V(()=>{if(this.rate<1&&this.rate>0){let o=this._getNoiseShape(e);return ul(()=>{let s=Fe(e),a=1.6732632423543772,i=1.0507009873554805,l=-a*i,u=io(As(o),this.rate);u=Fa(u,"float32");let c=((1-this.rate)*(1+this.rate*l**2))**-.5,p=-c*l*this.rate;return s.mul(u).add(u.add(-1).mul(l)).mul(c).add(p)},()=>Fe(e),t.training||!1)}return e})}};ld.className="AlphaDropout";J.registerClass(ld);function ud(r,e,t,o,n,s=.001){let a;if(r.rank===2)a=nw(r,e,t,o,n,s);else if(r.rank===3)a=sw(r,e,t,o,n,s);else if(r.rank===4)a=iw(r,e,t,o,n,s);else throw new Ne(`batchNormalization is not implemented for array of rank ${r.rank} yet`);return a}function nq(r,e,t,o,n=.001){return V(()=>{let s=qc(r,o),a=s.mean,i=s.variance;return[ud(r,a,i,t,e,n),a,i]})}function sq(r,e,t,o,n=.001){return V(()=>{let s=qc(r,o),a=s.mean,i=s.variance,l=[];for(let d of Mr(0,r.rank))o.indexOf(d)!==-1?l.push(1):l.push(r.shape[d]);let u=a.reshape(l),c=i.reshape(l),p=e==null?null:e.reshape(l),m=t==null?null:t.reshape(l);return[ud(r,u,c,m,p,n),a,i]})}function iq(r,e,t,o,n=.001){return x.arraysEqual(o.slice().sort(),Mr(0,r.rank-1))?nq(r,e,t,o,n):sq(r,e,t,o,n)}var cd=class extends Pe{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=pt(e.betaInitializer||"zeros"),this.gammaInitializer=pt(e.gammaInitializer||"ones"),this.movingMeanInitializer=pt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=pt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ft(e.betaConstraint),this.gammaConstraint=Ft(e.gammaConstraint),this.betaRegularizer=yt(e.betaRegularizer),this.gammaRegularizer=yt(e.gammaRegularizer)}build(e){e=Ze(e);let t=this.axis>=0?this.axis:this.axis+e.length,o=e[t];if(o==null)throw new L(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Nt({ndim:e.length,axes:{[t]:o}})];let n=[o];this.scale&&(this.gamma=this.addWeight("gamma",n,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",n,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",n,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",n,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let o=t.training==null?!1:t.training,n=Fe(e),s=n.shape,a=s.length,i=Mr(0,a),l=this.axis>=0?this.axis:this.axis+a;i.splice(l,1);let u=Un(1,a);u[l]=s[l];let c=i.slice();c.sort();let p=!x.arraysEqual(c,Mr(0,a).slice(0,a-1)),m=()=>{if(p){let b=this.movingMean.read().reshape(u),w=this.movingVariance.read().reshape(u),_=this.center?this.beta.read().reshape(u):null,C=this.scale?this.gamma.read().reshape(u):null;return ud(n,b,w,_,C,this.epsilon)}else return ud(n,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!o)return m();let[f,d,h]=iq(n,this.gamma.read(),this.beta.read(),i,this.epsilon),g=(b,w,_)=>{V(()=>{let C=1-_,D=b.read(),T=D.sub(w).mul(C);b.write(D.sub(T))})};return(()=>{g(this.movingMean,d,this.momentum),g(this.movingVariance,h,this.momentum)})(),f})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),movingMeanInitializer:kt(this.movingMeanInitializer),movingVarianceInitializer:kt(this.movingVarianceInitializer),betaRegularizer:st(this.betaRegularizer),gammaRegularizer:st(this.gammaRegularizer),betaConstraint:Rt(this.betaConstraint),gammaConstraint:Rt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};cd.className="BatchNormalization";J.registerClass(cd);var pd=class extends Pe{constructor(e){if(e==null&&(e={}),super(e),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=pt(e.betaInitializer||"zeros"),this.gammaInitializer=pt(e.gammaInitializer||"ones"),this.betaRegularizer=yt(e.betaRegularizer),this.gammaRegularizer=yt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=Ze(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let s=0;s<this.axis.length;++s)this.axis[s]<0&&(this.axis[s]+=t);for(let s of this.axis)if(s<0||s>=t)throw new Error(`Invalid axis: ${s}`);if(this.axis.length!==qn(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let o=this.axis.map(s=>e[s]),n=!0;this.scale?this.gamma=this.addWeight("gamma",o,"float32",this.gammaInitializer,this.gammaRegularizer,n):this.gamma=null,this.center?this.beta=this.addWeight("beta",o,"float32",this.betaInitializer,this.betaRegularizer,n):this.beta=null,this.built=!0}call(e,t){let o=Fe(e),n=o.shape,s=n.length;return V(()=>{let a=!0,{mean:i,variance:l}=qc(o,this.axis,a),u=Un(1,s);for(let h of this.axis)u[h]=n[h];let c=h=>h!=null&&h.shape.length!==s&&this.axis!==[s-1]?h.reshape(u):h,p=c(this.gamma.read()),m=c(this.beta.read()),f=[],d=[];for(let h=0;h<s;++h)this.axis.indexOf(h)!==-1?(f.push(n[h]),d.push(1)):(f.push(1),d.push(n[h]));return i=i.tile(f),l=l.tile(f),p=p.tile(d),m=m.tile(d),ud(o,i,l,m,p,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:kt(this.betaInitializer),gammaInitializer:kt(this.gammaInitializer),betaRegularizer:st(this.betaRegularizer),gammaRegularizer:st(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};pd.className="LayerNormalization";J.registerClass(pd);function aq(r,e,t){return V(()=>{if(r.rank!==4)throw new L(`temporalPadding expects input tensor to be 4-D, but received a ${r.rank}-D tensor.`);if(e==null&&(e=[[1,1],[1,1]]),e.length!==2||e[0].length!==2||e[1].length!==2)throw new L("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(t==null&&(t=Zr()),t!=="channelsLast"&&t!=="channelsFirst")throw new L(`Unknown data format: ${t}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let o;return t==="channelsFirst"?o=[[0,0],[0,0],e[0],e[1]]:o=[[0,0],e[0],e[1],[0,0]],Rr(r,o)})}var md=class extends Pe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Zr():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new L(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,o;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],o=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new L(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new L(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);o=e.padding[1]}this.padding=[t,o]}this.inputSpec=[new Nt({ndim:4})]}computeOutputShape(e){e=Ze(e);let t,o;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?o=e[3]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],e[1],t,o]):(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?o=e[2]+this.padding[1][0]+this.padding[1][1]:o=null,[e[0],t,o,e[3]])}call(e,t){return V(()=>aq(Fe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};md.className="ZeroPadding2D";J.registerClass(md);function Wg(r,e,t,o,n,s){return V(()=>{$t(n),o_(s),Jr(o),t==null&&(t=[1,1]),o==null&&(o="valid"),n==null&&(n=Zr()),s==null&&(s="max"),r=Rf(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Na(r,e,t,i):a=wa(r,e,t,i),n==="channelsFirst"&&(a=je(a,[0,3,1,2])),a})}function w1(r,e,t,o,n,s){return V(()=>{$t(n),o_(s),Jr(o),t==null&&(t=[1,1,1]),o==null&&(o="valid"),n==null&&(n=Zr()),s==null&&(s="max"),r=$_(r,n);let a,i=o==="same"?"same":"valid";return s==="max"?a=Pm(r,e,t,i):a=Im(r,e,t,i),n==="channelsFirst"&&(a=je(a,[0,4,1,2,3])),a})}var M_=class extends Pe{constructor(e){if(e.poolSize==null&&(e.poolSize=2),super(e),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 L(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Wt(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 L(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,Jr(this.padding),this.inputSpec=[new Nt({ndim:3})]}computeOutputShape(e){e=Ze(e);let t=po(e[1],this.poolSize[0],this.padding,this.strides[0]);return[e[0],t,e[2]]}call(e,t){return V(()=>{this.invokeCallHook(e,t),e=Oa(Fe(e),2);let o=this.poolingFunction(Fe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Co(o,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},fd=class extends M_{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Jr(n),Wg(e,t,o,n,s,"max")}};fd.className="MaxPooling1D";J.registerClass(fd);var dd=class extends M_{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Jr(n),Wg(e,t,o,n,s,"avg")}};dd.className="AveragePooling1D";J.registerClass(dd);var L_=class extends Pe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2]),super(e),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 L(`If the strides property of a 2D pooling layer is an Array, it is expected to have a length of 2, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides];Wt(this.poolSize,"poolSize"),Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),Jr(this.padding),this.inputSpec=[new Nt({ndim:4})]}computeOutputShape(e){e=Ze(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=po(t,this.poolSize[0],this.padding,this.strides[0]),o=po(o,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o]:[e[0],t,o,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Fe(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}},hd=class extends L_{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Jr(n),Wg(e,t,o,n,s,"max")}};hd.className="MaxPooling2D";J.registerClass(hd);var gd=class extends L_{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Jr(n),Wg(e,t,o,n,s,"avg")}};gd.className="AveragePooling2D";J.registerClass(gd);var z_=class extends Pe{constructor(e){if(e.poolSize==null&&(e.poolSize=[2,2,2]),super(e),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 L(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Wt(this.poolSize,"poolSize"),Wt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),Jr(this.padding),this.inputSpec=[new Nt({ndim:5})]}computeOutputShape(e){e=Ze(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],o=this.dataFormat==="channelsFirst"?e[3]:e[2],n=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=po(t,this.poolSize[0],this.padding,this.strides[0]),o=po(o,this.poolSize[1],this.padding,this.strides[1]),n=po(n,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,o,n]:[e[0],t,o,n,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Fe(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}},xd=class extends z_{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Jr(n),w1(e,t,o,n,s,"max")}};xd.className="MaxPooling3D";J.registerClass(xd);var yd=class extends z_{constructor(e){super(e)}poolingFunction(e,t,o,n,s){return $t(s),Jr(n),w1(e,t,o,n,s,"avg")}};yd.className="AveragePooling3D";J.registerClass(yd);var B_=class extends Pe{constructor(e){super(e);this.inputSpec=[new Nt({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Ne}},bd=class extends B_{constructor(e){super(e||{})}call(e,t){return V(()=>{let o=Fe(e);return dt(o,1)})}};bd.className="GlobalAveragePooling1D";J.registerClass(bd);var wd=class extends B_{constructor(e){super(e||{})}call(e,t){return V(()=>{let o=Fe(e);return lr(o,1)})}};wd.className="GlobalMaxPooling1D";J.registerClass(wd);var V_=class extends Pe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,$t(this.dataFormat),this.inputSpec=[new Nt({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Ne}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},_d=class extends V_{call(e,t){return V(()=>{let o=Fe(e);return this.dataFormat==="channelsLast"?dt(o,[1,2]):dt(o,[2,3])})}};_d.className="GlobalAveragePooling2D";J.registerClass(_d);var kd=class extends V_{call(e,t){return V(()=>{let o=Fe(e);return this.dataFormat==="channelsLast"?lr(o,[1,2]):lr(o,[2,3])})}};kd.className="GlobalMaxPooling2D";J.registerClass(kd);var G_=class extends Pe{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,o={}){let n=t.layer,s=Qr(n,o);delete t.layer;let a={layer:s};return Object.assign(a,t),new e(a)}},vd=class extends G_{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=Ze(e),e.length<3)throw new L(`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=Ze(e);let t=[e[0]].concat(e.slice(2)),o=this.layer.computeOutputShape(t),n=e[1];return[o[0],n].concat(o.slice(1))}call(e,t){return V(()=>(e=Fe(e),O_((a,i)=>[Fe(this.layer.call(a,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};vd.className="TimeDistributed";J.registerClass(vd);function lq(r){zi(IT,"BidirectionalMergeMode",r)}var uq="concat",Cd=class extends G_{constructor(e){super(e);let t=e.layer.getConfig(),o={};o.className=e.layer.getClassName(),o.config=t,this.forwardLayer=Qr(o),t.goBackwards=t.goBackwards!==!0;let n={};if(n.className=e.layer.getClassName(),n.config=t,this.backwardLayer=Qr(n),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?uq:e.mergeMode,lq(this.mergeMode),e.weights)throw new Ne("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,o=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,o)),this.backwardLayer.setWeights(e.slice(o))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let o,n,s;return this.returnState&&(s=t.slice(1)),o=t[0],o=o,this.mergeMode==="concat"?(o[o.length-1]*=2,n=[o]):this.mergeMode==null?n=[o,o.slice()]:n=[o],this.returnState?this.mergeMode==null?n.concat(s).concat(s.slice()):[o].concat(s).concat(s.slice()):hr(n)}apply(e,t){let o=t==null?null:t.initialState,n=t==null?null:t.constants;t==null&&(t={});let s=F_(e,o,n,this.numConstants);if(e=s.inputs,o=s.initialState,n=s.constants,Array.isArray(e)&&(o=e.slice(1),e=e[0]),(o==null||o.length===0)&&n==null)return super.apply(e,t);let a=[],i=[];if(o!=null){let u=o.length;if(u%2>0)throw new L("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=o,a.push(...o);let c=o.map(p=>new Nt({shape:p.shape}));this.forwardLayer.stateSpec=c.slice(0,u/2),this.backwardLayer.stateSpec=c.slice(u/2),i.push(...c)}if(n!=null)throw new Ne("Support for constants in Bidirectional layers is not implemented yet.");let l=a[0]instanceof zr;for(let u of a)if(u instanceof zr!==l)throw new L("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(l){let u=[e].concat(a),c=this.inputSpec.concat(i),p=this.inputSpec;this.inputSpec=c;let m=super.apply(u,t);return this.inputSpec=p,m}else return super.apply(e,t)}call(e,t){return V(()=>{let o=t.initialState,n,s;if(o==null)n=this.forwardLayer.call(e,t),s=this.backwardLayer.call(e,t);else{let l=o.slice(0,o.length/2),u=o.slice(o.length/2);n=this.forwardLayer.call(e,Object.assign(t,{initialState:l})),s=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let a;this.returnState&&(Array.isArray(n)&&(a=n.slice(1).concat(s.slice(1))),n=n[0],s=s[0]),this.returnSequences&&(s=Ht(s,1));let i;return this.mergeMode==="concat"?i=lp([n,s]):this.mergeMode==="sum"?i=Q(n,s):this.mergeMode==="ave"?i=O(.5,Q(n,s)):this.mergeMode==="mul"?i=O(n,s):this.mergeMode==null&&(i=[n,s]),this.returnState?this.mergeMode==null?i.concat(a):[i].concat(a):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){$s(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),$s(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let o;if(this.returnSequences?this.mergeMode==null?o=[t,t]:o=t:this.mergeMode==null?o=[null,null]:o=null,this.returnState){let s=this.forwardLayer.states.map(a=>null);return Array.isArray(o)?o.concat(s).concat(s):[o].concat(s).concat(s)}else return o}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return 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Missing built-in atob() or Buffer()")}function $1(r,e){let t=Array.isArray(r)?String.fromCharCode.apply(null,r):t6(r);return e?t:t.toLowerCase()}function Yg(r,e,t,o=!1){let n=r[e];return n!=null?$1(n.s,o):t}function Zg(r,e,t){let o=r[e];return o?o.b:t}function Jg(r,e,t){let o=r[e]||{},n=o.i!=null?o.i:o.f!=null?o.f:t;return typeof n=="number"?n:parseInt(n,10)}function dk(r){switch(typeof r=="string"&&(r=Yn[r]),r){case Yn.DT_FLOAT:return"float32";case Yn.DT_INT32:case Yn.DT_INT64:case Yn.DT_INT8:case Yn.DT_UINT8:return"int32";case Yn.DT_BOOL:return"bool";case Yn.DT_DOUBLE:return"float32";case Yn.DT_STRING:return"string";default:return null}}function D1(r,e,t){let o=r[e];return o&&o.func?o.func.name:t}function Qg(r,e,t){let o=r[e];return o&&o.type?dk(o.type):t}function ex(r,e,t){let o=r[e];return o&&o.list&&o.list.type?o.list.type.map(n=>dk(n)):t}function R1(r){if(!r.unknownRank)return r.dim!=null?r.dim.map(e=>typeof e.size=="number"?e.size:parseInt(e.size,10)):[]}function tx(r,e,t){let o=r[e];return o&&o.shape?R1(o.shape):t}function rx(r,e,t){let o=r[e];return o?((o.list.f&&o.list.f.length?o.list.f:o.list.i)||[]).map(n=>typeof n=="number"?n:parseInt(n,10)):t}function ox(r,e,t,o=!1){let n=r[e];return n&&n.list&&n.list.s?n.list.s.map(s=>$1(s,o)):t}function nx(r,e,t){let o=r[e];return o&&o.list&&o.list.shape?o.list.shape.map(n=>R1(n)):t}function sx(r,e,t){let o=r[e];return o&&o.list&&o.list.b?o.list.b:t}var hk=class{constructor(e,t,o){this.node=e,this.tensorMap=t,this.context=o,this.inputs=[],this.attrs={},this.inputs=e.inputNames.map(n=>this.getInput(n)),e.rawAttrs!=null&&(this.attrs=Object.keys(e.rawAttrs).reduce((n,s)=>(n[s]=this.getAttr(s),n),{}))}getInput(e){return gr(e,this.tensorMap,this.context)}getAttr(e,t){let o=this.node.rawAttrs[e];if(o.tensor!=null)return gr(e,this.tensorMap,this.context);if(o.i!=null||o.f!=null)return Jg(this.node.rawAttrs,e,t);if(o.s!=null)return Yg(this.node.rawAttrs,e,t);if(o.b!=null)return Zg(this.node.rawAttrs,e,t);if(o.shape!=null)return tx(this.node.rawAttrs,e,t);if(o.type!=null)return Qg(this.node.rawAttrs,e,t);if(o.list!=null){if(o.list.i!=null||o.list.f!=null)return rx(this.node.rawAttrs,e,t);if(o.list.s!=null)return ox(this.node.rawAttrs,e,t);if(o.list.shape!=null)return nx(this.node.rawAttrs,e,t);if(o.list.b!=null)return sx(this.node.rawAttrs,e,t);if(o.list.type!=null)return ex(this.node.rawAttrs,e,t)}return t}};var F1=(r,e,t)=>{switch(r.op){case"BiasAdd":case"AddV2":case"Add":return[Q(v("a",r,e,t),v("b",r,e,t))];case"AddN":return[ew(v("tensors",r,e,t))];case"FloorMod":case"Mod":return[Lm(v("a",r,e,t),v("b",r,e,t))];case"Mul":return[O(v("a",r,e,t),v("b",r,e,t))];case"RealDiv":case"Div":return[me(v("a",r,e,t),v("b",r,e,t))];case"DivNoNan":return[Em(v("a",r,e,t),v("b",r,e,t))];case"FloorDiv":return[gu(v("a",r,e,t),v("b",r,e,t))];case"Sub":return[ce(v("a",r,e,t),v("b",r,e,t))];case"Minimum":return[Ts(v("a",r,e,t),v("b",r,e,t))];case"Maximum":return[Yr(v("a",r,e,t),v("b",r,e,t))];case"Pow":return[Fr(v("a",r,e,t),v("b",r,e,t))];case"SquaredDifference":return[Mu(v("a",r,e,t),v("b",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var O1=(r,e,t)=>{switch(r.op){case"Abs":case"ComplexAbs":return[It(v("x",r,e,t))];case"Acos":return[xm(v("x",r,e,t))];case"Acosh":return[ym(v("x",r,e,t))];case"Asin":return[wm(v("x",r,e,t))];case"Asinh":return[_m(v("x",r,e,t))];case"Atan":return[km(v("x",r,e,t))];case"Atan2":return[vm(v("x",r,e,t),v("y",r,e,t))];case"Atanh":return[Cm(v("x",r,e,t))];case"Ceil":return[Nm(v("x",r,e,t))];case"Complex":return[ko(v("real",r,e,t),v("imag",r,e,t))];case"Cos":return[ka(v("x",r,e,t))];case"Cosh":return[ku(v("x",r,e,t))];case"Elu":return[Is(v("x",r,e,t))];case"Erf":return[Dm(v("x",r,e,t))];case"Exp":return[Yt(v("x",r,e,t))];case"Expm1":return[$m(v("x",r,e,t))];case"Floor":return[Ns(v("x",r,e,t))];case"Log":return[ar(v("x",r,e,t))];case"Log1p":return[Nu(v("x",r,e,t))];case"Imag":return[Cu(v("x",r,e,t))];case"Neg":return[He(v("x",r,e,t))];case"Reciprocal":return[Bm(v("x",r,e,t))];case"Real":return[il(v("x",r,e,t))];case"Relu":return[Nr(v("x",r,e,t))];case"Round":return[Vm(v("x",r,e,t))];case"Selu":return[Ru(v("x",r,e,t))];case"Sigmoid":return[qr(v("x",r,e,t))];case"Sin":return[Fu(v("x",r,e,t))];case"Sign":return[Wm(v("x",r,e,t))];case"Sinh":return[Ou(v("x",r,e,t))];case"Softplus":return[Ss(v("x",r,e,t))];case"Sqrt":return[gt(v("x",r,e,t))];case"Square":return[Oe(v("x",r,e,t))];case"Tanh":return[Fi(v("x",r,e,t))];case"Tan":return[qm(v("x",r,e,t))];case"ClipByValue":return[sr(v("x",r,e,t),v("clipValueMin",r,e,t),v("clipValueMax",r,e,t))];case"Relu6":return[Du(v("x",r,e,t))];case"Rsqrt":return[$u(gr(r.inputNames[0],e,t))];case"Prod":return[Au(v("x",r,e,t),v("axes",r,e,t))];case"LeakyRelu":return[Ca(v("x",r,e,t),v("alpha",r,e,t))];case"Prelu":return[Ta(v("x",r,e,t),v("alpha",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function So(r,e,t=""){if(!(typeof r=="number"||typeof e=="number")){x.assert(r.length===e.length,()=>t+` Shapes ${r} and ${e} must match`);for(let o=0;o<r.length;o++){let n=r[o],s=e[o];x.assert(n<0||s<0||n===s,()=>t+` Shapes ${r} and ${e} must match`)}}}function P1(r){return!(typeof r=="number"||r.some(e=>e<0))}function Ap(r,e,t){let o=ix(r,t),n=!P1(o);if(n&&e.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${o}`);if(n&&e.forEach(s=>{o=ix(s.shape,o)}),!P1(o))throw new Error(`Non-fully-defined elementShape: ${o}`);return o}function ix(r,e){if(typeof r=="number")return e;if(typeof e=="number")return r;if(r.length!==e.length)throw new Error(`Incompatible ranks during merge: ${r} vs. ${e}`);let t=[];for(let o=0;o<r.length;++o){let n=r[o],s=e[o];if(n>=0&&s>=0&&n!==s)throw new Error(`Incompatible shape during merge: ${r} vs. ${e}`);t[o]=n>=0?n:s}return t}var gk=class{constructor(e,t,o,n,s,a,i){this.name=e,this.dtype=t,this.maxSize=o,this.elementShape=n,this.identicalElementShapes=s,this.dynamicSize=a,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=ue(0),Et(this.idTensor)}get id(){return this.idTensor.id}get closed(){return this.closed_}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.tensor.id))&&t.tensor.dispose()}),this.tensors=[],this.closed_=!0,this.idTensor.dispose()}size(){return this.tensors.length}read(e){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||e>=this.size())throw new Error(`Tried to read from index ${e}, but array size is: ${this.size()}`);let t=this.tensors[e];if(t.cleared)throw new Error(`TensorArray ${this.name}: Could not read index ${e} twice because it was cleared after a previous read (perhaps try setting clear_after_read = false?).`);return this.clearAfterRead&&(t.cleared=!0),t.read=!0,t.tensor}readMany(e){return e.map(t=>this.read(t))}write(e,t){if(this.closed_)throw new Error(`TensorArray ${this.name} has already been closed.`);if(e<0||!this.dynamicSize&&e>=this.maxSize)throw new Error(`Tried to write to index ${e}, but array is not resizeable and size is: ${this.maxSize}`);let o=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
|
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because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),So(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),o.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(o.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);o.tensor=t,Et(t),o.written=!0,this.tensors[e]=o}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((o,n)=>this.write(o,t[n]))}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 n=0;n<this.size();n++)e.push(n)}if(e.length===0)return $r([],[0].concat(this.elementShape));let o=this.readMany(e);return So(this.elementShape,o[0].shape,"TensorArray shape mismatch: "),Bt(o,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 $r([],[0].concat(this.elementShape));let t=[];for(let n=0;n<this.size();n++)t.push(n);let o=this.readMany(t);return So(this.elementShape,o[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${o[0].shape})`),Ye(o,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. ${t.shape[0]}`);let o=Math.max(...e);if(!this.dynamicSize&&o>=this.maxSize)throw new Error(`Max index must be < array size (${o} vs. ${this.maxSize})`);this.writeMany(e,cr(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 o=0,n=e.map(l=>(o+=l,o));if(o!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
|
${o}, 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 s=o===0?0:t.size/o,a=[];V(()=>{t=M(t,[1,o,s]);for(let l=0;l<e.length;++l){let u=l===0?0:n[l-1],c=[0,u,0],p=[1,e[l],s];a[l]=M(Re(t,c,p),this.elementShape)}return a});let i=[];for(let l=0;l<e.length;l++)i[l]=l;this.writeMany(i,a)}};var ec=class{constructor(e,t,o,n=-1){this.tensors=e,this.elementShape=t,this.elementDtype=o,e!=null&&e.forEach(s=>{if(o!==s.dtype)throw new Error(`Invalid data types; op elements ${o}, but list elements ${s.dtype}`);So(t,s.shape,"TensorList shape mismatch: "),Et(s)}),this.idTensor=ue(0),this.maxNumElements=n,Et(this.idTensor)}get id(){return this.idTensor.id}copy(){return new ec([...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,o=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(o!==-1&&this.tensors.length!==o)throw new Error(`Operation expected a list with ${o} elements but got a list with ${this.tensors.length} elements.`);So(e,this.elementShape,"TensorList shape mismatch: ");let n=Ap(this.elementShape,this.tensors,e);return V(()=>{let s=this.tensors.map(a=>M(a,n));return Bt(s,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 o=Ap(this.elementShape,this.tensors,e),n=this.tensors.pop();return So(n.shape,e,"TensorList shape mismatch: "),M(n,o)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(So(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Et(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,o){if(o!==this.elementDtype)throw new Error(`Invalid data types; op elements ${o}, 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.`);So(this.tensors[e].shape,t,"TensorList shape mismatch: ");let n=Ap(this.elementShape,this.tensors,t);return M(this.tensors[e],n)}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.`);So(this.elementShape,t.shape,"TensorList shape mismatch: "),Et(t),this.tensors[e]=t}gather(e,t,o){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);So(this.elementShape,o,"TensorList shape mismatch: "),e=e.slice(0,this.size());let n=Ap(this.elementShape,this.tensors,o);return e.length===0?$r([],[0].concat(n)):V(()=>{let s=e.map(a=>M(this.tensors[a],n));return Bt(s,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);So(this.elementShape,t,"TensorList shape mismatch: ");let o=Ap(this.elementShape,this.tensors,t);return this.size()===0?$r([],[0].concat(o)):V(()=>{let n=this.tensors.map(s=>M(s,o));return Ye(n,0)})}};function M1(r,e,t){let o=r.dtype;if(r.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${r.shape}`);if(r.dtype!==t)throw new Error(`Invalid data types; op elements ${r.dtype}, but list elements ${t}`);let n=r.shape.slice(1);So(n,e,"TensorList shape mismatch: ");let s=cr(r);return new ec(s,e,o)}function L1(r,e,t){return new ec([],r,e,t)}function z1(r,e,t,o){if(e.length!==r.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${r.shape[0]}`);let n=Math.max(...e);if(o!=null&&o!==-1&&n>=o)throw new Error(`Max index must be < array size (${n} vs. ${o})`);let s=new ec([],t,r.dtype,o),a=cr(r,0);return e.forEach((i,l)=>{s.setItem(i,a[l])}),s}function B1(r,e,t){let o=0,n=e.map(c=>(o+=c,o));if(o!==r.shape[0])throw new Error(`Expected sum of lengths to be equal to
|
|
tensor.shape[0], but sum of lengths is
|
|
${o}, and tensor's shape is: ${r.shape}`);let s=r.shape.slice(1),a=ix(s,t),i=o===0?0:r.size/o,l=V(()=>{let c=[];r=M(r,[1,o,i]);for(let p=0;p<e.length;++p){let m=p===0?0:n[p-1],f=[0,m,0],d=[1,e[p],i];c[p]=M(Re(r,f,d),a)}return r.dispose(),c}),u=new ec([],t,r.dtype,e.length);for(let c=0;c<l.length;c++)u.setItem(c,l[c]);return u}var V1=async(r,e,t)=>{switch(r.op){case"If":case"StatelessIf":{let o=v("thenBranch",r,e,t),n=v("elseBranch",r,e,t),s=v("cond",r,e,t),a=v("args",r,e,t);return(await s.data())[0]?t.functionMap[o].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap):t.functionMap[n].executeFunctionAsync(a,t.tensorArrayMap,t.tensorListMap)}case"While":case"StatelessWhile":{let o=v("body",r,e,t),n=v("cond",r,e,t),s=v("args",r,e,t),a=await t.functionMap[n].executeFunctionAsync(s,t.tensorArrayMap,t.tensorListMap),i=s.map(c=>c.id),l=await a[0].data();a.forEach(c=>{!c.kept&&i.indexOf(c.id)===-1&&c.dispose()});let u=s;for(;l[0];){let c=u;u=await 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Z1=(r,e,t)=>{switch(r.op){case"BatchMatMul":case"BatchMatMulV2":case"MatMul":return[We(v("a",r,e,t),v("b",r,e,t),v("transposeA",r,e,t),v("transposeB",r,e,t))];case"Transpose":return[je(v("x",r,e,t),v("perm",r,e,t))];case"_FusedMatMul":let[o,n]=v("fusedOps",r,e,t),s=o==="biasadd",a=n==="prelu",i=v("numArgs",r,e,t),l=v("leakyreluAlpha",r,e,t);if(s){if(a&&i!==2)throw new Error("Fused MatMul with BiasAdd and Prelu must have two extra arguments: bias and alpha.");if(!a&&i!==1)throw new Error("Fused MatMul with BiasAdd must have one extra argument: bias.")}let[u,c]=v("args",r,e,t);return[Gn.matMul({a:v("a",r,e,t),b:v("b",r,e,t),transposeA:v("transposeA",r,e,t),transposeB:v("transposeB",r,e,t),bias:u,activation:n,preluActivationWeights:c,leakyreluAlpha:l})];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var J1=(r,e,t)=>{switch(r.op){case"FusedBatchNorm":case"FusedBatchNormV2":return[Ln(v("x",r,e,t),v("mean",r,e,t),v("variance",r,e,t),v("offset",r,e,t),v("scale",r,e,t),v("epsilon",r,e,t))];case"FusedBatchNormV3":return[Ln(v("x",r,e,t),v("mean",r,e,t),v("variance",r,e,t),v("offset",r,e,t),v("scale",r,e,t),v("epsilon",r,e,t))];case"LRN":return[Rm(v("x",r,e,t),v("radius",r,e,t),v("bias",r,e,t),v("alpha",r,e,t),v("beta",r,e,t))];case"Softmax":return[Aa(v("x",r,e,t))];case"LogSoftmax":return[Su(v("x",r,e,t))];case"SparseToDense":return[Zm(v("sparseIndices",r,e,t),v("outputShape",r,e,t),v("sparseValues",r,e,t),v("defaultValue",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var Q1=(r,e,t)=>{switch(r.op){case"Max":{let a=v("axis",r,e,t),i=v("keepDims",r,e,t);return[lr(v("x",r,e,t),a,i)]}case"Mean":{let a=v("axis",r,e,t),i=v("keepDims",r,e,t);return[dt(v("x",r,e,t),a,i)]}case"Min":{let a=v("axis",r,e,t),i=v("keepDims",r,e,t);return[Oi(v("x",r,e,t),a,i)]}case"Sum":{let a=v("axis",r,e,t),i=v("keepDims",r,e,t);return[ge(v("x",r,e,t),a,i)]}case"All":{let a=v("axis",r,e,t),i=v("keepDims",r,e,t);return[xu(v("x",r,e,t),a,i)]}case"Any":{let a=v("axis",r,e,t),i=v("keepDims",r,e,t);return[ol(v("x",r,e,t),a,i)]}case"ArgMax":{let a=v("axis",r,e,t);return[nl(v("x",r,e,t),a)]}case"ArgMin":{let a=v("axis",r,e,t);return[bm(v("x",r,e,t),a)]}case"Prod":{let a=v("axis",r,e,t),i=v("keepDims",r,e,t);return[Au(v("x",r,e,t),a,i)]}case"Cumsum":{let a=v("axis",r,e,t),i=v("exclusive",r,e,t),l=v("reverse",r,e,t);return[vu(v("x",r,e,t),a,i,l)]}case"Bincount":let o=v("x",r,e,t),n=v("weights",r,e,t),s=v("size",r,e,t);return[aw(o,n,s)];case"DenseBincount":{let a=v("x",r,e,t),i=v("weights",r,e,t),l=v("size",r,e,t),u=v("binaryOutput",r,e,t);return[mw(a,i,l,u)]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var eA=(r,e,t)=>{switch(r.op){case"ConcatV2":case"Concat":{let o=v("n",r,e,t),n=v("axis",r,e,t),s=v("tensors",r,e,t);return s=s.slice(0,o),[Ye(s,n)]}case"Gather":{let o=v("x",r,e,t),n=v("indices",r,e,t);return[zn(o,ne(n,"int32"),0)]}case"GatherV2":{let o=v("axis",r,e,t),n=v("batchDims",r,e,t),s=v("x",r,e,t),a=v("indices",r,e,t);return[zn(s,ne(a,"int32"),o,n)]}case"Reverse":{let o=v("dims",r,e,t),n=[];for(let a=0;a<o.length;a++)o[a]&&n.push(a);let s=v("x",r,e,t);return[Ht(s,n)]}case"ReverseV2":{let o=v("axis",r,e,t),n=v("x",r,e,t);return[Ht(n,o)]}case"Slice":{let o=v("begin",r,e,t),n=v("size",r,e,t);return[Re(v("x",r,e,t),o,n)]}case"StridedSlice":{let o=v("begin",r,e,t),n=v("end",r,e,t),s=v("strides",r,e,t),a=v("beginMask",r,e,t),i=v("endMask",r,e,t),l=v("ellipsisMask",r,e,t),u=v("newAxisMask",r,e,t),c=v("shrinkAxisMask",r,e,t),p=v("x",r,e,t);return[Hm(p,o,n,s,a,i,l,u,c)]}case"Pack":return V(()=>{let o=v("axis",r,e,t),n=v("tensors",r,e,t),s=n[0].shape,a=Co(n[0]).shape,i=n.map(l=>{let u=x.arraysEqual(l.shape,s);if(!u&&!x.arraysEqual(Co(l).shape,a))throw new Error("the input tensors shape does not match");return u?l:M(l,s)});return[Bt(i,o)]});case"Unpack":{let o=v("axis",r,e,t),n=v("tensor",r,e,t);return cr(n,o)}case"Tile":{let o=v("reps",r,e,t);return[Lo(v("x",r,e,t),o)]}case"Split":case"SplitV":{let o=v("axis",r,e,t),n=v("numOrSizeSplits",r,e,t),s=v("x",r,e,t);return ur(s,n,o)}case"ScatterNd":{let o=v("indices",r,e,t),n=v("values",r,e,t),s=v("shape",r,e,t);return[Mw(o,n,s)]}case"GatherNd":{let o=v("x",r,e,t),n=v("indices",r,e,t);return[Lw(o,n)]}case"SparseToDense":{let o=v("sparseIndices",r,e,t),n=v("outputShape",r,e,t),s=v("sparseValues",r,e,t),a=v("defaultValue",r,e,t);return[Zm(o,s,n,s.dtype===a.dtype?a:ne(a,s.dtype))]}default:throw TypeError(`Node type ${r.op} is not implemented`)}};var tA=(r,e,t)=>{switch(r.op){case"FFT":return[Ea(v("x",r,e,t))];case"IFFT":return[Pi(v("x",r,e,t))];case"RFFT":return[Da(v("x",r,e,t))];case"IRFFT":return[Pu(v("x",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};var rA=(r,e,t)=>{switch(r.op){case"Cast":return[ne(v("x",r,e,t),v("dtype",r,e,t))];case"ExpandDims":{let o=v("axis",r,e,t);return[ir(v("x",r,e,t),o)]}case"Squeeze":{let o=v("axis",r,e,t);return[Co(v("x",r,e,t),o)]}case"Reshape":return[M(v("x",r,e,t),v("shape",r,e,t))];case"MirrorPad":return[Mm(v("x",r,e,t),v("padding",r,e,t),v("mode",r,e,t))];case"PadV2":case"Pad":return[Rr(v("x",r,e,t),v("padding",r,e,t),v("constantValue",r,e,t))];case"SpaceToBatchND":{let o=v("blockShape",r,e,t),n=v("paddings",r,e,t);return[Sa(v("x",r,e,t),o,n)]}case"BatchToSpaceND":{let o=v("blockShape",r,e,t),n=v("crops",r,e,t);return[_a(v("x",r,e,t),o,n)]}case"DepthToSpace":{let o=v("blockSize",r,e,t),n=v("dataFormat",r,e,t).toUpperCase();return[Tm(v("x",r,e,t),o,n)]}case"BroadcastTo":return[sl(v("x",r,e,t),v("shape",r,e,t))];default:throw TypeError(`Node type ${r.op} is not implemented`)}};function bk(r,e,t,o){let n=((s,a,i)=>{switch(s.category){case"arithmetic":return V(()=>F1(s,a,i));case"basic_math":return V(()=>O1(s,a,i));case"control":return V1(s,a,i);case"convolution":return V(()=>W1(s,a,i));case"creation":return V(()=>U1(s,a,i));case"dynamic":return j1(s,a,i);case"evaluation":return V(()=>H1(s,a,i));case"image":return V(()=>X1(s,a,i));case"graph":return V(()=>q1(s,a,i));case"logical":return V(()=>Y1(s,a,i));case"matrices":return V(()=>Z1(s,a,i));case"normalization":return V(()=>J1(s,a,i));case"reduction":return V(()=>Q1(s,a,i));case"slice_join":return V(()=>eA(s,a,i));case"spectral":return V(()=>tA(s,a,i));case"transformation":return V(()=>rA(s,a,i));case"hash_table":return K1(s,a,i,o);case"custom":let l=qg(s.op);if(l&&l.customExecutor)return l.customExecutor(new hk(s,a,i));throw TypeError(`Custom op ${s.op} is not registered.`);default:throw TypeError(`Unknown op '${s.op}'. File an issue at https://github.com/tensorflow/tfjs/issues so we can add it, or register a custom execution with tf.registerOp()`)}})(r,e,t);return x.isPromise(n)?n.then(s=>[].concat(s)):[].concat(n)}var ax=class{constructor(e={},t={},o={},n={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=o,this.functionMap=n,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 o=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(o))}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 _k(r,e,t,o){let n=new Set,s=[],a=null,i=null,l=new Set,u=Object.keys(r).map(m=>eo(m)[0]),c=[];o!=null&&(c=o.map(m=>eo(m.name)[0]));let p=[...e];for(;p.length>0;){let m=p.pop();if((wk(m)||r6(m)||o6(m))&&a==null&&(a=m,i=a.children.map(f=>f.name).filter(f=>n.has(f))),n.add(m.name),t[m.name]==null&&u.indexOf(m.name)===-1&&c.indexOf(m.name)===-1){if(m.inputs.length===0){s.push(m.name);continue}m.inputs.forEach(f=>{l.has(f.name)||(l.add(f.name),p.push(f))})}}return{inputs:r,outputs:e,usedNodes:n,missingInputs:s,dynamicNode:a,syncInputs:i}}function oA(r,e,t){let{usedNodes:o,inputs:n}=t,s=[],a=Object.keys(n).map(c=>eo(c)[0]).map(c=>r.nodes[c]),i=r.initNodes;a.forEach(c=>{o.has(c.name)&&s.push(c)}),r.weights.forEach(c=>{o.has(c.name)&&s.push(c)}),i!=null&&i.forEach(c=>{o.has(c.name)&&s.push(c)});let l=new Set,u=[];for(;s.length>0;){let c=s.pop();l.add(c.name),e[c.name]||u.push(c),c.children.forEach(p=>{!l.has(p.name)&&o.has(p.name)&&p.inputs.every(m=>l.has(m.name))&&s.push(p)})}return u}var n6=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],s6=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],i6=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2"];function wk(r){return n6.indexOf(r.op)>=0}function r6(r){return s6.indexOf(r.op)>=0}function o6(r){return i6.indexOf(r.op)>=0}var Ep=class{constructor(e,t){this.graph=e,this.parent=t,this.compiledMap=new Map,this._weightMap={},this.SEPERATOR=",",this._functions={},this._functionExecutorMap={},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(o=>{this._functionExecutorMap[o]=new Ep(e.functions[o],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(o=>e[o].map(n=>n.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 o=e.map(s=>s.name).sort(),n=t.map(s=>s.name).sort();return o.join(this.SEPERATOR)+"--"+n.join(this.SEPERATOR)}compile(e,t){let o=_k(e,t,this.weightMap,this._initNodes),{missingInputs:n,dynamicNode:s,syncInputs:a}=o;if(s!=null)throw new Error(`This execution contains the node '${s.name}', which has the dynamic op '${s.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${a}]`);if(n.length>0){let i=t.map(u=>u.name),l=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${l}]. Missing the following inputs: [${n}]`)}return oA(this.graph,this.weightMap,o)}execute(e,t){e=this.mapInputs(e);let o=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let n=o.map(p=>this.graph.nodes[eo(p)[0]]),s=t.map(p=>eo(p)[0]),a=s.map(p=>this.graph.nodes[p]);a.length===0&&(a=this._outputs);let i=this.getCompilationKey(n,a),l=this.compiledMap.get(i);l==null&&(l=this.compile(e,a),this.compiledMap.set(i,l));let u={},c={};return V(()=>{let p=new ax(this.weightMap,u,c,this.functionExecutorMap),m=Object.assign({},this.weightMap);Object.keys(e).forEach(h=>{let[g,y]=eo(h),b=[];b[y]=e[h],m[g]=b});let f=this.getFrozenTensorIds(m),d={};for(let h=0;h<l.length;h++){let g=l[h];if(!m[g.name]){let y=bk(g,m,p,this._resourceManager);if(x.isPromise(y))throw new Error(`The execution of the op '${g.op}' returned a promise. Please use model.executeAsync() instead.`);m[g.name]=y,this.checkTensorForDisposal(g.name,g,m,p,f,s,d)}}return this.parent==null&&p.dispose(f),t.map(h=>gr(h,m,p))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(o=>e[o]).map(o=>o.map(n=>n.id)));return new Set(t)}checkTensorForDisposal(e,t,o,n,s,a,i){t.category==="control"||a.indexOf(e)!==-1||(o[e].forEach(l=>{l!=null&&(i[l.id]=(i[l.id]||0)+t.children.length)}),t.inputs.forEach(l=>{if(l.category!=="control"){let u=E1(l.name,o,n);u!=null&&u.forEach(c=>{if(c&&!s.has(c.id)){let p=i[c.id];p===1?(c.dispose(),delete i[c.id]):p!=null&&i[c.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,o=!1,n={},s={}){o||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let a=new ax(this.weightMap,n,s,this.functionExecutorMap),i=await this.executeWithControlFlow(e,a,t,o),l=t.map(m=>gr(m,i,a)),u=l.map(m=>m.id),c=Object.keys(e).map(m=>e[m].id),p=new Set([...u,...c,...this.weightIds]);return Object.keys(i).forEach(m=>{i[m].forEach(d=>{d&&!d.isDisposed&&!p.has(d.id)&&d.dispose()})}),this.parent==null&&a.dispose(p),l}async executeFunctionAsync(e,t,o){let n=e.reduce((s,a,i)=>(s[this.inputs[i].name]=a,s),{});return this._executeAsync(n,this.outputNodes,!0,t,o)}async executeWithControlFlow(e,t,o,n){let s=Object.keys(e),a=s.map(w=>this.graph.nodes[eo(w)[0]]),i=o.map(w=>eo(w)[0]),l=i.map(w=>this.graph.nodes[w]);l.length===0&&(l=this._outputs);let{usedNodes:u,missingInputs:c,dynamicNode:p,syncInputs:m}=_k(e,l,this.weightMap,this._initNodes),f=[...a,...this.graph.weights,...this._initNodes||[]].map(w=>({node:w,contexts:t.currentContext})),d=Object.assign({},this.weightMap);Object.keys(e).forEach(w=>{let[_,C]=eo(w),D=[];D[C]=e[w],d[_]=D});let h={},g=this.getFrozenTensorIds(d),y={};for(;f.length>0;){let w=this.processStack(a,f,t,d,y,g,i,h,u);await Promise.all(w)}p==null&&!n&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let b=l.filter(w=>!wk(w)&&!gr(w.name,d,t)).map(w=>w.name);if(b.length>0){let w="";throw p!=null&&(w=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${m}]`),new Error(`Cannot compute the outputs [${b}] from the provided inputs [${s}]. Consider providing the following inputs: [${c}]. ${w}`)}return d}processStack(e,t,o,n,s,a,i,l,u){let c=[];for(;t.length>0;){let p=t.pop();o.currentContext=p.contexts;let m="";if(p.node.op==="Enter"&&v("isConstant",p.node,n,o)&&([m]=Ms(p.node.name,o)),n[p.node.name]==null){let f=bk(p.node,n,o,this._resourceManager);m||([m]=Ms(p.node.name,o));let d=o.currentContext;x.isPromise(f)?c.push(f.then(h=>(n[m]=h,o.currentContext=d,this.checkTensorForDisposal(m,p.node,n,o,a,i,l),this.processChildNodes(p.node,t,o,n,s,u),h))):(n[m]=f,this.checkTensorForDisposal(m,p.node,n,o,a,i,l),this.processChildNodes(p.node,t,o,n,s,u))}else this.processChildNodes(p.node,t,o,n,s,u)}return c}processChildNodes(e,t,o,n,s,a){e.children.forEach(i=>{let[l]=Ms(i.name,o);s[l]||!a.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!gr(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})):i.inputNames.every(u=>!!gr(u,n,o))&&(s[l]=!0,t.push({contexts:o.currentContext,node:i})))})}dispose(){Object.keys(this.weightMap).forEach(e=>this.weightMap[e].forEach(t=>t.dispose()))}checkInputShapeAndType(e){Object.keys(e).forEach(t=>{let o=e[t],[n]=eo(t),s=this.graph.nodes[n];if(s.attrParams.shape&&s.attrParams.shape.value){let a=s.attrParams.shape.value,i=a.length===o.shape.length&&o.shape.every((l,u)=>a[u]===-1||a[u]===l);x.assert(i,()=>`The shape of dict['${s.name}'] provided in model.execute(dict) must be [${a}], but was [${o.shape}]`)}s.attrParams.dtype&&s.attrParams.dtype.value&&x.assert(o.dtype===s.attrParams.dtype.value,()=>`The dtype of dict['${s.name}'] provided in model.execute(dict) must be ${s.attrParams.dtype.value}, but was ${o.dtype}`)})}mapInputs(e){let t={};for(let o in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[o]!=null){let n=this._signature.inputs[o];t[n.name]=e[o]}else t[o]=e[o];return t}checkInputs(e){let t=Object.keys(e).filter(o=>{let[n]=eo(o);return this.graph.nodes[n]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${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[o]=eo(t);if(!this.graph.nodes[o])throw new Error(`The output '${t}' is not found in the graph`)})}};var kk=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in 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t=Cr.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Cr.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,o;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?o=this.artifacts.userDefinedMetadata.signature:o=this.artifacts.signature,this.signature=o,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let n=Cr.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new Ep(Xg.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(n),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let s=Xg.Instance.transformGraph(e.modelInitializer);this.initializer=new Ep(s),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let o=Cr.getSaveHandlers(e);if(o.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(o.length>1)throw new Error(`Found more than one (${o.length}) save handlers for URL '${e}'`);e=o[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 Ve)&&!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 ${e.length} input tensors.`);return this.inputNodes.reduce((t,o,n)=>(t[o]=e[n],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=this.executor.execute(e,t);return o.length>1?o:o[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let o=await this.executor.executeAsync(e,t);return o.length>1?o:o[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,o)=>(t[o]=[e[o]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function nA(r,e={}){if(r==null)throw new Error("modelUrl in loadGraphModel() cannot be null. 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u6(r){return r===null||typeof r!="object"&&typeof r!="function"}function uA(r){return sA(r,c6)}function c6(r){return r instanceof Ve?{value:r.clone(),recurse:!1}:_l(r)?{value:null,recurse:!0}:{value:r,recurse:!1}}var Nd=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is 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qt{constructor(e){super();this.upstream=e,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Serial`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){return this.upstream.next()}},xA=class extends qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Skip`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.count++<this.maxCount;){let e=await this.upstream.next();if(e.done)return e;Te(e.value)}return this.upstream.next()}},yA=class extends qt{constructor(e,t){super();this.upstream=e,this.maxCount=t,this.count=0}summary(){return`${this.upstream.summary()} -> Take`}async next(){return this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},bA=class extends qt{constructor(e,t,o=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=o,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},wA=class extends qt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Te(e.value)}}},_A=class extends qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=Mn.getTensorsInContainer(e.value),o=this.transform(e.value),n=Mn.getTensorsInContainer(o);for(let s of t)Mn.isTensorInList(s,n)||s.dispose();return{value:o,done:!1}}},kA=class extends qt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${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}}}},Nk=class extends qt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await 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qt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let o=await this.moreIterators.next();if(o.done)return{value:null,done:!0};this.iterator=o.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},La;(function(r){r[r.FAIL=0]="FAIL",r[r.SHORTEST=1]="SHORTEST",r[r.LONGEST=2]="LONGEST"})(La||(La={}));var dA=class extends qt{constructor(e,t=La.FAIL){super();this.iterators=e,this.mismatchMode=t,this.count=0,this.currentPromise=null}summary(){return"{TODO: fill in upstream of zip summaries} -> Zip"}async nextState(e){await e;let t=0,o=0;function n(a){return a instanceof 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${e}`);let n;return this.size===Infinity||this.size==null?n=this.size:t?n=Math.ceil(this.size/e):n=Math.floor(this.size/e),fo(async()=>(await o.iterator()).columnMajorBatch(e,t,p6),n)}concatenate(e){let t=this,o;return this.size===Infinity||e.size===Infinity?o=Infinity:this.size!=null&&e.size!=null?o=this.size+e.size:o=null,fo(async()=>(await t.iterator()).concatenate(await e.iterator()),o)}filter(e){let t=this,o;return this.size===Infinity?o=Infinity:o=null,fo(async()=>(await t.iterator()).filter(n=>V(()=>e(n))),o)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return fo(async()=>(await t.iterator()).map(o=>V(()=>e(o))),this.size)}mapAsync(e){let t=this;return fo(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 fo(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,o;return this.size!=null&&e>0?o=this.size*e:e===0?o=0:this.size!=null&&(e===void 0||e<0)?o=Infinity:o=null,fo(async()=>{let n=Sd(async()=>({value:await t.iterator(),done:!1}));return fA(n.take(e))},o)}skip(e){let t=this,o;return this.size!=null&&e>=0&&this.size>=e?o=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?o=0:o=null,fo(async()=>(await t.iterator()).skip(e),o)}shuffle(e,t,o=!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 (${this.size} elements)`);let n=this,s=IA.alea(t||x.now().toString());return fo(async()=>{let a=s.int32();return o&&(a+=s.int32()),(await n.iterator()).shuffle(e,a.toString())},this.size)}take(e){let t=this,o;return this.size!=null&&this.size>e?o=e:this.size!=null&&this.size<=e?o=this.size:o=null,fo(async()=>(await t.iterator()).take(e),o)}async toArray(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArray()}async toArrayForTest(){if(this.size===Infinity)throw new Error("Can not convert infinite data stream to array.");return(await this.iterator()).toArrayForTest()}};Ui.MAX_BUFFER_SIZE=1e4;function fo(r,e=null){return new class extends Ui{constructor(){super(...arguments);this.size=e}async iterator(){return r()}}}function NA(r){return fo(async()=>Ck(r),r.length)}function SA(r){if(!_l(r))throw new Error("The argument to zip() must be an object or array.");let e;if(Array.isArray(r))for(let t=0;t<r.length;t++)e=e==null?r[t].size:Math.min(e,r[t].size);else if(r instanceof Object)for(let t in r)e=e==null?r[t].size:Math.min(e,r[t].size);return fo(async()=>{let t=await px(r,o=>{if(o instanceof Ui)return{value:o.iterator(),recurse:!1};if(_l(o))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return hA(t,La.SHORTEST)},e)}function p6(r){if(r===null)return null;let e=r[0];return lA(e)?{value:m6(r),recurse:!1}:{value:null,recurse:!0}}function m6(r){if(r.length===0)throw new Error("Can't make a batch of zero elements.");return r[0]instanceof Ve?Bt(r):$r(r)}var Td=class extends Ui{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
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`).map(n=>(n.endsWith("\r")&&(n=n.slice(0,-1)),n))}};var mx='"',Ad=Symbol("out"),TA=Symbol("field"),fx=Symbol("quote"),Tk=Symbol("quoteafterquote"),AA=Symbol("quoteinquote"),Ed=class extends Ui{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 Td(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(x.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&&x.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((n,s)=>(n[s]=n[s]+1||1,n),{}),o=Object.keys(t).filter(n=>t[n]>1);if(x.assert(o.length===0,()=>"Duplicate column names found: "+o.toString()),this.columnConfigs){for(let n of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(n)===-1)throw new Error('The key "'+n+'" 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 o=t.value;return this.parseRow(o,!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),o={},n={};for(let s=0;s<this.fullColumnNames.length;s++){let a=this.fullColumnNames[s],i=this.columnConfigs?this.columnConfigs[a]:null;if(!(this.configuredColumnsOnly&&!i)){let l=t[s],u=null;if(l==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${a} is empty in this line: ${e}`);u=void 0}else{let c=Number(l);if(isNaN(c))i&&i.dtype==="bool"?u=this.getBoolean(l):u=l;else if(!i||!i.dtype)u=c;else switch(i.dtype){case"float32":u=c;break;case"int32":u=Math.floor(c);break;case"bool":u=this.getBoolean(l);break;default:u=c}}i&&i.isLabel?n[a]=u:o[a]=u}}return Object.keys(n).length===0?o:{xs:o,ys:n}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let o=[],n=0,s=e.length,a=Ad;for(let i=0;i<s;i++)switch(a){case Ad:switch(e.charAt(i)){case mx:n=i+1,a=fx;break;case this.delimiter:if(n=i+1,this.delimiter===" "&&this.delimWhitespace)break;o.push(""),a=Ad;break;default:a=TA,n=i;break}break;case TA:switch(e.charAt(i)){case this.delimiter:o.push(e.substring(n,i)),a=Ad,n=i+1;break;default:}break;case fx:switch(e.charAt(i)){case mx:a=Tk;break;default:}break;case Tk:switch(e.charAt(i)){case this.delimiter:o.push(e.substring(n,i-1)),a=Ad,n=i+1;break;case mx:a=fx;break;default:a=AA;break}break;case AA:switch(e.charAt(i)){case mx:a=fx;break;default:}break;default:}if(a===Tk?o.push(e.substring(n,s-1)):o.push(e.substring(n)),t&&o.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${o}`);return o}};var Dd=class extends qt{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(W().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Dd(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(o){throw new Error(`Error thrown while initializing video stream: ${o.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: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,o=await this.getAudioData();if(this.includeSpectrogram){let n=this.flattenQueue(o.freqDataQueue);e=this.getTensorFromAudioDataArray(n,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let n=this.flattenQueue(o.timeDataQueue);t=this.getTensorFromAudioDataArray(n,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],o=0;return new Promise(n=>{let s=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&n({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++o===this.numFrames&&(clearInterval(s),n({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,o=new Float32Array(e.length*t);return e.forEach((n,s)=>o.set(n,s*t)),o}getTensorFromAudioDataArray(e,t){let o=new Float32Array(x.sizeFromShape(t));return o.set(e,o.length-e.length),$r(o,t)}};var $d=class extends qt{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=Vt([0],"int32"),this.webcamConfig.centerCrop){let o=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,n=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,s=(1-o)/2,a=(1-n)/2,i=s+o,l=n+a;this.cropBox=Mi([a,s,l,i],[1,4])}else this.cropBox=Mi([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(W().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 o=new $d(e,t);return await o.start(),o}async start(){this.webcamConfig.facingMode&&x.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=Uh.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 V(()=>{let t=ir(ne(e,"float32"),0),o;o=Ds.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let n=o.shape;return M(o,n.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.")}};var Rd=class{};var dx=class extends qt{split(e){return new EA(this,e)}},EA=class extends dx{constructor(e,t){super();this.upstream=e,this.impl=new DA(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},DA=class extends $p{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${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 o of t.slice(0,-1))this.outputQueue.push(o);return this.carryover=t[t.length-1],!0}};var Ak=class extends qt{decodeUTF8(){return new RA(this)}},RA=class extends dx{constructor(e){super();this.upstream=e,this.impl=new FA(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},FA=class extends $p{constructor(e){super();if(this.upstream=e,W().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=$A();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 o;return W().get("IS_BROWSER")?o=this.decoder.decode(t,{stream:!0}):o=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(o),!0}};var Fd=class extends Ak{constructor(e,t={}){super();this.file=e,this.options=t,x.assert(e instanceof Uint8Array||(W().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,o)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)t(new Uint8Array(this.file.slice(this.offset,n)));else{let s=new FileReader;s.onload=i=>{let l=s.result;if(l instanceof ArrayBuffer&&(l=new Uint8Array(l)),!(l instanceof Uint8Array))return o(new TypeError("FileReader returned unknown type."));t(l)},s.onabort=i=>o(new Error("Aborted")),s.onerror=i=>o(new Error(i.type));let a=this.file.slice(this.offset,n);s.readAsArrayBuffer(a)}this.offset=n}),done:!1}}};async function OA(r,e={}){let t,o;typeof r=="string"?t=r:(t=r.url,o=f6(r));let n=await x.fetch(t,o);if(n.ok){let s=new Uint8Array(await n.arrayBuffer());return new Fd(s,e)}else throw new Error(n.statusText)}var f6=r=>({method:r.method,headers:r.headers,body:r.body,mode:r.mode,credentials:r.credentials,cache:r.cache,redirect:r.redirect,referrer:r.referrer,integrity:r.integrity});function hx(r){return typeof r=="string"&&r.substr(0,7)==="file://"}var Od=class extends Rd{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(hx(this.input)&&W().get("IS_NODE")){let e=require("fs");this.input=e.readFileSync(this.input.substr(7))}return new Fd(this.input,this.options)}};var Pd=class extends Rd{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return hx(this.url)?new Od(this.url,this.fileOptions).iterator():OA(this.url,this.fileOptions)}};function PA(r,e={}){return new Ed(new Pd(r),e)}function MA(r){let e=Sd(r);return fo(async()=>e)}function LA(r){return fo(async()=>{let e=await r();return Sd(()=>e.next())})}async function zA(r,e){return $d.create(r,e)}async function BA(r){return Dd.create(r)}var gx="3.2.0";function ee(r,e){Array.isArray(r)||(r=[r]),r.forEach(t=>{t!=null&&x.assert(t.dtype!=="complex64",()=>`${e} does not support complex64 tensors in the CPU backend.`)})}var d6=Tr.whereImpl,Rp=class extends Gs{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new Xa(this,Po())}nextDataId(){return Rp.nextDataId++}write(e,t,o){this.firstUse&&(this.firstUse=!1,W().get("IS_NODE")&&N.warn(`
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============================
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Hi there \u{1F44B}. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.
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============================`));let n={id:this.nextDataId()};return this.data.set(n,{values:e,dtype:o,refCount:1}),n}makeTensorInfo(e,t,o){let n;if(t==="string"&&o!=null&&o.length>0&&x.isString(o[0])){let s=o.map(a=>x.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return{dataId:n,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,o,n,s){this.data.set(e,{values:t,dtype:n,refCount:s})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:o}=this.data.get(e);if(t==="complex64"){let n=this.readSync(o.real.dataId),s=this.readSync(o.imag.dataId);return N.mergeRealAndImagArrays(n,s)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),o=t;if(e.dtype==="string")try{o=t.map(n=>x.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return ve(e.shape,e.dtype,o)}makeOutput(e,t,o){let n=this.write(e,t,o);return Po().makeTensorFromDataId(n,t,o,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:o}=this.data.get(e);o!=null&&(this.disposeData(o.real.dataId,!0),this.disposeData(o.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=x.now();return e(),{kernelMs:x.now()-t}}memory(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}}where(e){ee([e],"where");let t=this.readSync(e.dataId);return d6(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Rp.nextDataId=0;var Bk={};Je(Bk,{addImpl:()=>qA,bincountImpl:()=>Md,bincountReduceImpl:()=>Dk,ceilImpl:()=>XA,concatImpl:()=>Ld,expImpl:()=>ZA,expm1Impl:()=>QA,floorImpl:()=>tE,gatherV2Impl:()=>Rk,greaterImpl:()=>oE,lessImpl:()=>sE,linSpaceImpl:()=>Fk,logImpl:()=>aE,maxImpl:()=>Ok,maximumImpl:()=>uE,minimumImpl:()=>pE,multiplyImpl:()=>xx,negImpl:()=>dE,notEqualImpl:()=>gE,prodImpl:()=>bE,rangeImpl:()=>Vd,rsqrtImpl:()=>_E,simpleAbsImpl:()=>VA,sliceImpl:()=>Gd,squaredDifferenceImpl:()=>CE,stridedSliceImpl:()=>Pk,subImpl:()=>NE,tileImpl:()=>Mk,topKImpl:()=>Lk,transposeImpl:()=>Bd,uniqueImpl:()=>zk});function VA(r){let e=new Float32Array(r.length);for(let t=0;t<r.length;++t)e[t]=Math.abs(r[t]);return e}var h6=r=>{let{x:e}=r.inputs,t=r.backend;ee(e,"abs");let o=new 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l.complexTensorInfos={real:t.makeTensorInfo(o.shape,"float32",s),imag:t.makeTensorInfo(n.shape,"float32",a)},i}var WA={kernelName:Gl,backendName:"cpu",kernelFunc:pr};function Fp(r,e,t="float32"){if(t==="complex64"){let n=Fp(r,e,"float32"),s=Fp(r,e,"float32");return pr({inputs:{real:n,imag:s},backend:r})}let o=x.makeZerosTypedArray(x.sizeFromShape(e),t);return r.makeTensorInfo(e,t,o)}function Ar(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var UA={kernelName:Ro,backendName:"cpu",kernelFunc:Ar};function Zn(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.data.get(o.dataId).complexTensorInfos.real,s=t.data.get(n.dataId).values;return t.makeTensorInfo(n.shape,n.dtype,s)}var jA={kernelName:iu,backendName:"cpu",kernelFunc:Zn};function Jn(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return Ar({inputs:{x:n},backend:t});let 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JE={kernelName:Bl,backendName:"cpu",kernelFunc:X6};function Y6(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;ee([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=c.strideHeight,m=c.strideWidth,f=c.filterHeight,d=c.filterWidth,h=c.dilationHeight,g=c.dilationWidth,y=c.effectiveFilterHeight,b=c.effectiveFilterWidth,w=b-1-c.padInfo.left,_=y-1-c.padInfo.top,C=ve(a.shape,"float32"),D=1/(f*d),T=t.data.get(n.dataId).values,R=ve(n.shape,"float32",T);for(let P=0;P<c.batchSize;++P)for(let B=0;B<c.inChannels;++B)for(let G=0;G<c.inHeight;++G)for(let U=0;U<c.inWidth;++U){let j=G-_,H=U-w,K=0;for(let X=0;X<y;X+=h){let oe=(j+X)/p;if(!(oe<0||oe>=c.outHeight||Math.floor(oe)!==oe))for(let Y=0;Y<b;Y+=g){let re=(H+Y)/m;if(re<0||re>=c.outWidth||Math.floor(re)!==re)continue;K+=R.get(P,oe,re,B)}}C.set(K*D,P,G,U,B)}return t.makeTensorInfo(C.shape,C.dtype,C.values)}var QE={kernelName:zl,backendName:"cpu",kernelFunc:Y6};function 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t.makeTensorInfo(n.shape,n.dtype,h)}var e2={kernelName:an,backendName:"cpu",kernelFunc:Z6};function J6(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;ee([n],"batchToSpaceND");let i=s.reduce((y,b)=>y*b),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=Qe({inputs:{x:n},backend:t,attrs:{shape:l}}),d=tr({inputs:{x:f},backend:t,attrs:{perm:u}}),h=Qe({inputs:{x:d},backend:t,attrs:{shape:c}}),g=ts({inputs:{x:h},backend:t,attrs:{begin:p,size:m}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(h),g}var t2={kernelName:ra,backendName:"cpu",kernelFunc:J6};function Q6(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.data.get(n.dataId).values,l=t.data.get(s.dataId).values,u=Md(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var r2={kernelName:Vl,backendName:"cpu",kernelFunc:Q6};var e5=$e($o,(r,e)=>{let t=e;return r>t.clipValueMax?t.clipValueMax:r<t.clipValueMin?t.clipValueMin:r}),o2={kernelName:$o,backendName:"cpu",kernelFunc:e5};var t5=r=>{let{x:e}=r.inputs,t=r.backend,o=new Float32Array(x.sizeFromShape(e.shape)),n=t.data.get(e.dataId),s=n.complexTensorInfos.real,a=n.complexTensorInfos.imag,i=t.data.get(s.dataId).values,l=t.data.get(a.dataId).values;for(let u=0;u<i.length;u++){let c=i[u],p=l[u];o[u]=Math.hypot(c,p)}return t.makeOutput(o,e.shape,"float32")},n2={kernelName:oa,backendName:"cpu",kernelFunc:t5};function ji(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.data.get(o.dataId).complexTensorInfos.imag,s=t.data.get(n.dataId).values;return t.makeTensorInfo(n.shape,n.dtype,s)}var s2={kernelName:Ql,backendName:"cpu",kernelFunc:ji};function kl(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=x.parseAxisParam(n,e[0].shape)[0],a=N.computeOutShape(e.map(h=>h.shape),s);if(x.sizeFromShape(a)===0)return 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u.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var i2={kernelName:ls,backendName:"cpu",kernelFunc:kl};function qk(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=o;ee([n,s],"conv2d");let p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,p),f=m.filterHeight,d=m.filterWidth,h=m.dilationHeight,g=m.dilationWidth,y=m.padInfo.left,b=m.padInfo.top,w=m.dataFormat==="channelsLast",_=new lt(m.outShape,n.dtype),C=x.computeStrides(n.shape),D=x.computeStrides(s.shape),T=C[0],R=w?C[1]:C[2],P=w?C[2]:1,B=w?1:C[1],G=_.strides[0],U=w?_.strides[1]:_.strides[2],j=w?_.strides[2]:1,H=w?1:_.strides[1],K=t.data.get(n.dataId).values,X=t.data.get(s.dataId).values,oe=_.values;for(let Y=0;Y<m.batchSize;++Y){let re=Y*T,te=Y*G;for(let ie=0;ie<m.outHeight;++ie){let le=te+ie*U,ae=ie*m.strideHeight-b;for(let fe=0;fe<f;++fe){let de=ae+fe*h;if(de<0||de>=m.inHeight)continue;let xe=fe*D[0],we=re+de*R;for(let 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t.makeTensorInfo(h.shape,h.dtype,h.values)}var u2={kernelName:Jo,backendName:"cpu",kernelFunc:o5};function n5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o;ee([n,s],"conv3d");let u=N.computeConv3DInfo(n.shape,s.shape,a,l,i),{filterDepth:c,filterHeight:p,filterWidth:m,dilationDepth:f,dilationHeight:d,dilationWidth:h,padInfo:g}=u,y=g.front,b=g.left,w=g.top,_=new lt(u.outShape,n.dtype),C=t.data.get(n.dataId).values,D=t.data.get(s.dataId).values,T=_.values,R=x.computeStrides(n.shape),P=x.computeStrides(s.shape);for(let B=0;B<u.batchSize;++B){let G=B*R[0],U=B*_.strides[0];for(let j=0;j<u.outDepth;++j){let H=U+j*_.strides[1],K=j*u.strideDepth-y;for(let X=0;X<c;++X){let oe=K+X*f;if(oe<0||oe>=u.inDepth)continue;let Y=X*P[0],re=G+oe*R[1];for(let te=0;te<u.outHeight;++te){let ie=H+te*_.strides[2],le=te*u.strideHeight-w;for(let ae=0;ae<p;++ae){let fe=le+ae*d;if(fe<0||fe>=u.inHeight)continue;let de=Y+ae*P[1],xe=re+fe*R[2];for(let we=0;we<u.outWidth;++we){let 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u=x.computeStrides(n.shape),c=x.computeStrides(s.shape),p=N.computeConv3DInfo(l,s.shape,i,1,a),m=new lt(p.inShape,"float32"),f=m.values,[d,h,g,y]=m.strides,b=t.data.get(n.dataId).values,[w,_,C,D]=u,T=t.data.get(s.dataId).values,[R,P,B,G]=c,{batchSize:U,filterDepth:j,filterHeight:H,filterWidth:K,inChannels:X,inDepth:oe,inHeight:Y,inWidth:re,outChannels:te,outDepth:ie,outHeight:le,outWidth:ae,strideDepth:fe,strideHeight:de,strideWidth:xe}=p,we=j-1-p.padInfo.front,De=H-1-p.padInfo.top,Ie=K-1-p.padInfo.left;for(let ze=0;ze<U;++ze)for(let qe=0;qe<X;++qe)for(let it=0;it<oe;++it){let Tt=it-we,At=Math.max(0,Math.ceil(Tt/fe)),Ue=Math.min(ie,(j+Tt)/fe);for(let ut=0;ut<Y;++ut){let mt=ut-De,Pt=Math.max(0,Math.ceil(mt/de)),xo=Math.min(le,(H+mt)/de);for(let Kt=0;Kt<re;++Kt){let to=Kt-Ie,Dr=Math.max(0,Math.ceil(to/xe)),Uo=Math.min(ae,(K+to)/xe),rr=0;for(let yo=At;yo<Ue;++yo){let Vr=yo*fe-Tt;for(let yr=Pt;yr<xo;++yr){let ro=yr*de-mt;for(let Ao=Dr;Ao<Uo;++Ao){let 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$2={kernelName:si,backendName:"cpu",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,n=t,s=x.getTypedArrayFromDType(o.dtype,x.sizeFromShape(o.shape)),[a,i,l,u]=o.shape,c=n.data.get(o.dataId).values;for(let m=0;m<a;m++){let f=m*l*i*u;for(let d=0;d<i;d++){let h=d*(l*u);for(let g=0;g<l;g++){let y=g*u;for(let b=0;b<u;b++){let _=[a,d,g,b][2],C=Math.round(l-_),D=f+h+y+b,T=c[D];if(C>=0&&C<l){let R=C*u,P=f+h+R+b;T=c[P]}s[D]=T}}}}return{dataId:n.write(s,o.shape,o.dtype),shape:o.shape,dtype:o.dtype}}};var D5=Ke((r,e)=>Math.floor(r/e)),$5=et(sn,D5,null,"int32"),R2={kernelName:sn,backendName:"cpu",kernelFunc:$5};function R5(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=o,h=qk({inputs:{x:n,filter:s},backend:t,attrs:{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m}});if(a){let 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_r;(function(r){r[r.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",r[r.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",r[r.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",r[r.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",r[r.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"})(_r||(_r={}));function oc(r,e){return[e,r]}function e$(r,e){return r*e}function Cl(r){let e=x.sizeFromShape(r),t=Math.ceil(e/4);return x.sizeToSquarishShape(t)}function Hi(r,e){return[Math.max(1,Math.ceil(e/2)),Math.max(1,Math.ceil(r/2))]}function t$(r,e){let[t,o]=Hi(r,e);return t*o*4}function Kd(r,e){let t=r,o,n,s,a,i,l,u,c,p,m;return W().getNumber("WEBGL_VERSION")===2?(o=t.R32F,n=t.R16F,s=t.RGBA16F,a=t.RGBA32F,i=t.RED,u=4,c=1,p=t.HALF_FLOAT,m=t.FLOAT):(o=r.RGBA,n=r.RGBA,s=r.RGBA,a=t.RGBA,i=r.RGBA,u=4,c=4,p=e!=null?e.HALF_FLOAT_OES:null,m=r.FLOAT),l=r.RGBA,{internalFormatFloat:o,internalFormatHalfFloat:n,internalFormatPackedHalfFloat:s,internalFormatPackedFloat:a,textureFormatFloat:i,downloadTextureFormat:l,downloadUnpackNumChannels:u,defaultNumChannels:c,textureTypeHalfFloat:p,textureTypeFloat:m}}function Ae(r,e){let t=e();return W().getBool("DEBUG")&&t8(r),t}function t8(r){let e=r.getError();if(e!==r.NO_ERROR)throw new Error("WebGL Error: "+r8(r,e))}var o8=596e-10,n8=65504;function r$(r){return!!(W().getBool("WEBGL_RENDER_FLOAT32_ENABLED")||r===0||o8<Math.abs(r)&&Math.abs(r)<n8)}function r8(r,e){switch(e){case r.NO_ERROR:return"NO_ERROR";case r.INVALID_ENUM:return"INVALID_ENUM";case r.INVALID_VALUE:return"INVALID_VALUE";case r.INVALID_OPERATION:return"INVALID_OPERATION";case r.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case r.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case r.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return`Unknown error code ${e}`}}function Xd(r,e){return Ba(r,()=>r.getExtension(e),'Extension "'+e+'" not supported on this browser.')}function o$(r,e){let t=Ba(r,()=>r.createShader(r.VERTEX_SHADER),"Unable to create vertex WebGLShader.");if(Ae(r,()=>r.shaderSource(t,e)),Ae(r,()=>r.compileShader(t)),r.getShaderParameter(t,r.COMPILE_STATUS)===!1)throw console.log(r.getShaderInfoLog(t)),new Error("Failed to compile vertex shader.");return t}function n$(r,e){let t=Ba(r,()=>r.createShader(r.FRAGMENT_SHADER),"Unable to create fragment WebGLShader.");if(Ae(r,()=>r.shaderSource(t,e)),Ae(r,()=>r.compileShader(t)),r.getShaderParameter(t,r.COMPILE_STATUS)===!1)throw s8(e,r.getShaderInfoLog(t)),new Error("Failed to compile fragment shader.");return t}var i8=/ERROR: [0-9]+:([0-9]+):/g;function s8(r,e){let t=i8.exec(e);if(t==null){console.log(`Couldn't parse line number in error: ${e}`),console.log(r);return}let o=+t[1],n=r.split(`
|
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`),s=n.length.toString().length+2,a=n.map((p,m)=>x.rightPad((m+1).toString(),s)+p),i=0;for(let p=0;p<a.length;p++)i=Math.max(a[p].length,i);let l=a.slice(0,o-1),u=a.slice(o-1,o),c=a.slice(o);console.log(l.join(`
|
|
`)),console.log(e.split(`
|
|
`)[0]),console.log(`%c ${x.rightPad(u[0],i)}`,"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(c.join(`
|
|
`))}function s$(r){return Ba(r,()=>r.createProgram(),"Unable to create WebGLProgram.")}function i$(r,e){if(Ae(r,()=>r.linkProgram(e)),r.getProgramParameter(e,r.LINK_STATUS)===!1)throw console.log(r.getProgramInfoLog(e)),new Error("Failed to link vertex and fragment shaders.")}function vx(r,e){if(Ae(r,()=>r.validateProgram(e)),r.getProgramParameter(e,r.VALIDATE_STATUS)===!1)throw console.log(r.getProgramInfoLog(e)),new Error("Shader program validation failed.")}function a$(r,e){let t=Ba(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return Ae(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),Ae(r,()=>r.bufferData(r.ARRAY_BUFFER,e,r.STATIC_DRAW)),t}function l$(r,e){let t=Ba(r,()=>r.createBuffer(),"Unable to create WebGLBuffer");return Ae(r,()=>r.bindBuffer(r.ELEMENT_ARRAY_BUFFER,t)),Ae(r,()=>r.bufferData(r.ELEMENT_ARRAY_BUFFER,e,r.STATIC_DRAW)),t}function u$(r){return Ba(r,()=>r.createTexture(),"Unable to create WebGLTexture.")}function c$(r,e){let t=W().getNumber("WEBGL_MAX_TEXTURE_SIZE");if(r<=0||e<=0){let o=`[${r}x${e}]`;throw new Error("Requested texture size "+o+" is invalid.")}if(r>t||e>t){let o=`[${r}x${e}]`,n=`[${t}x${t}]`;throw new Error("Requested texture size "+o+" greater than WebGL maximum on this browser / GPU "+n+".")}}function p$(r){return Ba(r,()=>r.createFramebuffer(),"Unable to create WebGLFramebuffer.")}function tv(r,e,t,o,n,s,a){let i=r.getAttribLocation(e,t);return i===-1?!1:(Ae(r,()=>r.bindBuffer(r.ARRAY_BUFFER,o)),Ae(r,()=>r.vertexAttribPointer(i,n,r.FLOAT,!1,s,a)),Ae(r,()=>r.enableVertexAttribArray(i)),!0)}function l8(r,e,t){a8(r,t),Ae(r,()=>r.activeTexture(r.TEXTURE0+t)),Ae(r,()=>r.bindTexture(r.TEXTURE_2D,e))}function m$(r,e,t){return Ba(r,()=>r.getUniformLocation(e,t),'uniform "'+t+'" not present in program.')}function f$(r,e,t){return r.getUniformLocation(e,t)}function d$(r,e,t,o){Ae(r,()=>l8(r,e,o)),Ae(r,()=>r.uniform1i(t,o))}function Cx(r,e,t){Ae(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,t)),Ae(r,()=>r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,e,0))}function rv(r,e){Ae(r,()=>r.bindFramebuffer(r.FRAMEBUFFER,e)),Ae(r,()=>r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,null,0))}function Yd(r){let e=r.checkFramebufferStatus(r.FRAMEBUFFER);if(e!==r.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+u8(r,e))}function u8(r,e){switch(e){case r.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case r.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case r.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case r.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return`unknown error ${e}`}}function Ba(r,e,t){let o=Ae(r,()=>e());if(o==null)throw new Error(t);return o}function a8(r,e){let t=r.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,o=e+r.TEXTURE0;if(o<r.TEXTURE0||o>t){let n=`[gl.TEXTURE0, gl.TEXTURE${t}]`;throw new Error(`textureUnit must be in ${n}.`)}}function Il(r,e=2){return x.sizeFromShape(r.slice(0,r.length-e))}function Nl(r){if(r.length===0)throw Error("Cannot get rows and columns of an empty shape array.");return[r.length>1?r[r.length-2]:1,r[r.length-1]]}function Ix(r){let e=[1,1,1];return r.length===0||r.length===1&&r[0]===1||(e=[Il(r),...Nl(r)]),e}function h$(r,e=!1){let t=W().getNumber("WEBGL_MAX_TEXTURE_SIZE");e&&(t=t*2,r=r.map((n,s)=>s>=r.length-2?x.nearestLargerEven(r[s]):r[s]),r.length===1&&(r=[2,r[0]])),r.length!==2&&(r=x.squeezeShape(r).newShape);let o=x.sizeFromShape(r);if(r.length<=1&&o<=t)return[1,o];if(r.length===2&&r[0]<=t&&r[1]<=t)return r;if(r.length===3&&r[0]*r[1]<=t&&r[2]<=t)return[r[0]*r[1],r[2]];if(r.length===3&&r[0]<=t&&r[1]*r[2]<=t)return[r[0],r[1]*r[2]];if(r.length===4&&r[0]*r[1]*r[2]<=t&&r[3]<=t)return[r[0]*r[1]*r[2],r[3]];if(r.length===4&&r[0]<=t&&r[1]*r[2]*r[3]<=t)return[r[0],r[1]*r[2]*r[3]];if(e){let n=Il(r),s=2,a=2;return r.length&&([s,a]=Nl(r)),o=n*(s/2)*(a/2),x.sizeToSquarishShape(o).map(i=>i*2)}return x.sizeToSquarishShape(o)}function Nx(r){return r%2==0}function nc(r,e){if(r=r.slice(-2),e=e.slice(-2),x.arraysEqual(r,e)||!r.length||!e.length||r[0]===0||r[1]===0||e[0]===0||e[1]===0)return!0;if(r.length!==e.length){let t=r.slice(-1)[0],o=e.slice(-1)[0];if(t===o||Nx(t)&&Nx(o)&&(r[0]===1||e[0]===1))return!0}return r[1]===e[1]&&Nx(r[0])&&Nx(e[0])}var ov,nv;function g$(r){if(ov==null){let e=Go(r);ov=e.getParameter(e.MAX_TEXTURE_SIZE)}return ov}function x$(r){if(nv==null){let e=Go(r);nv=e.getParameter(e.MAX_TEXTURE_IMAGE_UNITS)}return Math.min(16,nv)}function y$(r){if(r===0)return 0;let e,t=Go(r);return Wo(t,"EXT_disjoint_timer_query_webgl2")&&r===2?e=2:Wo(t,"EXT_disjoint_timer_query")?e=1:e=0,e}function Wo(r,e){return r.getExtension(e)!=null}function sv(r){try{if(Go(r)!=null)return!0}catch(e){return console.log("Error when getting WebGL context: ",e),!1}return!1}function b$(r){if(r===0)return!1;let e=Go(r);if(r===1){if(!Wo(e,"OES_texture_float"))return!1}else if(!Wo(e,"EXT_color_buffer_float"))return!1;return iv(e)}function w$(r){if(r===0)return!1;let e=Go(r);if(r===1){if(!Wo(e,"OES_texture_float")||!Wo(e,"WEBGL_color_buffer_float"))return!1}else{if(Wo(e,"EXT_color_buffer_float"))return iv(e);let o="EXT_color_buffer_half_float";if(Wo(e,o)){let n=e.getExtension(o);return c8(e,n)}return!1}return iv(e)}function iv(r){let e=Kd(r),t=r.createTexture();r.bindTexture(r.TEXTURE_2D,t);let o=1,n=1;r.texImage2D(r.TEXTURE_2D,0,e.internalFormatFloat,o,n,0,e.textureFormatFloat,e.textureTypeFloat,null);let s=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,s),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,t,0);let a=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(t),r.deleteFramebuffer(s),a}function c8(r,e){let t=Kd(r,e),o=r.createTexture();r.bindTexture(r.TEXTURE_2D,o);let n=1,s=1;r.texImage2D(r.TEXTURE_2D,0,t.internalFormatHalfFloat,n,s,0,t.textureFormatFloat,t.textureTypeHalfFloat,null);let a=r.createFramebuffer();r.bindFramebuffer(r.FRAMEBUFFER,a),r.framebufferTexture2D(r.FRAMEBUFFER,r.COLOR_ATTACHMENT0,r.TEXTURE_2D,o,0);let i=r.checkFramebufferStatus(r.FRAMEBUFFER)===r.FRAMEBUFFER_COMPLETE;return r.bindTexture(r.TEXTURE_2D,null),r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteTexture(o),r.deleteFramebuffer(a),i}function _$(r){return r!==2?!1:Go(r).fenceSync!=null}function 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Me=W();Me.registerFlag("HAS_WEBGL",()=>Me.getNumber("WEBGL_VERSION")>0);Me.registerFlag("WEBGL_VERSION",()=>sv(2)?2:sv(1)?1:0);Me.registerFlag("WEBGL_CHECK_NUMERICAL_PROBLEMS",()=>!1);Me.registerFlag("WEBGL_BUFFER_SUPPORTED",()=>Me.get("WEBGL_VERSION")===2);Me.registerFlag("WEBGL_CPU_FORWARD",()=>!0);Me.registerFlag("WEBGL_FORCE_F16_TEXTURES",()=>!1);Me.registerFlag("WEBGL_PACK",()=>Me.getBool("HAS_WEBGL"));Me.registerFlag("WEBGL_PACK_NORMALIZATION",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_CLIP",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_DEPTHWISECONV",()=>!1);Me.registerFlag("WEBGL_PACK_BINARY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_UNARY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_ARRAY_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_IMAGE_OPERATIONS",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_PACK_REDUCE",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_LAZILY_UNPACK",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_CONV_IM2COL",()=>Me.getBool("WEBGL_PACK"));Me.registerFlag("WEBGL_MAX_TEXTURE_SIZE",()=>g$(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>x$(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let r=Me.getNumber("WEBGL_VERSION");return r===0?0:y$(r)});Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Me.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!Lc.isMobile());Me.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>b$(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_RENDER_FLOAT32_ENABLED",()=>Me.getBool("WEBGL_FORCE_F16_TEXTURES")?!1:Me.getBool("WEBGL_RENDER_FLOAT32_CAPABLE"));Me.registerFlag("WEBGL_DOWNLOAD_FLOAT_ENABLED",()=>w$(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_FENCE_API_ENABLED",()=>_$(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_SIZE_UPLOAD_UNIFORM",()=>Me.getBool("WEBGL_RENDER_FLOAT32_ENABLED")?4:0);Me.registerFlag("WEBGL_DELETE_TEXTURE_THRESHOLD",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${r}.`)});Me.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>-1,r=>{if(r<0&&r!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 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|
|
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="",u=`
|
|
#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)));
|
|
}
|
|
`):(r="",e="attribute",t="varying",o="varying",n="texture2D",s="gl_FragColor",a="",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));
|
|
}
|
|
`,u=`
|
|
int round(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 round(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`),{version:r,attribute:e,varyingVs:t,varyingFs:o,texture2D:n,output:s,defineOutput:a,defineSpecialNaN:i,defineSpecialInf:l,defineRound:u}}function zs(r,e,t="index"){let o=x.computeStrides(e);return o.map((n,s)=>{let a=`int ${r[s]} = ${t} / ${n}`,i=s===o.length-1?`int ${r[s+1]} = ${t} - ${r[s]} * ${n}`:`index -= ${r[s]} * ${n}`;return`${a}; ${i};`}).join("")}function zp(r){let e=x.computeStrides(r).map(t=>t.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${e[0]} + coords.y * ${e[1]} + coords.z;
|
|
}
|
|
`}var Sx=`
|
|
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;
|
|
}
|
|
`;var av=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=vl.DENSE;let t=Cl(e),o=Ot();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${zs(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = 4 * (resTexRC.x * ${t[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);
|
|
}
|
|
|
|
${o.output} = result;
|
|
}
|
|
`}};var lv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=vl.DENSE;let t=Cl(e),o=Ot();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${zs(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = 4 * (resTexRC.x * ${t[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));
|
|
}
|
|
|
|
${o.output} = result;
|
|
}
|
|
`}};var uv=class{constructor(e){this.variableNames=["A"],this.outTexUsage=Er.DOWNLOAD;let t=Ot();this.outputShape=e,this.userCode=`
|
|
${Sx}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}};var cv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=Er.DOWNLOAD;let t=Ot();this.outputShape=e,this.userCode=`
|
|
${Sx}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}};var pv=class{constructor(e,t,o=!1){this.variableNames=["A"];let n=Ot(),[s,a]=t;this.outputShape=e;let i="result";o&&(i="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${zp(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
int flatIndex = getFlatIndex(coords);
|
|
int offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / ${a};
|
|
int c = imod(flatIndex, ${a});
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${s}.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];
|
|
}
|
|
|
|
${n.output} = vec4(${i}, 0., 0., 0.);
|
|
}
|
|
`}};var mv=class{constructor(e,t,o=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let n=Ot(),[s,a]=t;this.outputShape=e;let i="",l="result";o&&(l="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let c=0;c<=1;c++){let p=u*2+c;i+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${c} < ${e[2]}) {
|
|
localCoords[2] += ${c};
|
|
if(localCoords[1] + ${u} < ${e[1]}) {
|
|
localCoords[1] += ${u};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
r = flatIndex / ${a};
|
|
c = imod(flatIndex, ${a});
|
|
uv = (vec2(c, r) + halfCR) / vec2(${a}.0, ${s}.0);
|
|
values = ${n.texture2D}(A, uv);
|
|
|
|
if(offset == 0) {
|
|
result[${p}] = values[0];
|
|
} else if(offset == 1) {
|
|
result[${p}] = values[1];
|
|
} else if(offset == 2) {
|
|
result[${p}] = values[2];
|
|
} else {
|
|
result[${p}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${zp(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${i}
|
|
|
|
${n.output} = ${l};
|
|
}
|
|
`}};function k$(r){let e=Ot(),t=`${e.version}
|
|
precision highp float;
|
|
${e.attribute} vec3 clipSpacePos;
|
|
${e.attribute} vec2 uv;
|
|
${e.varyingVs} vec2 resultUV;
|
|
|
|
void main() {
|
|
gl_Position = vec4(clipSpacePos, 1);
|
|
resultUV = uv;
|
|
}`;return o$(r,t)}function v$(r){let e=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return a$(r,e)}function C$(r){let e=new Uint16Array([0,1,2,2,1,3]);return l$(r,e)}function Zd(r,e,t,o,n,s){c$(e,t);let a=u$(r),i=r.TEXTURE_2D;return Ae(r,()=>r.bindTexture(i,a)),Ae(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_S,r.CLAMP_TO_EDGE)),Ae(r,()=>r.texParameteri(i,r.TEXTURE_WRAP_T,r.CLAMP_TO_EDGE)),Ae(r,()=>r.texParameteri(i,r.TEXTURE_MIN_FILTER,r.NEAREST)),Ae(r,()=>r.texParameteri(i,r.TEXTURE_MAG_FILTER,r.NEAREST)),Ae(r,()=>r.texImage2D(i,0,o,e,t,0,n,s,null)),Ae(r,()=>r.bindTexture(r.TEXTURE_2D,null)),a}function fv(r){return r.internalFormatFloat}function I$(r,e,t,o){let[n,s]=oc(e,t);return Zd(r,n,s,fv(o),o.textureFormatFloat,r.FLOAT)}function dv(r){return r.internalFormatHalfFloat}function N$(r,e,t,o){let[n,s]=oc(e,t);return Zd(r,n,s,dv(o),o.textureFormatFloat,o.textureTypeHalfFloat)}function hv(r){return r.downloadTextureFormat}function S$(r,e,t,o){let[n,s]=oc(e,t);return Zd(r,n,s,hv(o),r.RGBA,r.UNSIGNED_BYTE)}function gv(r){return r.internalFormatPackedFloat}function T$(r,e,t,o){let[n,s]=Hi(e,t);return Zd(r,n,s,gv(o),r.RGBA,r.FLOAT)}function xv(r){return r.internalFormatPackedHalfFloat}function A$(r,e,t,o){let[n,s]=Hi(e,t);return Zd(r,n,s,xv(o),r.RGBA,o.textureTypeHalfFloat)}function E$(r,e,t){let o=0,n=3*4,s=3*4+2*4;return Ae(r,()=>r.bindBuffer(r.ARRAY_BUFFER,t)),tv(r,e,"clipSpacePos",t,3,s,o)&&tv(r,e,"uv",t,2,s,n)}function D$(r,e,t,o,n,s){Ae(r,()=>r.bindTexture(r.TEXTURE_2D,e));let a,i,l;n instanceof Uint8Array?(a=new Uint8Array(t*o*4),i=r.UNSIGNED_BYTE,l=r.RGBA):(a=new Float32Array(t*o*4),i=r.FLOAT,l=s.internalFormatPackedFloat),a.set(n),Ae(r,()=>r.texImage2D(r.TEXTURE_2D,0,l,t,o,0,r.RGBA,i,a)),Ae(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function $$(r,e,t){Ae(r,()=>r.bindTexture(r.TEXTURE_2D,e)),t.data instanceof Uint8Array?Ae(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,t.width,t.height,0,r.RGBA,r.UNSIGNED_BYTE,t.data)):Ae(r,()=>r.texImage2D(r.TEXTURE_2D,0,r.RGBA,r.RGBA,r.UNSIGNED_BYTE,t)),Ae(r,()=>r.bindTexture(r.TEXTURE_2D,null))}function R$(r,e,t,o){let n=r.createBuffer();Ae(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,n));let i=4*4*e*t;return Ae(r,()=>r.bufferData(r.PIXEL_PACK_BUFFER,i,r.STREAM_READ)),Ae(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,0)),Ae(r,()=>r.bindBuffer(r.PIXEL_PACK_BUFFER,null)),n}function F$(r,e,t){let o=r,n=new Float32Array(t);return o.bindBuffer(o.PIXEL_PACK_BUFFER,e),o.getBufferSubData(o.PIXEL_PACK_BUFFER,0,n),o.bindBuffer(o.PIXEL_PACK_BUFFER,null),n}function O$(r,e,t,o){let[n,s]=oc(e,t),a=4,i=new Uint8Array(e$(e*t,a));return Ae(r,()=>r.readPixels(0,0,n,s,o.downloadTextureFormat,r.UNSIGNED_BYTE,i)),new Float32Array(i.buffer)}function P$(r,e,t,o,n,s,a,i){let l=r,u=new Float32Array(t$(s,a));return l.bindBuffer(l.PIXEL_PACK_BUFFER,e),l.getBufferSubData(l.PIXEL_PACK_BUFFER,0,u),l.bindBuffer(l.PIXEL_PACK_BUFFER,null),u}function M$(r,e,t){let o=new Float32Array(e*t*4);return Ae(r,()=>r.readPixels(0,0,t,e,r.RGBA,r.FLOAT,o)),o}var yv=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=W().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,QD(t,e)):this.gl=Go(t);let o="WEBGL_color_buffer_float",n="EXT_color_buffer_half_float";if(W().getNumber("WEBGL_VERSION")===1){let s="OES_texture_float",a="OES_texture_half_float";if(this.textureFloatExtension=Xd(this.gl,s),Wo(this.gl,a))this.textureHalfFloatExtension=Xd(this.gl,a);else if(W().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(o),Wo(this.gl,n))this.colorBufferHalfFloatExtension=Xd(this.gl,n);else if(W().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(o="EXT_color_buffer_float",Wo(this.gl,o))this.colorBufferFloatExtension=this.gl.getExtension(o);else if(Wo(this.gl,n))this.colorBufferHalfFloatExtension=this.gl.getExtension(n);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=v$(this.gl),this.indexBuffer=C$(this.gl),this.framebuffer=p$(this.gl),this.textureConfig=Kd(this.gl,this.textureHalfFloatExtension)}get debug(){return W().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;Ae(e,()=>e.finish()),Ae(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),Ae(e,()=>e.deleteFramebuffer(this.framebuffer)),Ae(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),Ae(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),Ae(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),I$(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),N$(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),S$(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),$$(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,o,n){this.throwIfDisposed(),D$(this.gl,e,t,o,n,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),A$(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),T$(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(rv(this.gl,this.framebuffer),this.outputTexture=null),Ae(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,o){return this.downloadMatrixDriver(e,()=>O$(this.gl,t,o,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,o,n,s,a){return P$(this.gl,e,t,o,n,s,a,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return F$(this.gl,e,t)}createBufferFromTexture(e,t,o){this.bindTextureToFrameBuffer(e);let n=R$(this.gl,t,o,this.textureConfig);return this.unbindTextureToFrameBuffer(),n}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,o;if(W().getBool("WEBGL_FENCE_API_ENABLED")){let n=e,s=n.fenceSync(n.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),o=()=>{let a=n.clientWaitSync(s,0,0);return a===n.ALREADY_SIGNALED||a===n.CONDITION_SATISFIED},t=s}else W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),o=()=>this.isQueryAvailable(t,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):o=()=>!0;return{query:t,isFencePassed:o}}downloadMatrixFromPackedTexture(e,t,o){return this.downloadMatrixDriver(e,()=>M$(this.gl,t,o))}createProgram(e){this.throwIfDisposed();let t=this.gl,o=n$(t,e),n=k$(t),s=s$(t);return Ae(t,()=>t.attachShader(s,n)),Ae(t,()=>t.attachShader(s,o)),i$(t,s),this.debug&&vx(t,s),this.vertexAttrsAreBound||(this.setProgram(s),this.vertexAttrsAreBound=E$(t,this.program,this.vertexBuffer)),s}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&Ae(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&vx(this.gl,this.program),Ae(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,o=!0){return this.throwIfDisposed(),o?m$(this.gl,e,t):f$(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),Ae(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,o){this.throwIfDisposed(),this.throwIfNoProgram(),d$(this.gl,e,t,o)}setOutputMatrixTexture(e,t,o){this.setOutputMatrixTextureDriver(e,o,t)}setOutputPackedMatrixTexture(e,t,o){this.throwIfDisposed();let[n,s]=Hi(t,o);this.setOutputMatrixTextureDriver(e,n,s)}setOutputMatrixWriteRegion(e,t,o,n){this.setOutputMatrixWriteRegionDriver(o,e,n,t)}setOutputPackedMatrixWriteRegion(e,t,o,n){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&vx(this.gl,this.program),Yd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),Ae(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),Ae(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Xd(this.gl,W().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(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.createQuery();return o.beginQuery(n.TIME_ELAPSED_EXT,s),s}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,o=this.getQueryTimerExtensionWebGL2();t.endQuery(o.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await x.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let o=this.gl;return o.getQueryParameter(e,o.QUERY_RESULT)/1e6}else{let o=this.getQueryTimerExtensionWebGL1();return o.getQueryObjectEXT(e,o.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let o=this.gl,n=this.getQueryTimerExtensionWebGL2(),s=o.getQueryParameter(e,o.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),s&&!this.disjoint}else{let o=this.getQueryTimerExtensionWebGL1(),n=o.getQueryObjectEXT(e,o.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(o.GPU_DISJOINT_EXT)),n&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=p8(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:o}=this.itemsToPoll[t];o()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&x.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Cx(this.gl,e,this.framebuffer),this.debug&&Yd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Cx(this.gl,this.outputTexture,this.framebuffer),this.debug&&Yd(this.gl)):rv(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let o=t();return this.unbindTextureToFrameBuffer(),o}setOutputMatrixTextureDriver(e,t,o){this.throwIfDisposed();let n=this.gl;Cx(n,e,this.framebuffer),this.debug&&Yd(n),this.outputTexture=e,Ae(n,()=>n.viewport(0,0,t,o)),Ae(n,()=>n.scissor(0,0,t,o))}setOutputMatrixWriteRegionDriver(e,t,o,n){this.throwIfDisposed(),Ae(this.gl,()=>this.gl.scissor(e,t,o,n))}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 p8(r){let e=0;for(;e<r.length&&r[e]();++e);return e-1}var{getBroadcastDims:L$}=N;function z$(r,e,t,o){let n=[];r.forEach(d=>{let h=x.sizeFromShape(d.shapeInfo.logicalShape);d.shapeInfo.isUniform?n.push(`uniform float ${d.name}${h>1?`[${h}]`:""};`):(n.push(`uniform sampler2D ${d.name};`),n.push(`uniform int offset${d.name};`))});let s=n.join(`
|
|
`),a=r.map(d=>m8(d,e,o)).join(`
|
|
`),i=e.texShape,l=Ot(),u=h8(l),c,p,m=y8(l);return e.isPacked?(c=f8(e.logicalShape,i),p=x8(l)):(c=d8(e.logicalShape,i),p=g8(l)),o&&(m+=b8),[m,u,p,s,c,a,t].join(`
|
|
`)}function Bp(r){let e=r.shapeInfo.logicalShape;switch(e.length){case 0:return w8(r);case 1:return _8(r);case 2:return k8(r);case 3:return v8(r);case 4:return C8(r);case 5:return I8(r);case 6:return N8(r);default:throw new Error(`${e.length}-D input sampling is not yet supported`)}}function B$(r){switch(r.shapeInfo.logicalShape.length){case 0:return S8(r);case 1:return T8(r);case 2:return A8(r);case 3:return E8(r);default:return D8(r)}}function m8(r,e,t=!1){let o="";t?o+=B$(r):o+=Bp(r);let n=r.shapeInfo.logicalShape,s=e.logicalShape;return n.length<=s.length&&(t?o+=$8(r,e):o+=R8(r,e)),o}function f8(r,e){switch(r.length){case 0:return V$();case 1:return F8(r,e);case 2:return M8(r,e);case 3:return O8(r,e);default:return P8(r,e)}}function d8(r,e){switch(r.length){case 0:return V$();case 1:return L8(r,e);case 2:return W8(r,e);case 3:return z8(r,e);case 4:return B8(r,e);case 5:return V8(r,e);case 6:return G8(r,e);default:throw new Error(`${r.length}-D output sampling is not yet supported`)}}function h8(r){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${r.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function g8(r){return`
|
|
void setOutput(float val) {
|
|
${r.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function x8(r){return`
|
|
void setOutput(vec4 val) {
|
|
${r.output} = val;
|
|
}
|
|
`}function y8(r){return`${r.version}
|
|
precision highp float;
|
|
precision highp int;
|
|
precision highp sampler2D;
|
|
${r.varyingFs} vec2 resultUV;
|
|
${r.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;
|
|
${r.defineSpecialNaN}
|
|
${r.defineSpecialInf}
|
|
${r.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);
|
|
}
|
|
|
|
${U8}
|
|
${j8}
|
|
${H8}
|
|
`}var U8=`
|
|
vec2 uvFromFlat(int texNumR, int texNumC, int index) {
|
|
int texR = index / texNumC;
|
|
int texC = index - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
vec2 packedUVfrom1D(int texNumR, int texNumC, int index) {
|
|
int texelIndex = index / 2;
|
|
int texR = texelIndex / texNumC;
|
|
int texC = texelIndex - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,j8=`
|
|
vec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,
|
|
int texNumC, int row, int col) {
|
|
int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);
|
|
int texR = texelIndex / texNumC;
|
|
int texC = texelIndex - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,H8=`
|
|
vec2 packedUVfrom3D(int texNumR, int texNumC,
|
|
int texelsInBatch, int texelsInLogicalRow, int b,
|
|
int row, int col) {
|
|
int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);
|
|
int texR = index / texNumC;
|
|
int texC = index - texR * texNumC;
|
|
return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);
|
|
}
|
|
`,b8=`
|
|
float getChannel(vec4 frag, vec2 innerDims) {
|
|
vec2 modCoord = mod(innerDims, 2.);
|
|
return modCoord.x == 0. ?
|
|
(modCoord.y == 0. ? frag.r : frag.g) :
|
|
(modCoord.y == 0. ? frag.b : frag.a);
|
|
}
|
|
float getChannel(vec4 frag, int dim) {
|
|
float modCoord = mod(float(dim), 2.);
|
|
return modCoord == 0. ? frag.r : frag.g;
|
|
}
|
|
`;function V$(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function F8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];return t[0]===1?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${t[1]}.0);
|
|
}
|
|
`:t[1]===1?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${t[0]}.0);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
return 2 * (resTexRC.x * ${t[1]} + resTexRC.y);
|
|
}
|
|
`}function L8(r,e){return e[0]===1?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * ${e[1]}.0);
|
|
}
|
|
`:e[1]===1?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * ${e[0]}.0);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
return resTexRC.x * ${e[1]} + resTexRC.y;
|
|
}
|
|
`}function O8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[2]/2),n=o*Math.ceil(r[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
int b = index / ${n};
|
|
index -= b * ${n};
|
|
|
|
int r = 2 * (index / ${o});
|
|
int c = imod(index, ${o}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function z8(r,e){let t=zs(["r","c","d"],r);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
${t}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function P8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],o=Math.ceil(r[r.length-1]/2),n=o*Math.ceil(r[r.length-2]/2),s=n,a="",i="b, r, c";for(let l=2;l<r.length-1;l++)s*=r[r.length-l-1],a=`
|
|
int b${l} = index / ${s};
|
|
index -= b${l} * ${s};
|
|
`+a,i=`b${l}, `+i;return`
|
|
ivec${r.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${a}
|
|
|
|
int b = index / ${n};
|
|
index -= b * ${n};
|
|
|
|
int r = 2 * (index / ${o});
|
|
int c = imod(index, ${o}) * 2;
|
|
|
|
return ivec${r.length}(${i});
|
|
}
|
|
`}function B8(r,e){let t=zs(["r","c","d","d2"],r);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
${t}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function V8(r,e){let t=zs(["r","c","d","d2","d3"],r);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${e[0]},
|
|
${e[1]}));
|
|
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
|
|
${t}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function G8(r,e){let t=zs(["r","c","d","d2","d3","d4"],r);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
|
|
${t}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function M8(r,e){let t=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(x.arraysEqual(r,e))return`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`;let o=Math.ceil(r[1]/2);return`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${o});
|
|
int c = imod(index, ${o}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function W8(r,e){return x.arraysEqual(r,e)?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${e[0]}, ${e[1]}));
|
|
}
|
|
`:r[1]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:r[0]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${e[0]}, ${e[1]}));
|
|
int index = resTexRC.x * ${e[1]} + resTexRC.y;
|
|
int r = index / ${r[1]};
|
|
int c = index - r * ${r[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function sc(r){return`offset${r}`}function S8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=Ot();return`
|
|
vec4 ${t}() {
|
|
return ${o.texture2D}(${e}, halfCR);
|
|
}
|
|
`}function w8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`float ${t}() {return ${e};}`;let[o,n]=r.shapeInfo.texShape;if(o===1&&n===1)return`
|
|
float ${t}() {
|
|
return sampleTexture(${e}, halfCR);
|
|
}
|
|
`;let[s,a]=r.shapeInfo.texShape,i=sc(e);return`
|
|
float ${t}() {
|
|
vec2 uv = uvFromFlat(${s}, ${a}, ${i});
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`}function T8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1),o=r.shapeInfo.texShape,n=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],s=Ot();return`
|
|
vec4 ${t}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${n[0]}, ${n[1]}, index);
|
|
return ${s.texture2D}(${e}, uv);
|
|
}
|
|
`}function _8(r){let e=r.name,t="get"+e.charAt(0).toUpperCase()+e.slice(1);if(r.shapeInfo.isUniform)return`
|
|
float ${t}(int index) {
|
|
${Vp(r)}
|
|
}
|
|
`;let o=r.shapeInfo.texShape,n=o[0],s=o[1];if(s===1&&n===1)return`
|
|
float ${t}(int index) {
|
|
return sampleTexture(${e}, halfCR);
|
|
}
|
|
`;let a=sc(e);return s===1?`
|
|
float ${t}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${a}) + 0.5) / ${n}.0);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`:n===1?`
|
|
float ${t}(int index) {
|
|
vec2 uv = vec2((float(index + ${a}) + 0.5) / ${s}.0, 0.5);
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`:`
|
|
float ${t}(int index) {
|
|
vec2 uv = uvFromFlat(${n}, ${s}, index + ${a});
|
|
return sampleTexture(${e}, uv);
|
|
}
|
|
`}function A8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=n[0],a=n[1],i=Ot();if(n!=null&&x.arraysEqual(e,n))return`
|
|
vec4 ${o}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${a}.0, ${s}.0);
|
|
|
|
return ${i.texture2D}(${t}, uv);
|
|
}
|
|
`;let l=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)],u=Math.ceil(e[1]/2);return`
|
|
vec4 ${o}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${u}, ${l[0]}, ${l[1]}, row, col);
|
|
return ${i.texture2D}(${t}, uv);
|
|
}
|
|
`}function k8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape;if(n!=null&&x.arraysEqual(e,n)){let p=n[0],m=n[1];return`
|
|
float ${o}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${m}.0, ${p}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}let{newShape:s,keptDims:a}=x.squeezeShape(e),i=s;if(i.length<e.length){let p=Gp(r,i),m=["row","col"];return`
|
|
${Bp(p)}
|
|
float ${o}(int row, int col) {
|
|
return ${o}(${Wp(m,a)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${e[1]}, 1)));
|
|
${Vp(r)}
|
|
}
|
|
`;let l=n[0],u=n[1],c=sc(t);return u===1?`
|
|
float ${o}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${l}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`:l===1?`
|
|
float ${o}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${c}), vec3(${e[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${u}.0, 0.5);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`:`
|
|
float ${o}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${e[1]} + col + ${c};
|
|
vec2 uv = uvFromFlat(${l}, ${u}, index);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function E8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=r.shapeInfo.texShape,s=[Math.ceil(n[0]/2),Math.ceil(n[1]/2)];if(e[0]===1){let p=e.slice(1),m=[1,2],f=Gp(r,p),d=["b","row","col"];return`
|
|
${B$(f)}
|
|
vec4 ${o}(int b, int row, int col) {
|
|
return ${o}(${Wp(d,m)});
|
|
}
|
|
`}let a=s[0],i=s[1],l=Math.ceil(e[2]/2),u=l*Math.ceil(e[1]/2),c=Ot();return`
|
|
vec4 ${o}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${a}, ${i}, ${u}, ${l}, b, row, col);
|
|
return ${c.texture2D}(${t}, uv);
|
|
}
|
|
`}function v8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[1]*e[2],s=e[2],{newShape:a,keptDims:i}=x.squeezeShape(e),l=a;if(l.length<e.length){let d=Gp(r,l),h=["row","col","depth"];return`
|
|
${Bp(d)}
|
|
float ${o}(int row, int col, int depth) {
|
|
return ${o}(${Wp(h,i)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${n}, ${s}, 1)));
|
|
${Vp(r)}
|
|
}
|
|
`;let u=r.shapeInfo.texShape,c=u[0],p=u[1],m=r.shapeInfo.flatOffset;if(p===n&&m==null)return`
|
|
float ${o}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${s}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${c}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;if(p===s&&m==null)return`
|
|
float ${o}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${e[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${c}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;let f=sc(t);return`
|
|
float ${o}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${n} + col * ${s} + depth + ${f};
|
|
vec2 uv = uvFromFlat(${c}, ${p}, index);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function D8(r){let e=r.shapeInfo.logicalShape,t=e.length,o=r.name,n="get"+o.charAt(0).toUpperCase()+o.slice(1),s=r.shapeInfo.texShape,a=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],i=a[0],l=a[1],u=Math.ceil(e[t-1]/2),c=u*Math.ceil(e[t-2]/2),p="int b, int row, int col",m=`b * ${c} + (row / 2) * ${u} + (col / 2)`;for(let d=2;d<t-1;d++)p=`int b${d}, `+p,c*=e[t-d-1],m=`b${d} * ${c} + `+m;let f=Ot();return`
|
|
vec4 ${n}(${p}) {
|
|
int index = ${m};
|
|
int texR = index / ${l};
|
|
int texC = index - texR * ${l};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${l}, ${i});
|
|
return ${f.texture2D}(${o}, uv);
|
|
}
|
|
`}function C8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[3],s=e[2]*n,a=e[1]*s,{newShape:i,keptDims:l}=x.squeezeShape(e);if(i.length<e.length){let d=Gp(r,i),h=["row","col","depth","depth2"];return`
|
|
${Bp(d)}
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
return ${o}(${Wp(h,l)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${a}, ${s}, ${n}, 1)));
|
|
${Vp(r)}
|
|
}
|
|
`;let u=r.shapeInfo.flatOffset,c=r.shapeInfo.texShape,p=c[0],m=c[1];if(m===a&&u==null)return`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${s}, ${n}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${m}.0, ${p}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;if(m===n&&u==null)return`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${e[1]*e[2]}, ${e[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${m}.0, ${p}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;let f=sc(t);return`
|
|
float ${o}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${a} + col * ${s} +
|
|
depth * ${n} + depth2;
|
|
vec2 uv = uvFromFlat(${p}, ${m}, index + ${f});
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function I8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),n=e[4],s=e[3]*n,a=e[2]*s,i=e[1]*a,{newShape:l,keptDims:u}=x.squeezeShape(e);if(l.length<e.length){let h=Gp(r,l),g=["row","col","depth","depth2","depth3"];return`
|
|
${Bp(h)}
|
|
float ${o}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${o}(${Wp(g,u)});
|
|
}
|
|
`}if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${a}, ${s}, ${n})) +
|
|
depth3;
|
|
${Vp(r)}
|
|
}
|
|
`;let c=r.shapeInfo.flatOffset,p=r.shapeInfo.texShape,m=p[0],f=p[1];if(f===i&&c==null)return`
|
|
float ${o}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${a}, ${s}, ${n}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${m}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;if(f===n&&c==null)return`
|
|
float ${o}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${e[1]*e[2]*e[3]},
|
|
${e[2]*e[3]}, ${e[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${f}.0, ${m}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;let d=sc(t);return`
|
|
float ${o}(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 * ${a} + depth * ${s} +
|
|
depth2 * ${n} + depth3 + ${d};
|
|
vec2 uv = uvFromFlat(${m}, ${f}, index);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function N8(r){let e=r.shapeInfo.logicalShape,t=r.name,o="get"+t.charAt(0).toUpperCase()+t.slice(1),{newShape:n,keptDims:s}=x.squeezeShape(e);if(n.length<e.length){let g=Gp(r,n),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${Bp(g)}
|
|
float ${o}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${o}(${Wp(y,s)});
|
|
}
|
|
`}let a=e[5],i=e[4]*a,l=e[3]*i,u=e[2]*l,c=e[1]*u;if(r.shapeInfo.isUniform)return`
|
|
float ${o}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${c}, ${u}, ${l}, ${i})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${a}, 1)));
|
|
${Vp(r)}
|
|
}
|
|
`;let p=r.shapeInfo.flatOffset,m=r.shapeInfo.texShape,f=m[0],d=m[1];if(d===c&&p==null)return`
|
|
float ${o}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${u}, ${l}, ${i}, ${a})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${d}.0, ${f}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;if(d===a&&p==null)return`
|
|
float ${o}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${e[1]*e[2]*e[3]*e[4]},
|
|
${e[2]*e[3]*e[4]},
|
|
${e[3]*e[4]},
|
|
${e[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${d}.0, ${f}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`;let h=sc(t);return`
|
|
float ${o}(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 * ${c} + col * ${u} + depth * ${l} +
|
|
depth2 * ${i} + depth3 * ${a} + depth4 + ${h};
|
|
vec2 uv = uvFromFlat(${f}, ${d}, index);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function Vp(r){let e=r.name,t=x.sizeFromShape(r.shapeInfo.logicalShape);return t<2?`return ${e};`:`
|
|
for (int i = 0; i < ${t}; i++) {
|
|
if (i == index) {
|
|
return ${e}[i];
|
|
}
|
|
}
|
|
`}function $8(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=r.shapeInfo.logicalShape.length,a=e.logicalShape.length,i=L$(r.shapeInfo.logicalShape,e.logicalShape),l=Le(a),u=a-s,c,p=["x","y","z","w","u","v"];s===0?c="":a<2&&i.length>=1?c="coords = 0;":c=i.map(b=>`coords.${p[b+u]} = 0;`).join(`
|
|
`);let m="";a<2&&s>0?m="coords":m=r.shapeInfo.logicalShape.map((b,w)=>`coords.${p[w+u]}`).join(", ");let f="return outputValue;",h=x.sizeFromShape(r.shapeInfo.logicalShape)===1,y=x.sizeFromShape(e.logicalShape)===1;if(s===1&&!h&&!y)f=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(h&&!y)a===1?f=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:f=`
|
|
return vec4(outputValue.x);
|
|
`;else if(i.length){let b=s-2,w=s-1;i.indexOf(b)>-1&&i.indexOf(w)>-1?f="return vec4(outputValue.x);":i.indexOf(b)>-1?f="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":i.indexOf(w)>-1&&(f="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${n}() {
|
|
${l} coords = getOutputCoords();
|
|
${c}
|
|
vec4 outputValue = get${o}(${m});
|
|
${f}
|
|
}
|
|
`}function R8(r,e){let t=r.name,o=t.charAt(0).toUpperCase()+t.slice(1),n="get"+o+"AtOutCoords",s=e.texShape,a=r.shapeInfo.texShape,i=r.shapeInfo.logicalShape.length,l=e.logicalShape.length;if(!r.shapeInfo.isUniform&&i===l&&r.shapeInfo.flatOffset==null&&x.arraysEqual(a,s))return`
|
|
float ${n}() {
|
|
return sampleTexture(${t}, resultUV);
|
|
}
|
|
`;let u=Le(l),c=L$(r.shapeInfo.logicalShape,e.logicalShape),p=l-i,m,f=["x","y","z","w","u","v"];i===0?m="":l<2&&c.length>=1?m="coords = 0;":m=c.map(h=>`coords.${f[h+p]} = 0;`).join(`
|
|
`);let d="";return l<2&&i>0?d="coords":d=r.shapeInfo.logicalShape.map((h,g)=>`coords.${f[g+p]}`).join(", "),`
|
|
float ${n}() {
|
|
${u} coords = getOutputCoords();
|
|
${m}
|
|
return get${o}(${d});
|
|
}
|
|
`}function Le(r){if(r<=1)return"int";if(r===2)return"ivec2";if(r===3)return"ivec3";if(r===4)return"ivec4";if(r===5)return"ivec5";if(r===6)return"ivec6";throw Error(`GPU for rank ${r} is not yet supported`)}function Gp(r,e){let t=JSON.parse(JSON.stringify(r));return t.shapeInfo.logicalShape=e,t}function Wp(r,e){return e.map(t=>r[t]).join(", ")}function G$(r,e,t,o){let n=e.userCode,s=t.map((f,d)=>{let h={logicalShape:f.shape,texShape:f.isUniform?null:f.texData.texShape,isUniform:f.isUniform,isPacked:f.isUniform?!1:f.texData.isPacked,flatOffset:null};return f.texData!=null&&f.texData.slice!=null&&f.texData.slice.flatOffset>0&&(h.flatOffset=f.texData.slice.flatOffset),{name:e.variableNames[d],shapeInfo:h}}),a=s.map(f=>f.shapeInfo),i={logicalShape:o.shape,texShape:o.texData.texShape,isUniform:!1,isPacked:o.texData.isPacked,flatOffset:null},l=z$(s,i,n,e.packedInputs),u=r.createProgram(l),c=null,p=r.getUniformLocation(u,"NAN",!1);W().getNumber("WEBGL_VERSION")===1&&(c=r.getUniformLocation(u,"INFINITY",!1));let m={};for(let f=0;f<e.variableNames.length;f++){let d=e.variableNames[f],h=!1;m[d]=r.getUniformLocation(u,d,h),m[`offset${d}`]=r.getUniformLocation(u,`offset${d}`,h)}return{program:e,source:l,webGLProgram:u,uniformLocations:m,inShapeInfos:a,outShapeInfo:i,infLoc:c,nanLoc:p}}function W$(r,e){if(r.length!==e.length)throw Error(`Binary was compiled with ${r.length} inputs, but was executed with ${e.length} inputs`);r.forEach((t,o)=>{let n=t.logicalShape,s=e[o],a=s.shape;if(!x.arraysEqual(n,a))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${n} and ${a} must match`);if(t.isUniform&&s.isUniform)return;let i=t.texShape,l=s.isUniform?null:s.texData.texShape;if(!x.arraysEqual(i,l))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${i} and ${l} must match`)})}function U$(r,e,t,o,n){W$(e.inShapeInfos,t),W$([e.outShapeInfo],[o]);let s=o.texData.texture,a=o.texData.texShape;o.texData.isPacked?r.setOutputPackedMatrixTexture(s,a[0],a[1]):r.setOutputMatrixTexture(s,a[0],a[1]),r.setProgram(e.webGLProgram),W().getNumber("WEBGL_VERSION")===1&&e.infLoc!==null&&r.gl.uniform1f(e.infLoc,Infinity),e.nanLoc!==null&&r.gl.uniform1f(e.nanLoc,NaN),t.forEach((i,l)=>{let u=e.program.variableNames[l],c=e.uniformLocations[u],p=e.uniformLocations[`offset${u}`];if(c!=null){if(i.isUniform){if(x.sizeFromShape(i.shape)<2)r.gl.uniform1f(c,i.uniformValues[0]);else{let m=i.uniformValues;m instanceof Float32Array||(m=new Float32Array(m)),r.gl.uniform1fv(c,m)}return}i.texData.slice!=null&&p!=null&&r.gl.uniform1i(p,i.texData.slice.flatOffset),r.setInputMatrixTexture(i.texData.texture,c,l)}}),n!=null&&n(r,e.webGLProgram),r.executeProgram()}function j$(r,e,t){let o="";e.concat(t).forEach(a=>{let i=a.texData!=null&&a.texData.slice!=null&&a.texData.slice.flatOffset>0,l=a.isUniform?"uniform":a.texData.texShape;o+=`${a.shape}_${l}_${i}`});let n=r.userCode,s=r.constructor.name;return s+="_"+o+"_"+n,s}var{addImpl:H$,bincountImpl:Tx,bincountReduceImpl:q$,ceilImpl:K$,concatImpl:X$,expImpl:Y$,expm1Impl:Z$,floorImpl:J$,gatherV2Impl:Q$,greaterImpl:eR,lessImpl:tR,linSpaceImpl:rR,logImpl:oR,maxImpl:nR,maximumImpl:sR,minimumImpl:iR,multiplyImpl:aR,negImpl:lR,prodImpl:uR,rangeImpl:cR,rsqrtImpl:pR,simpleAbsImpl:Ax,sliceImpl:mR,stridedSliceImpl:fR,subImpl:dR,tileImpl:hR,topKImpl:gR,transposeImpl:Up,uniqueImpl:xR}=Bk;function bv(r,e){return["x","y","z","w","u","v"].slice(0,e).map(t=>`${r}.${t}`)}function Ut(r,e){return e===1?[r]:bv(r,e)}function yR(r,e){if(r===1)return"rc";let t="";for(let o=0;o<r;o++)t+=e[o],o<r-1&&(t+=",");return t}var wv=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 o=Ut("rc",t),n=Le(t),s=q8(t,e,o),a=K8(t,e[e.length-1],e[e.length-2],o),i=X8(e,o);this.userCode=`
|
|
void main() {
|
|
${n} rc = getOutputCoords();
|
|
|
|
if(${s}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${a}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}};function Y8(r,e){let t=[];for(let o=0;o<=1;o++)for(let n=0;n<=1;n++){let s=`${o===0?"r":"rp1"}, ${n===0?"c":"cp1"}`;for(let a=2;a<r;a++)s=`${e[e.length-1-a]},`+s;t.push(s)}return t}function q8(r,e,t){if(r===1)return`rc > ${e[0]}`;let o="";for(let n=r-2;n<r;n++)o+=`${t[n]} >= ${e[n]}`,n<r-1&&(o+="||");return o}function K8(r,e,t,o){if(r===1)return"";let n=o.slice(-2);return`
|
|
int r = ${n[0]};
|
|
int c = ${n[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${e};
|
|
bool rEdge = rp1 >= ${t};
|
|
`}function X8(r,e){let t=r.length,o=Y8(t,e);return t===1?`getA(rc),
|
|
rc + 1 >= ${r[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${o[0]}),
|
|
cEdge ? 0. : getA(${o[1]}),
|
|
rEdge ? 0. : getA(${o[2]}),
|
|
rEdge || cEdge ? 0. : getA(${o[3]})`}var Jd=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let o="";for(let n=0;n<4;n++){let s="thisRC = rc;";n%2==1&&(s+="thisRC.z += 1;"),n>1&&(s+="thisRC.y += 1;"),o+=`
|
|
${s}
|
|
${n>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${n}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${n>0?"}":""}
|
|
`}this.userCode=`
|
|
${Z8(t)}
|
|
${zp(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${e[1]};
|
|
int cols = ${e[2]};
|
|
|
|
${o}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function Z8(r){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${zs(["r","c","d"],r)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var _v=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,o){let n=wR(t,o),s=_R(e,n,o);s in this.freeTextures||(this.freeTextures[s]=[]),s in this.usedTextures||(this.usedTextures[s]=[]);let a=bR(e,n,this.gpgpu.gl,this.gpgpu.textureConfig,o);if(this.freeTextures[s].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=a,this.log();let l=this.freeTextures[s].shift();return this.usedTextures[s].push(l),l}let i;return n===_r.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):n===_r.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):n===_r.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):n===_r.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):n===_r.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[s].push(i),this.numUsedTextures++,this._numBytesAllocated+=a,this.log(),i}releaseTexture(e,t,o,n){if(this.freeTextures==null)return;let s=wR(o,n),a=_R(t,s,n);a in this.freeTextures||(this.freeTextures[a]=[]);let i=bR(t,s,this.gpgpu.gl,this.gpgpu.textureConfig,n),l=W().get("WEBGL_DELETE_TEXTURE_THRESHOLD");l!==-1&&this._numBytesAllocated>l?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[a].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[a],c=u.indexOf(e);if(c<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(c,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 J8(r,e){let t=r;if(e===t.R32F)return 4;if(e===t.R16F)return 2;if(e===t.RGBA32F)return 16;if(e===r.RGBA)return 16;if(e===t.RGBA16F)return 8;throw new Error(`Unknown internal format ${e}`)}function bR(r,e,t,o,n){let s=Q8(e,o),a;if(n){let[l,u]=Hi(r[0],r[1]);a=l*u}else{let[l,u]=oc(r[0],r[1]);a=l*u}let i=J8(t,s);return a*i}function Q8(r,e){switch(r){case _r.PACKED_2X2_FLOAT32:return gv(e);case _r.PACKED_2X2_FLOAT16:return xv(e);case _r.UNPACKED_FLOAT32:return fv(e);case _r.UNPACKED_FLOAT16:return dv(e);case _r.PACKED_4X1_UNSIGNED_BYTE:return hv(e);default:throw new Error(`Unknown physical texture type ${r}`)}}function eY(r){return W().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?r?_r.PACKED_2X2_FLOAT32:_r.UNPACKED_FLOAT32:r?_r.PACKED_2X2_FLOAT16:_r.UNPACKED_FLOAT16}function wR(r,e){if(r===Er.UPLOAD)return _r.PACKED_2X2_FLOAT32;if(r===Er.RENDER||r==null)return eY(e);if(r===Er.DOWNLOAD||r===Er.PIXELS)return _r.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${r}`)}function _R(r,e,t){return`${r[0]}_${r[1]}_${e}_${t}`}var ho=class{constructor(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode=`
|
|
float unaryOperation(float x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
float y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},xr="if (isnan(x)) return x;",kR="return x;",kv="return abs(x);";var vR="return (x >= 0.0) ? x : (exp(x) - 1.0);",CR=xr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,IR=xr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Qd="return x;";var NR="return x;",SR=`
|
|
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;
|
|
`,TR=`
|
|
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;
|
|
`,AR=`
|
|
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;
|
|
`,Bs=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}};var vv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,o=Ut("rc",t),n=Le(t),s=yR(t,o),a=o.slice(-2),i=t<=1?"rc":`vec2(${a.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${n} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${s});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}};var tY=Tr.whereImpl,rY=1e-7,oY=1e-4,Ex={};function nY(r){return r in Ex||(Ex[r]={}),Ex[r]}var sY=128,iY=600;function aY(){return W().global.screen==null?1024:W().global.screen.height*W().global.screen.width*window.devicePixelRatio*iY/1024/1024}var jp=class extends Gs{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.warnedAboutCPUBackend=!1,this.pendingDeletes=0,this.disposed=!1,!W().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Go(W().getNumber("WEBGL_VERSION"));this.binaryCache=nY(W().getNumber("WEBGL_VERSION")),this.gpgpu=new yv(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 _v(this.gpgpu),this.numMBBeforeWarning=aY(),this.texData=new Xa(this,Po())}nextDataId(){return jp.nextDataId++}numDataIds(){return this.texData.numDataIds()+(this.cpuBackend?this.cpuBackend.numDataIds():0)-this.pendingDeletes}write(e,t,o){if((W().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||W().getBool("DEBUG"))&&this.checkNumericalProblems(e),o==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let n={id:this.nextDataId()};return this.texData.set(n,{shape:t,dtype:o,values:e,usage:Er.UPLOAD,refCount:1}),n}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,o,n,s){if(W().getBool("DEBUG")&&this.checkNumericalProblems(t),n==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:o,dtype:n,values:t,usage:Er.UPLOAD,refCount:s})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:o,dtype:n,complexTensorInfos:s,slice:a,shape:i,isPacked:l}=t;if(a!=null){let m;l?m=new Bs(i,Qd):m=new ho(i,Qd);let f=this.runWebGLProgram(m,[{dataId:e,shape:i,dtype:n}],n),d=this.readSync(f.dataId);return this.disposeIntermediateTensorInfo(f),d}if(o!=null)return this.convertAndCacheOnCPU(e);if(n==="string")return o;let u=this.activeTimers!=null,c;u&&(c=x.now());let p;if(n==="complex64"){let m=this.readSync(s.real.dataId),f=this.readSync(s.imag.dataId);p=N.mergeRealAndImagArrays(m,f)}else p=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=x.now()-c),this.convertAndCacheOnCPU(e,p)}async read(e){if(this.pendingRead.has(e)){let d=this.pendingRead.get(e);return new Promise(h=>d.push(h))}let t=this.texData.get(e),{values:o,shape:n,slice:s,dtype:a,complexTensorInfos:i,isPacked:l}=t;if(s!=null){let d;l?d=new Bs(n,Qd):d=new ho(n,Qd);let h=this.runWebGLProgram(d,[{dataId:e,shape:n,dtype:a}],a),g=this.read(h.dataId);return this.disposeIntermediateTensorInfo(h),g}if(o!=null)return this.convertAndCacheOnCPU(e);if(!W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&W().getNumber("WEBGL_VERSION")===2)throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");let u=null,c;if(a!=="complex64"&&W().get("WEBGL_BUFFER_SUPPORTED")){c=this.decode(e);let d=this.texData.get(c.dataId);u=this.gpgpu.createBufferFromTexture(d.texture,...Cl(n))}this.pendingRead.set(e,[]),a!=="complex64"&&await this.gpgpu.createAndWaitForFence();let p;if(a==="complex64"){let d=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),h=d[0],g=d[1];p=N.mergeRealAndImagArrays(h,g)}else if(u==null)p=this.getValuesFromTexture(e);else{let d=x.sizeFromShape(n);p=this.gpgpu.downloadFloat32MatrixFromBuffer(u,d)}c!=null&&this.disposeIntermediateTensorInfo(c);let m=this.convertAndCacheOnCPU(e,p),f=this.pendingRead.get(e);return this.pendingRead.delete(e),f.forEach(d=>d(m)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&Po().removeDataId(e,this),this.pendingDeletes--),m}bufferSync(e){let t=this.readSync(e.dataId),o=t;if(e.dtype==="string")try{o=t.map(n=>x.decodeString(n))}catch(n){throw new Error("Failed to decode encoded string bytes into utf-8")}return ve(e.shape,e.dtype,o)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let o=e[t];if(!r$(o))throw W().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${o} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${o} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:o,isPacked:n}=this.texData.get(e),s=x.sizeFromShape(t);if(W().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let m=this.decode(e),f=this.texData.get(m.dataId),d=this.gpgpu.downloadMatrixFromPackedTexture(f.texture,...Cl(t)).subarray(0,s);return this.disposeIntermediateTensorInfo(m),d}let a=W().getBool("WEBGL_PACK")&&n===!0,i=a?Ix(t):t,l=a?new cv(i):new uv(i),u=this.runWebGLProgram(l,[{shape:i,dtype:o,dataId:e}],"float32"),c=this.texData.get(u.dataId),p=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(c.texture,c.texShape[0],c.texShape[1]).subarray(0,s);return this.disposeIntermediateTensorInfo(u),p}timerAvailable(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,o=[],n=!1;this.programTimersStack==null?(this.programTimersStack=o,n=!0):this.activeTimers.push(o),this.activeTimers=o,e();let s=x.flatten(this.activeTimers.map(l=>l.query)).filter(l=>l!=null),a=x.flatten(this.activeTimers.map(l=>l.name)).filter(l=>l!=null);this.activeTimers=t,n&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let l=await Promise.all(s);i.kernelMs=x.sum(l),i.getExtraProfileInfo=()=>l.map((u,c)=>({name:a[c],ms:u})).map(u=>`${u.name}: ${u.ms}`).join(", ")}else i.kernelMs={error:"WebGL query timers are not supported in this environment."};return this.uploadWaitMs=0,this.downloadWaitMs=0,i}memory(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU,numBytesInGPUAllocated:this.textureManager.numBytesAllocated,numBytesInGPUFree:this.textureManager.numBytesFree}}startTimer(){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:x.now(),endMs:null}}endTimer(e){return W().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=x.now(),e)}async getQueryTime(e){if(W().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:o}=this.texData.get(e);return o!=null&&(this.disposeData(o.real.dataId,t),this.disposeData(o.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:o,texShape:n,usage:s,isPacked:a,slice:i}=this.texData.get(e),l=i&&i.origDataId||e,u=this.dataRefCount.get(l);u>1?this.dataRefCount.set(l,u-1):(this.dataRefCount.delete(l),t!=null&&(this.numBytesInGPU-=this.computeBytes(n,o),this.textureManager.releaseTexture(t,n,s,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)}getCPUBackend(){return W().getBool("WEBGL_CPU_FORWARD")?(this.cpuBackend==null&&(this.cpuBackend=Po().findBackend("cpu")),this.cpuBackend):null}shouldExecuteOnCPU(e,t=sY){let o=this.getCPUBackend();return!W().getBool("IS_TEST")&&!this.warnedAboutCPUBackend&&o==null&&(console.warn("Your application contains ops that are small enough to be executed on the CPU backend, however the CPU backend cannot be found. Consider importing the CPU backend (@tensorflow/tfjs-backend-cpu) for better performance."),this.warnedAboutCPUBackend=!0),o!=null&&e.every(n=>this.texData.get(n.dataId).texture==null&&x.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){N.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return tY(e.shape,t)}packedUnaryOp(e,t,o){let n=new Bs(e.shape,t),s=this.compileAndRun(n,[e],o);return Po().makeTensorFromDataId(s.dataId,s.shape,s.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let n=Ax(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,n)}if(W().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,kv,e.dtype);let t=new ho(e.shape,kv),o=this.compileAndRun(t,[e]);return Po().makeTensorFromDataId(o.dataId,o.shape,o.dtype)}makeTensorInfo(e,t,o){let n;if(t==="string"&&o!=null&&o.length>0&&x.isString(o[0])){let s=o.map(a=>x.encodeString(a));n=this.write(s,e,t)}else n=this.write(o,e,t);return this.texData.get(n).usage=null,{dataId:n,shape:e,dtype:t}}makeOutput(e,t,o){let{dataId:n}=this.makeTensorInfo(e,t,o);return Po().makeTensorFromDataId(n,e,t,this)}unpackTensor(e){let t=new vv(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new wv(e.shape),o=!0;return this.runWebGLProgram(t,[e],e.dtype,null,o)}packedReshape(e,t){let o=[Il(e.shape),...Nl(e.shape)],n={dtype:e.dtype,shape:o,dataId:e.dataId},s=[Il(t),...Nl(t)],a=new Jd(s,o),i=!0,l=this.runWebGLProgram(a,[n],e.dtype,null,i);return{dataId:l.dataId,shape:t,dtype:l.dtype}}decode(e){let t=this.texData.get(e),{isPacked:o,shape:n,dtype:s}=t,a=Ix(n),i;o?i=new lv(a):i=new av(a);let l=!0,u=this.runWebGLProgram(i,[{shape:a,dtype:s,dataId:e}],s,null,l);return{dtype:s,shape:n,dataId:u.dataId}}runWebGLProgram(e,t,o,n,s=!1){let a=this.makeTensorInfo(e.outputShape,o),i=this.texData.get(a.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===vl.DENSE){let g=Cl(e.outputShape);i.texShape=g.map(y=>y*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),x.sizeFromShape(a.shape)===0)return i.values=x.getTypedArrayFromDType(a.dtype,0),a;let l=[],u=t.map(g=>{if(g.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let y=this.texData.get(g.dataId);if(y.texture==null){if(!e.packedInputs&&x.sizeFromShape(g.shape)<=W().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:g.shape,texData:null,isUniform:!0,uniformValues:y.values};e.packedInputs&&(y.isPacked=!0,y.shape=g.shape)}else if(!!y.isPacked!=!!e.packedInputs)g=y.isPacked?this.unpackTensor(g):this.packTensor(g),l.push(g),y=this.texData.get(g.dataId);else if(y.isPacked&&!nc(y.shape,g.shape)){let b=g,w=g.shape;g.shape=y.shape,g=this.packedReshape(g,w),l.push(g),y=this.texData.get(g.dataId),b.shape=w}return this.uploadToGPU(g.dataId),{shape:g.shape,texData:y,isUniform:!1}});this.uploadToGPU(a.dataId);let c={shape:a.shape,texData:i,isUniform:!1},p=j$(e,u,c),m=this.getAndSaveBinary(p,()=>G$(this.gpgpu,e,u,c)),f=this.activeTimers!=null,d;f&&(d=this.startTimer()),U$(this.gpgpu,m,u,c,n),l.forEach(g=>this.disposeIntermediateTensorInfo(g)),f&&(d=this.endTimer(d),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(d)}));let h=W().get("WEBGL_FLUSH_THRESHOLD");if(h>0){let g=x.now();g-this.lastGlFlushTime>h&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=g)}if(!W().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&s===!1){let g=this.unpackTensor(a);return this.disposeIntermediateTensorInfo(a),g}return a}compileAndRun(e,t,o,n,s=!1){return o=o||t[0].dtype,this.runWebGLProgram(e,t,o,n,s)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(W().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=V(()=>{if(!W().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=W().getBool("DEBUG");W().set("DEBUG",!1);let t=this.abs(ue(1e-8)).dataSync()[0];if(W().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?rY:oY}uploadToGPU(e){let t=this.texData.get(e),{shape:o,dtype:n,values:s,texture:a,usage:i,isPacked:l}=t;if(a!=null)return;let u=this.activeTimers!=null,c;u&&(c=x.now());let p=t.texShape;if(p==null&&(p=h$(o,l),t.texShape=p),s!=null){let m=Ix(o),f,d=p[1],h=p[0],g=s instanceof Uint8Array;l?([d,h]=Hi(p[0],p[1]),f=new mv(m,[h,d],g)):f=new pv(m,[h,d],g);let y=this.makeTensorInfo([h,d],n);g?this.texData.get(y.dataId).usage=Er.PIXELS:this.texData.get(y.dataId).usage=Er.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(y.dataId),d,h,s);let b=!0,w=this.runWebGLProgram(f,[y],n,null,b),_=this.texData.get(w.dataId);t.texture=_.texture,t.texShape=_.texShape,t.isPacked=_.isPacked,t.usage=_.usage,this.disposeIntermediateTensorInfo(y),this.texData.delete(w.dataId),t.values=null,u&&(this.uploadWaitMs+=x.now()-c)}else{let m=this.acquireTexture(p,i,n,l);t.texture=m}}convertAndCacheOnCPU(e,t){let o=this.texData.get(e),{dtype:n}=o;return this.releaseGPUData(e),t!=null&&(o.values=lY(t,n)),o.values}acquireTexture(e,t,o,n){if(this.numBytesInGPU+=this.computeBytes(e,o),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let s=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${s} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,n)}computeBytes(e,t){return e[0]*e[1]*x.bytesPerElement(t)}};jp.nextDataId=0;function lY(r,e){if(e==="float32"||e==="complex64")return r;if(e==="int32"||e==="bool"){let t=e==="int32"?new Int32Array(r.length):new Uint8Array(r.length);for(let o=0;o<t.length;++o)t[o]=Math.round(r[o]);return t}else throw new Error(`Unknown dtype ${e}`)}var ER="3.2.0";Lc.isBrowser()&&hu("webgl",()=>new jp,2);var Dx=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`;var rs=class{constructor(e,t,o){this.variableNames=["A","B"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}};var Sl=`
|
|
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;
|
|
`;var Vs=class{constructor(e,t,o,n=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=N.assertAndGetBroadcastShape(t,o);let s=this.outputShape.length,a="";if(n)if(s===0||x.sizeFromShape(this.outputShape)===1)a=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(a=`
|
|
${Le(s)} coords = getOutputCoords();
|
|
`,s===1)a+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let l=Ut("coords",s);a+=`
|
|
bool nextRowOutOfBounds =
|
|
(${l[s-2]} + 1) >= ${this.outputShape[s-2]};
|
|
bool nextColOutOfBounds =
|
|
(${l[s-1]} + 1) >= ${this.outputShape[s-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 jt(r){let{inputs:e,backend:t}=r,{x:o}=e;return t.incRef(o.dataId),{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}var DR={kernelName:Ro,backendName:"webgl",kernelFunc:jt};function go(r){let{inputs:e,backend:t}=r,{real:o,imag:n}=e,s=t.makeTensorInfo(o.shape,"complex64"),a=t.texData.get(s.dataId),i=jt({inputs:{x:o},backend:t}),l=jt({inputs:{x:n},backend:t});return a.complexTensorInfos={real:i,imag:l},s}var $R={kernelName:Gl,backendName:"webgl",kernelFunc:go};var Cv="return (a < 0.) ? b * a : a;",Iv=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function uY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{alpha:s}=o,a=t.makeTensorInfo([],"float32",x.createScalarValue(s,"float32")),i=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Vs(Iv,n.shape,a.shape):new rs(Cv,n.shape,a.shape),l=t.runWebGLProgram(i,[n,a],n.dtype);return t.disposeIntermediateTensorInfo(a),l}var RR={kernelName:un,backendName:"webgl",kernelFunc:uY};var Nv="return (a < 0.) ? b * a : a;",Sv=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function cY(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Vs(Sv,o.shape,n.shape):new rs(Nv,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)}var FR={kernelName:_n,backendName:"webgl",kernelFunc:cY};var $x="if (isnan(x)) return x;",OR=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,PR=`
|
|
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 _e({opSnippet:r,packedOpSnippet:e,cpuKernelImpl:t,dtype:o}){return({inputs:n,backend:s})=>{let{x:a}=n,i=s,l=o||a.dtype;if(i.shouldExecuteOnCPU([a])&&t!=null){let p=i.texData.get(a.dataId),m=t(p.values,l);return i.makeTensorInfo(a.shape,l,m)}let u=W().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&e!=null,c;return u?c=new Bs(a.shape,e):c=new ho(a.shape,r),i.runWebGLProgram(c,[a],l)}}function nt({opSnippet:r,packedOpSnippet:e,checkOutOfBounds:t=!1,supportsComplex:o=!1,cpuKernelImpl:n,dtype:s}){return({inputs:a,backend:i})=>{let{a:l,b:u}=a,c=i;if(o&&l.dtype==="complex64"){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,y]=[[d.complexTensorInfos.real,h.complexTensorInfos.real],[d.complexTensorInfos.imag,h.complexTensorInfos.imag]].map(w=>{let[_,C]=w,D={dataId:_.dataId,dtype:_.dtype,shape:l.shape},T={dataId:C.dataId,dtype:C.dtype,shape:u.shape},R=new rs(r,l.shape,u.shape);return c.runWebGLProgram(R,[D,T],fr(_.dtype,C.dtype))}),b=go({inputs:{real:g,imag:y},backend:c});return c.disposeIntermediateTensorInfo(g),c.disposeIntermediateTensorInfo(y),b}let p=s||fr(l.dtype,u.dtype);if(c.shouldExecuteOnCPU([l,u])&&n!=null){let d=c.texData.get(l.dataId),h=c.texData.get(u.dataId),[g,y]=n(l.shape,u.shape,d.values,h.values,p),b=c.makeTensorInfo(y,p),w=c.texData.get(b.dataId);return w.values=g,b}let m=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&e!=null,f;return m?f=new Vs(e,l.shape,u.shape,t):f=new rs(r,l.shape,u.shape),c.runWebGLProgram(f,[l,u],p)}}function Tl(r,e=!1){if(r==="linear")return e?NR:kR;if(r==="relu")return e?TR:CR;if(r==="elu")return e?SR:vR;if(r==="relu6")return e?AR:IR;if(r==="prelu")return e?Sv:Nv;if(r==="leakyrelu")return e?Iv:Cv;throw new Error(`Activation ${r} has not been implemented for the WebGL backend.`)}var eh=class{constructor(e,t,o,n=!1,s=!1,a=!1,i=null,l=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=o;let c=n?e[1]:e[2],p=Math.ceil(c/2),m=n?"i * 2, rc.y":"rc.y, i * 2",f=s?"rc.z, i * 2":"i * 2, rc.z",d=n?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],h=s?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],g="",y="";i&&(l?g=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${i}
|
|
}`:u?g=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${i}
|
|
}`:g=`vec4 activation(vec4 x) {
|
|
${i}
|
|
}`,y="result = activation(result);");let b=a?"result += getBiasAtOutCoords();":"";a&&this.variableNames.push("bias"),l&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let w="rc.x",_="rc.x";e[0]<t[0]?w=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(_=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${g}
|
|
|
|
const float sharedDimension = ${p}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${p}; i++) {
|
|
int batchA = ${w};
|
|
int batchB = ${_};
|
|
vec4 a = getMatrixA(batchA, ${m});
|
|
vec4 b = getMatrixB(batchB, ${f});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${d[0]} * ${h[0]});
|
|
result += (${d[1]} * ${h[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${b}
|
|
|
|
${y}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};var Tv={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Rx=class{constructor(e,t,o){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=N.assertAndGetBroadcastShape(t,o),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}};var MR="return a * b;";function Av(r){let{inputs:e,backend:t}=r,{a:o,b:n}=e,s=N.upcastType(o.dtype,n.dtype);if(o.dtype==="complex64"){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),u=new Rx(Tv.REAL,o.shape,n.shape),c=new Rx(Tv.IMAG,o.shape,n.shape),p=[{dataId:i.complexTensorInfos.real.dataId,dtype:i.complexTensorInfos.real.dtype,shape:o.shape},{dataId:i.complexTensorInfos.imag.dataId,dtype:i.complexTensorInfos.imag.dtype,shape:o.shape},{dataId:l.complexTensorInfos.real.dataId,dtype:l.complexTensorInfos.real.dtype,shape:n.shape},{dataId:l.complexTensorInfos.imag.dataId,dtype:l.complexTensorInfos.imag.dtype,shape:n.shape}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=go({inputs:{real:m,imag:f},backend:t});return t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}if(t.shouldExecuteOnCPU([o,n])){let i=t.texData.get(o.dataId),l=t.texData.get(n.dataId),[u,c]=aR(o.shape,n.shape,i.values,l.values,s),p=t.makeTensorInfo(c,s),m=t.texData.get(p.dataId);return m.values=u,p}let a;return W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?a=new Vs(MR,o.shape,n.shape):a=new rs(MR,o.shape,n.shape),t.runWebGLProgram(a,[o,n],s)}var LR={kernelName:xn,backendName:"webgl",kernelFunc:Av};function zR(r,e,t){let o=[Il(r.shape),...Nl(r.shape)],n={dtype:r.dtype,shape:o,dataId:r.dataId},s=[Il(e),...Nl(e)],a=new Jd(s,o),i=!0,l=t.runWebGLProgram(a,[n],r.dtype,null,i);return{dataId:l.dataId,shape:e,dtype:l.dtype}}function pe(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{shape:s}=o,a=t,i=x.sizeFromShape(n.shape),l=x.inferFromImplicitShape(s,i),u=x.sizeFromShape(l);x.assert(i===u,()=>`The new shape (${l}) has ${u} elements and the old shape (${n.shape}) has ${i} elements. The new shape and old shape must have the same number of elements.`);let c=a.texData.get(n.dataId);return c.isPacked&&!nc(n.shape,l)&&!(c.texture!==null&&nc(c.shape,l))?zR(n,l,a):(a.incRef(n.dataId),{dataId:n.dataId,shape:l,dtype:n.dtype})}var BR={kernelName:ds,backendName:"webgl",kernelFunc:pe};var Fx=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i=Math.floor(o/4)*4,l=o%4,u="sumValue += dot(values, ones);";if(t!=null){let p=1/t;u=`sumValue += dot(values * ${x.isInt(p)?p.toPrecision(2):p}, ones);`}let c="";s%o>0&&(c=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
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 * ${o};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${i}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${u}
|
|
}
|
|
|
|
int inIdx = inOffset + ${i};
|
|
if (${l===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${u}
|
|
} else if (${l===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${u}
|
|
} else if (${l===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${u}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}};var Ev=class{constructor(e,t){this.variableNames=["x"];let{windowSize:o,batchSize:n,inSize:s,outSize:a}=e;this.outputShape=[n,a];let i="0.0",l="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",l="min"):t==="max"&&(i="-1.0 / 1e-20",l="max");let u=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="sum"?u="sumValue":t==="prod"?u="prodValue":t==="all"?u="allValue":t==="any"&&(u="anyValue");let c=Math.floor(o/4)*4,p=o%4,m=`
|
|
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 = ${l}(values, minMaxValue);
|
|
}
|
|
`,f="vec4";t==="all"?(i="1.0",m=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,f="bvec4"):t==="any"&&(i="0.0",m=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,f="bvec4");let d="";s%o>0&&(d=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
return initializationValue;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${i};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${d}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${o};
|
|
|
|
vec4 minMaxValue = vec4(${i});
|
|
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;
|
|
${f} values = ${f}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${m}
|
|
}
|
|
|
|
int inIdx = inOffset + ${c};
|
|
if (${p===1}) {
|
|
${f} values = ${f}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${m}
|
|
} else if (${p===2}) {
|
|
${f} values = ${f}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${m}
|
|
} else if (${p===3}) {
|
|
${f} values = ${f}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${m}
|
|
}
|
|
setOutput(${u});
|
|
}
|
|
`}};function pY(r){let e=[];for(;e.length===0||e[e.length-1].outSize!==1;){let t=e.length?e[e.length-1].outSize:r[1],o=N.computeOptimalWindowSize(t);e.push({inSize:t,windowSize:o,outSize:Math.ceil(t/o)})}return e}function To(r,e,t,o){let n=pY(r.shape),s=r;for(let a=0;a<n.length;a++){let{inSize:i,windowSize:l,outSize:u}=n[a],c,p;t==="mean"?c=a===0?new Fx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},i):new Fx({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u}):c=new Ev({windowSize:l,inSize:i,batchSize:r.shape[0],outSize:u},t),p=s,s=o.runWebGLProgram(c,[s],e),p.dataId!==r.dataId&&o.disposeIntermediateTensorInfo(p)}return s}var Dv=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[t[a]];this.outputShape=o,this.rank=o.length;let n=Le(this.rank),s=mY(t);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function mY(r){let e=r.length;if(e>6)throw Error(`Transpose for rank ${e} is not yet supported`);let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],o=new Array(e);for(let n=0;n<r.length;n++)o[r[n]]=t[n];return o.join()}var $v=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let o=new Array(e.length);for(let c=0;c<o.length;c++)o[c]=e[t[c]];if(this.outputShape=o,this.rank=o.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let n=Le(this.rank),s=bv("rc",this.rank),a=new Array(this.rank);for(let c=0;c<t.length;c++)a[t[c]]=s[c];let i=`vec2(${a.slice(-2).join()})`,l=`++${s[this.rank-1]} < ${o[this.rank-1]}`,u=`getChannel(getA(${a.join()}), ${i})`;this.userCode=`
|
|
void main() {
|
|
${n} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${u};
|
|
if(${l}) {
|
|
result[1] = ${u};
|
|
}
|
|
--${s[this.rank-1]};
|
|
if(++${s[this.rank-2]} < ${o[this.rank-2]}) {
|
|
result[2] = ${u};
|
|
if(${l}) {
|
|
result[3] = ${u};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Al(r,e,t){let o=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $v(r.shape,e):new Dv(r.shape,e);return t.runWebGLProgram(o,[r],r.dtype)}function VR(r,e,t,o){let n=e,s=r.shape.length,a=x.parseAxisParam(n,r.shape),i=a,l=N.getAxesPermutation(i,s),u=l!=null,c=r;u&&(c=Al(r,l,o),i=N.getInnerMostAxes(i.length,s)),N.assertAxesAreInnerMostDims("sum",i,s);let[p,m]=N.computeOutAndReduceShapes(c.shape,i),f=p;t&&(f=N.expandShapeToKeepDim(p,a));let d=x.sizeFromShape(m),g=x.sizeFromShape(r.shape)/d,y=pe({inputs:{x:c},attrs:{shape:[g,d]},backend:o}),b=mu(r.dtype),w=To(y,b,"sum",o),_=pe({inputs:{x:w},attrs:{shape:f},backend:o});return o.disposeIntermediateTensorInfo(y),o.disposeIntermediateTensorInfo(w),u&&o.disposeIntermediateTensorInfo(c),_}function th(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o;return VR(n,s,a,t)}var GR={kernelName:Dn,backendName:"webgl",kernelFunc:th};function Mt(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{perm:s}=o,a=t,i=n.shape.length,l=new Array(i);for(let c=0;c<l.length;c++)l[c]=n.shape[s[c]];let u;if(a.shouldExecuteOnCPU([n])){let p=a.texData.get(n.dataId).values,m=Up(p,n.shape,n.dtype,s,l);u=a.makeTensorInfo(l,n.dtype);let f=a.texData.get(u.dataId);f.values=m}else u=Al(n,s,a);return u}var WR={kernelName:Pn,backendName:"webgl",kernelFunc:Mt};var Rv=1e3;function ic({a:r,b:e,transposeA:t,transposeB:o,backend:n,bias:s=null,preluActivationWeights:a=null,leakyreluAlpha:i=0,activation:l=null}){let u=r.shape.length,c=e.shape.length,p=t?r.shape[u-2]:r.shape[u-1],m=o?e.shape[c-1]:e.shape[c-2],f=t?r.shape[u-1]:r.shape[u-2],d=o?e.shape[c-2]:e.shape[c-1],h=r.shape.slice(0,-2),g=e.shape.slice(0,-2),y=x.sizeFromShape(h),b=x.sizeFromShape(g),w=y===b||y===1||b===1;x.assert(u>=2&&c>=2&&w,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${h}) and (${g}).`);let C=(y>b?r.shape.slice(0,-2):e.shape.slice(0,-2)).concat([f,d]);x.assert(p===m,()=>`Error in matMul: inner shapes (${p}) and (${m}) of Tensors with shapes ${r.shape} and ${e.shape} and transposeA=${t} and transposeB=${o} must match.`);let D=t?[y,p,f]:[y,f,p],T=o?[b,d,m]:[b,m,d],R=pe({inputs:{x:r},backend:n,attrs:{shape:D}}),P=pe({inputs:{x:e},backend:n,attrs:{shape:T}}),B=[R,P],G=Math.max(y,b),U=t?R.shape[1]:R.shape[2],j=s!=null,H=a!=null,K=l==="leakyrelu",X=l!=null?Tl(l,!0):null,oe=j||H||K||X!=null,Y;if((f===1||d===1)&&U>Rv&&oe===!1){let te=R,ie=P;t&&(te=Mt({inputs:{x:R},backend:n,attrs:{perm:[0,2,1]}}),B.push(te)),o&&(ie=Mt({inputs:{x:P},backend:n,attrs:{perm:[0,2,1]}}),B.push(ie));let le=d!==1,ae=d===1,fe=te;le&&(fe=pe({inputs:{x:te},backend:n,attrs:{shape:[G,U,1]}}),B.push(fe));let de=d===1?2:1,xe=ie;ae&&(xe=pe({inputs:{x:ie},backend:n,attrs:{shape:[G,1,U]}}),B.push(xe));let we=Av({inputs:{a:fe,b:xe},backend:n});Y=th({inputs:{x:we},backend:n,attrs:{axis:de,keepDims:!0}}),B.push(we)}else{let te=fr(r.dtype,e.dtype),ie=new eh(D,T,[G,f,d],t,o,j,X,H,K),le=[R,P];if(s!=null&&le.push(s),H&&le.push(a),K){let ae=n.makeTensorInfo([],"float32",x.createScalarValue(i,"float32"));le.push(ae),B.push(ae)}Y=n.runWebGLProgram(ie,le,te)}let re=pe({inputs:{x:Y},backend:n,attrs:{shape:C}});B.push(Y);for(let te of B)n.disposeIntermediateTensorInfo(te);return re}function fY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e,{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o;return ic({a:n,b:s,transposeA:l,transposeB:u,backend:t,bias:a,preluActivationWeights:i,leakyreluAlpha:p,activation:c})}var UR={kernelName:ws,backendName:"webgl",kernelFunc:fY};var jR="return abs(x);";function dY(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])&&o.dtype!=="complex64"){let s=t.texData.get(o.dataId),a=Ax(s.values);return t.makeTensorInfo(o.shape,o.dtype,a)}let n;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Bs(o.shape,jR):n=new ho(o.shape,jR),t.runWebGLProgram(n,[o],o.dtype)}var HR={kernelName:as,backendName:"webgl",kernelFunc:dY};var hY=xr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,gY=_e({opSnippet:hY}),qR={kernelName:js,backendName:"webgl",kernelFunc:gY};var xY=xr+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,yY=_e({opSnippet:xY}),KR={kernelName:Hs,backendName:"webgl",kernelFunc:yY};var XR="return a + b;",bY=nt({opSnippet:XR,packedOpSnippet:XR,supportsComplex:!0,cpuKernelImpl:H$}),YR={kernelName:wo,backendName:"webgl",kernelFunc:bY};var Fv=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`float v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${o.join(`
|
|
`)}
|
|
|
|
float result = ${n};
|
|
setOutput(result);
|
|
}
|
|
`}};var Ov=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((s,a)=>`T${a}`);let o=[];this.variableNames.forEach(s=>{o.push(`vec4 v${s} = get${s}AtOutCoords();`)});let n=this.variableNames.map(s=>`v${s}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${o.join(`
|
|
`)}
|
|
|
|
vec4 result = ${n};
|
|
setOutput(result);
|
|
}
|
|
`}};function Ox(r){let{inputs:e,backend:t}=r,o=e;if(o.length===1)return jt({inputs:{x:o[0]},backend:t});if(o.length>W().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let l=Math.floor(o.length/2),u=Ox({inputs:o.slice(0,l),backend:t}),c=Ox({inputs:o.slice(l),backend:t});return Ox({inputs:[u,c],backend:t})}let n=o.map(l=>l.dtype).reduce((l,u)=>fr(l,u)),s=o.map(l=>l.shape),i=W().getBool("WEBGL_PACK")?new Ov(o[0].shape,s):new Fv(o[0].shape,s);return t.runWebGLProgram(i,o,n)}var ZR={kernelName:Ho,backendName:"webgl",kernelFunc:Ox};function wY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=x.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Mt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("all",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=x.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=To(h,h.dtype,"all",t),y;if(a){let b=N.expandShapeToKeepDim(m,l);y=pe({inputs:{x:g},backend:t,attrs:{shape:b}})}else y=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),y}var JR={kernelName:Ml,backendName:"webgl",kernelFunc:wY};function _Y(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=x.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Mt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,i)),N.assertAxesAreInnerMostDims("any",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=x.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=To(h,h.dtype,"any",t),y;if(a){let b=N.expandShapeToKeepDim(m,l);y=pe({inputs:{x:g},backend:t,attrs:{shape:b}})}else y=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),y}var QR={kernelName:Ll,backendName:"webgl",kernelFunc:_Y};var Pv=class{constructor(e,t,o){this.variableNames=["A"];let{windowSize:n,batchSize:s,outSize:a}=e;o||this.variableNames.push("bestIndicesA"),this.outputShape=[s,a];let i=t==="max"?">":"<",l=o?"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 * ${n};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${n}; i++) {
|
|
int inIdx = ${l};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}};var Mv=class{constructor(e,t,o,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,x.assert(e.length>2,()=>`Packed arg${o.charAt(0).toUpperCase()+o.slice(1)} supports only inputs with rank above 2.`);let s=e[e.length-1],a=Math.ceil(s/t);this.outputShape=e.slice(0,-1),a>1&&this.outputShape.push(a),n||this.variableNames.push("bestIndicesA");let i=this.outputShape,l=i.length,u=Le(l),c=Ut("coords",l),p,m;if(a===1){m=l+1;let R=Le(m);p=`
|
|
${R} sourceLocR = ${R}(${c.join()}, 0);
|
|
++${c[l-1]};
|
|
${R} sourceLocG = ${R}(${c.join()}, 0);
|
|
++${c[l-2]};
|
|
${R} sourceLocA = ${R}(${c.join()}, 0);
|
|
--${c[l-1]};
|
|
${R} sourceLocB = ${R}(${c.join()}, 0);
|
|
--${c[l-2]};`}else m=l,p=`
|
|
${u} sourceLocR = coords;
|
|
++${c[l-1]};
|
|
${u} sourceLocG = coords;
|
|
++${c[l-2]};
|
|
${u} sourceLocA = coords;
|
|
--${c[l-1]};
|
|
${u} sourceLocB = coords;
|
|
--${c[l-2]};`;let f=["x","y","z","w","u","v"].slice(0,m),d="."+f[m-1],h=f.map(R=>"int "+R),g=Ut("sourceLocR",m-1).concat("inIdx.r"),y=Ut("sourceLocG",m-1).concat("inIdx.g"),b=Ut("sourceLocB",m-1).concat("inIdx.b"),w=Ut("sourceLocA",m-1).concat("inIdx.a"),_=o==="max"?"greaterThan":"lessThan",C=n?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${g.join()}),
|
|
getBestIndicesAChannel(${y.join()}),
|
|
getBestIndicesAChannel(${b.join()}),
|
|
getBestIndicesAChannel(${w.join()})));`,D=`vec4(
|
|
getAChannel(${g.join()}),
|
|
hasNextCol ? getAChannel(${y.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${b.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${w.join()}) : 0.)`,T=n?"":`
|
|
float getBestIndicesAChannel(${h.join()}) {
|
|
return getChannel(getBestIndicesA(${f.join()}),
|
|
vec2(${f.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${h.join()}) {
|
|
return getChannel(getA(${f.join()}),
|
|
vec2(${f.slice(-2).join()}));
|
|
}
|
|
${T}
|
|
void main() {
|
|
${u} coords = getOutputCoords();
|
|
bool hasNextCol = ${c[l-1]} < ${i[l-1]-1};
|
|
bool hasNextRow = ${c[l-2]} < ${i[l-2]-1};
|
|
${p}
|
|
ivec4 srcIdx = ivec4(sourceLocR${d}, sourceLocG${d},
|
|
sourceLocB${d}, sourceLocA${d}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${D};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${C}
|
|
vec4 candidate = ${D};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${_}(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 eF(r,e,t,o=null){let n=e.shape[0],s=e.shape[1];o!=null&&(n=o.shape[0],s=o.shape[1]);let a=N.computeOptimalWindowSize(s),i={windowSize:a,inSize:s,batchSize:n,outSize:Math.ceil(s/a)},l=new Pv(i,t,o==null),u=[e];o!=null&&u.push(o);let c=r.runWebGLProgram(l,u,"int32");if(c.shape[1]===1)return c;let p=eF(r,e,t,c);return r.disposeIntermediateTensorInfo(c),p}function tF(r,e,t,o=null){let n=o!=null?o.shape:e.shape,s=n[n.length-1],a=N.computeOptimalWindowSize(s),i=new Mv(n,a,t,o==null),l=o==null?[e]:[e,o],u=r.runWebGLProgram(i,l,"int32");if(u.shape.length===e.shape.length){let c=tF(r,e,t,u);return r.disposeIntermediateTensorInfo(u),c}return u}function Px(r,e,t,o){let n=[t];if(N.assertAxesAreInnerMostDims("arg"+o.charAt(0).toUpperCase()+o.slice(1),n,e.shape.length),!W().getBool("WEBGL_PACK_REDUCE")||e.shape.length<=2){let s=[],[a,i]=N.computeOutAndReduceShapes(e.shape,n),l=x.sizeFromShape(i),u=pe({inputs:{x:e},backend:r,attrs:{shape:[-1,l]}});s.push(u);let c=eF(r,u,o);s.push(c);let p=pe({inputs:{x:c},backend:r,attrs:{shape:a}});return s.forEach(m=>r.disposeIntermediateTensorInfo(m)),p}return tF(r,e,o)}function kY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=x.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Mt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMax",[a[0]],l.shape.length);let c=Px(t,l,a[0],"max");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var rF={kernelName:qo,backendName:"webgl",kernelFunc:kY};function vY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s}=o,a=x.parseAxisParam(s,n.shape),i=N.getAxesPermutation(a,n.shape.length),l=n,u=[];i!=null&&(l=Mt({inputs:{x:n},backend:t,attrs:{perm:i}}),u.push(l),a=N.getInnerMostAxes(a.length,l.shape.length)),N.assertAxesAreInnerMostDims("argMin",[a[0]],l.shape.length);let c=Px(t,l,a[0],"min");return u.forEach(p=>t.disposeIntermediateTensorInfo(p)),c}var oF={kernelName:ea,backendName:"webgl",kernelFunc:vY};var CY=xr+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,IY=_e({opSnippet:CY}),nF={kernelName:qs,backendName:"webgl",kernelFunc:IY};var NY=xr+"return log(x + sqrt(x * x + 1.0));",SY=_e({opSnippet:NY}),sF={kernelName:Ks,backendName:"webgl",kernelFunc:SY};var TY=xr+`
|
|
return atan(x);
|
|
`,AY=_e({opSnippet:TY}),iF={kernelName:Xs,backendName:"webgl",kernelFunc:AY};var EY=OR+`
|
|
return atan(a, b);
|
|
`,DY=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+PR+`
|
|
return result;
|
|
`,$Y=nt({opSnippet:EY,packedOpSnippet:DY}),aF={kernelName:Zs,backendName:"webgl",kernelFunc:$Y};var RY=xr+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,FY=_e({opSnippet:RY}),lF={kernelName:Ys,backendName:"webgl",kernelFunc:FY};var Ki=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideHeight,l=e.strideWidth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.top,d=e.padInfo.left;this.outputShape=e.outShape;let h=t==="avg",g=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,y=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,b="0.0";if(h||(b="-1.0 / 1e-20"),o){let R=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${l});
|
|
const ivec2 pads = ivec2(${f}, ${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
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
float avgValue = 0.0;
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${m};
|
|
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 ${R} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${n?s?g:y:`wR * ${m} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let w="max",_=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(_="avgValue / count");let C=Math.floor(a/4)*4,D=a%4,T=`
|
|
if (${h}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${w}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${l});
|
|
const ivec2 pads = ivec2(${f}, ${d});
|
|
const float initializationValue = ${b};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${b});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${C}; wC += 4) {
|
|
int xC = xCCorner + wC * ${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)
|
|
);
|
|
|
|
${T}
|
|
}
|
|
|
|
int xC = xCCorner + ${C};
|
|
if (${D===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${T}
|
|
} else if (${D===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${T}
|
|
} else if (${D===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${c}, d),
|
|
getValue(batch, xR, xC + 2 * ${c}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${T}
|
|
}
|
|
}
|
|
setOutput(${_});
|
|
}
|
|
`}},ac=class{constructor(e,t,o,n=!1,s=!1){if(this.variableNames=["x"],t==="avg"&&o)throw new Error("Cannot compute positions for average pool.");let a=e.filterWidth,i=e.strideDepth,l=e.strideHeight,u=e.strideWidth,c=e.dilationDepth,p=e.dilationHeight,m=e.dilationWidth,f=e.effectiveFilterDepth,d=e.effectiveFilterHeight,h=e.effectiveFilterWidth,g=e.padInfo.front,y=e.padInfo.top,b=e.padInfo.left;this.outputShape=e.outShape;let w=t==="avg",_="0.0";if(w||(_="-1.0 / 1e-20"),o){let B=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${l}, ${u});
|
|
const ivec3 pads = ivec3(${g}, ${y}, ${b});
|
|
|
|
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 < ${f};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${p}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${h};
|
|
wC += ${m}) {
|
|
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 ${B} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${n?s?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${d} * ${h} +
|
|
wR * ${h} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let C="max",D=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(D="avgValue / count");let T=Math.floor(a/4)*4,R=a%4,P=`
|
|
if (${w}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${C}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${l}, ${u});
|
|
const ivec3 pads = ivec3(${g}, ${y}, ${b});
|
|
const float initializationValue = ${_};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${_});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${f};
|
|
wD += ${c}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${p}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${T}; wC += 4) {
|
|
int xC = xCCorner + wC * ${m};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${m}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${m}, ch)
|
|
);
|
|
|
|
${P}
|
|
}
|
|
|
|
int xC = xCCorner + ${T};
|
|
if (${R===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${P}
|
|
} else if (${R===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${m}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${P}
|
|
} else if (${R===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${m}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${m}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${P}
|
|
}
|
|
}
|
|
setOutput(${D});
|
|
}
|
|
}
|
|
`}};function OY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;qi(n,"avgPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;x.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&x.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new Ki(c,"avg",!1);return t.runWebGLProgram(p,[n],"float32")}var uF={kernelName:Ko,backendName:"webgl",kernelFunc:OY};function PY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dimRoundingMode:l,dataFormat:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,l,u),m=new ac(p,"avg",!1);return t.runWebGLProgram(m,[n],"float32")}var cF={kernelName:ta,backendName:"webgl",kernelFunc:PY};var Lv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=l-1-e.padInfo.top,p=u-1-e.padInfo.left,m=1/(t*o);this.userCode=`
|
|
const ivec2 pads = ivec2(${c}, ${p});
|
|
const float avgMultiplier = float(${m});
|
|
|
|
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 < ${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 < ${u};
|
|
wC+= ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},zv=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.effectiveFilterDepth,m=e.effectiveFilterHeight,f=e.effectiveFilterWidth,d=p-1-e.padInfo.front,h=m-1-e.padInfo.top,g=f-1-e.padInfo.left,y=1/(t*o*n);this.userCode=`
|
|
const ivec3 pads = ivec3(${d}, ${h}, ${g});
|
|
const float avgMultiplier = float(${y});
|
|
|
|
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 < ${p};
|
|
wD += ${l}) {
|
|
float dyD = float(dyDCorner + wD) / ${s}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${m};
|
|
wR += ${u}) {
|
|
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 < ${f};
|
|
wC += ${c}) {
|
|
float dyC = float(dyCCorner + wC) / ${i}.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 MY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new zv(m);return t.runWebGLProgram(f,[n],a.dtype)}var pF={kernelName:Bl,backendName:"webgl",kernelFunc:MY};function LY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s;qi([n,s],"avgPoolGrad");let{filterSize:i,strides:l,pad:u}=o,c=N.computePool2DInfo(a.shape,i,l,1,u),p=new Lv(c);return t.runWebGLProgram(p,[n],a.dtype)}var mF={kernelName:zl,backendName:"webgl",kernelFunc:LY};function zY(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s}=e,{transposeA:a,transposeB:i}=o;return ic({a:n,b:s,transposeA:a,transposeB:i,backend:t})}var fF={kernelName:Xo,backendName:"webgl",kernelFunc:zY};var Bv=class{constructor(e,t,o,n,s,a){this.outputShape=[],this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="0.0";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="1.0";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${i};
|
|
float scale = ${l};
|
|
float inv = scale * inversesqrt(variance + float(${a}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}};var Vv=class{constructor(e,t,o,n,s,a){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],N.assertAndGetBroadcastShape(e,t),N.assertAndGetBroadcastShape(e,o);let i="vec4(0.0)";n!=null&&(N.assertAndGetBroadcastShape(e,n),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let l="vec4(1.0)";s!=null&&(N.assertAndGetBroadcastShape(e,s),this.variableNames.push("scale"),l="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${i};
|
|
vec4 scale = ${l};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${a}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}};var BY=({inputs:r,backend:e,attrs:t})=>{let{x:o,mean:n,variance:s,offset:a,scale:i}=r;x.assert(n.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),x.assert(a==null||n.shape.length===a.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),x.assert(i==null||n.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:l}=t;l==null&&(l=.001);let u=[o,n,s],c=null;a!=null&&(c=a.shape,u.push(a));let p=null;i!=null&&(p=i.shape,u.push(i));let m=W().getBool("WEBGL_PACK_NORMALIZATION")?new Vv(o.shape,n.shape,s.shape,c,p,l):new Bv(o.shape,n.shape,s.shape,c,p,l);return e.runWebGLProgram(m,u,u[0].dtype)},dF={kernelName:an,backendName:"webgl",kernelFunc:BY};var Gv=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=`uniform int start[${this.rank}];`,n=VY(this.rank),s,a=e.map((i,l)=>`sourceLoc.${Wv[l]} = start[${l}] + coords.${Wv[l]};`);s=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${a.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
${o}
|
|
void main() {
|
|
${s}
|
|
setOutput(getSource(${n}));
|
|
}
|
|
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},Wv=["x","y","z","w","u","v"];function VY(r){if(r===1)return"sourceLoc";if(r<=6)return Wv.slice(0,r).map(e=>"sourceLoc."+e).join(",");throw Error(`Slicing for rank ${r} is not yet supported`)}var Uv=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=Le(this.rank),o=Ut("coords",this.rank),n=Ut("sourceLoc",this.rank),s=this.rank===1?"sourceLoc":`vec2(${n.slice(-2).join()})`,a=`getChannel(getSource(${n.join()}), ${s})`,i=`
|
|
result.x = ${a};
|
|
if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${n[this.rank-1]};
|
|
result.y = ${a};
|
|
--${n[this.rank-1]};
|
|
}
|
|
`,l=this.rank===1?"":`
|
|
--${o[this.rank-1]};
|
|
if (++${o[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${n[this.rank-2]};
|
|
result.z = ${a};
|
|
if (++${o[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${n[this.rank-1]};
|
|
result.w = ${a};
|
|
}
|
|
}
|
|
`,u=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((c,p)=>`start[${p}]`).join()});`:e.map((c,p)=>`${n[p]} = ${o[p]} + start[${p}];`).join(`
|
|
`);this.userCode=`
|
|
uniform int start[${this.rank}];
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${u}
|
|
vec4 result = vec4(0.);
|
|
${i}
|
|
${l}
|
|
setOutput(result);
|
|
}
|
|
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,o)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(o,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function GY(r,e,t,o){let n=o.texData.get(r.dataId),s=o.makeTensorInfo(t,r.dtype),a=o.texData.get(s.dataId);Object.assign(a,n),a.refCount=1,a.shape=t,a.dtype=r.dtype;let i=nr.computeFlatOffset(e,x.computeStrides(r.shape));n.slice&&(i+=n.slice.flatOffset),a.slice={flatOffset:i,origDataId:n.slice&&n.slice.origDataId||r.dataId};let l=o.dataRefCount.get(a.slice.origDataId)||1;return o.dataRefCount.set(a.slice.origDataId,l+1),s}function Va(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,size:a}=o,[i,l]=nr.parseSliceParams(n,s,a);if(nr.assertParamsValid(n,i,l),x.sizeFromShape(l)===0)return t.makeTensorInfo(l,n.dtype,[]);if(t.shouldExecuteOnCPU([n])||n.dtype==="string"){let p=t.texData.get(n.dataId),m=mR(p.values,i,l,n.shape,n.dtype);return t.makeTensorInfo(l,n.dtype,m)}let{isPacked:u}=t.texData.get(n.dataId),c=nr.isSliceContinous(n.shape,i,l);if(u||!c){let p=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Uv(l):new Gv(l),m=p.getCustomSetupFunc(i);return t.runWebGLProgram(p,[n],n.dtype,m)}return t.uploadToGPU(n.dataId),GY(n,i,l,t)}var hF={kernelName:gs,backendName:"webgl",kernelFunc:Va};var WY=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,crops:a}=o;x.assert(n.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((b,w)=>b*w),l=N.getReshaped(n.shape,s,i),u=N.getPermuted(l.length,s.length),c=N.getReshapedPermuted(n.shape,s,i),p=N.getSliceBeginCoords(a,s.length),m=N.getSliceSize(c,a,s.length),f=[],d=pe({inputs:{x:n},backend:t,attrs:{shape:l}}),h=Mt({inputs:{x:d},backend:t,attrs:{perm:u}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:c}}),y=Va({inputs:{x:g},backend:t,attrs:{begin:p,size:m}});return f.push(d),f.push(h),f.push(g),f.forEach(b=>t.disposeIntermediateTensorInfo(b)),y},gF={kernelName:ra,backendName:"webgl",kernelFunc:WY};function UY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a}=o,i=t.readSync(n.dataId),l=t.readSync(s.dataId),u=Tx(i,l,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,u)}var xF={kernelName:Vl,backendName:"webgl",kernelFunc:UY};var jY="return float(a != b);",jv=nt({opSnippet:jY,dtype:"bool"}),yF={kernelName:gi,backendName:"webgl",kernelFunc:jv};function Ga(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.real},backend:t})}var bF={kernelName:iu,backendName:"webgl",kernelFunc:Ga};var HY="return float(int(x));";function wF(r,e){let t=new ho(r.shape,HY),o=e.runWebGLProgram(t,[r],"int32");return{dataId:o.dataId,shape:o.shape,dtype:o.dtype}}function Hv(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dtype:s}=o;if(s==="complex64"){if(n.dtype==="complex64")return jt({inputs:{x:n},backend:t});let a=ht(n.shape),i=Hv({inputs:{x:n},backend:t,attrs:{dtype:"float32"}}),l=go({inputs:{real:i,imag:a},backend:t});return a.dispose(),t.disposeIntermediateTensorInfo(i),l}if(n.dtype==="complex64"){let a=Ga({inputs:{input:n},backend:t}),i=Hv({inputs:{x:a},backend:t,attrs:{dtype:s}});return t.disposeIntermediateTensorInfo(a),i}if(!x.hasEncodingLoss(n.dtype,s)){let a=jt({inputs:{x:n},backend:t});return{dataId:a.dataId,shape:a.shape,dtype:s}}if(s==="int32")return wF(n,t);if(s==="bool"){let a=t.makeTensorInfo([],"bool",x.getTypedArrayFromDType("bool",1)),l=jv({inputs:{a:n,b:a},backend:t});return t.disposeIntermediateTensorInfo(a),l}throw new Error(`Error in Cast: failed to cast ${n.dtype} to ${s}`)}var _F={kernelName:Do,backendName:"webgl",kernelFunc:Hv};var kF="return ceil(x);",qY=_e({opSnippet:kF,packedOpSnippet:kF,cpuKernelImpl:K$}),vF={kernelName:Yo,backendName:"webgl",kernelFunc:qY};var qv=class{constructor(e){this.variableNames=["A"],this.outputShape=e,this.userCode=`
|
|
uniform float minVal;
|
|
uniform float maxVal;
|
|
|
|
void main() {
|
|
float value = getAAtOutCoords();
|
|
if (isnan(value)) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, minVal, maxVal));
|
|
}
|
|
`}getCustomSetupFunc(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};var Kv=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
uniform float minVal;
|
|
uniform float maxVal;
|
|
|
|
void main() {
|
|
vec4 value = getAAtOutCoords();
|
|
|
|
if (any(isnan(value))) {
|
|
setOutput(value);
|
|
return;
|
|
}
|
|
|
|
setOutput(clamp(value, vec4(minVal), vec4(maxVal)));
|
|
}
|
|
`}getCustomSetupFunc(e,t){return(o,n)=>{this.minLoc==null&&(this.minLoc=o.getUniformLocationNoThrow(n,"minVal"),this.maxLoc=o.getUniformLocationNoThrow(n,"maxVal")),o.gl.uniform1f(this.minLoc,e),o.gl.uniform1f(this.maxLoc,t)}}};function KY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{clipValueMin:s,clipValueMax:a}=o,i;W().getBool("WEBGL_PACK_CLIP")?i=new Kv(n.shape):i=new qv(n.shape);let l=i.getCustomSetupFunc(s,a);return t.runWebGLProgram(i,[n],n.dtype,l)}var CF={kernelName:$o,backendName:"webgl",kernelFunc:KY};var Xv=class{constructor(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float re = abs(getRealAtOutCoords());
|
|
float im = abs(getImagAtOutCoords());
|
|
float mx = max(re, im);
|
|
|
|
// sadly the length function in glsl is not underflow-safe
|
|
// (at least not on Intel GPUs). So the safe solution is
|
|
// to ensure underflow-safety in all cases.
|
|
setOutput(
|
|
mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))
|
|
);
|
|
}
|
|
`}};function IF(r,e){return{dataId:e.dataId,dtype:e.dtype,shape:r.shape}}function XY(r){let{inputs:e,backend:t}=r,{x:o}=e,n=t.texData.get(o.dataId),s=new Xv(o.shape),a=[IF(o,n.complexTensorInfos.real),IF(o,n.complexTensorInfos.imag)];return t.runWebGLProgram(s,a,a[0].dtype)}var NF={kernelName:oa,backendName:"webgl",kernelFunc:XY};var Yv=class{constructor(e){this.outputShape=[],this.outputShape=N.computeOutShape(e,1),this.variableNames=e.map((a,i)=>`T${i}`);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 o=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let a=1;a<t.length;a++){let i=t[a-1];o.push(`else if (yC < ${t[a]}) setOutput(getT${a}(yR, yC-${i}));`)}let n=t.length,s=t[t.length-1];o.push(`else setOutput(getT${n}(yR, yC-${s}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${o.join(`
|
|
`)}
|
|
}
|
|
`}};var Zv=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=N.computeOutShape(e,t);let o=this.outputShape,n=o.length,s=Le(n),a=Ut("coords",n),i=["x","y","z","w","u","v"].slice(0,n);this.variableNames=e.map((h,g)=>`T${g}`);let l=new Array(e.length-1);l[0]=e[0][t];for(let h=1;h<l.length;h++)l[h]=l[h-1]+e[h][t];let u=i[t],c=i.slice(-2),p=i.join(),m=`if (${u} < ${l[0]}) {
|
|
return getChannel(
|
|
getT0(${p}), vec2(${c.join()}));
|
|
}`;for(let h=1;h<l.length;h++){let g=l[h-1];m+=`
|
|
if (${u} < ${l[h]} && ${u} >= ${l[h-1]}) {
|
|
return getChannel(
|
|
getT${h}(${Mx(i,u,g)}),
|
|
vec2(${Mx(c,u,g)}));
|
|
}`}let f=l.length,d=l[l.length-1];m+=`
|
|
return getChannel(
|
|
getT${f}(${Mx(i,u,d)}),
|
|
vec2(${Mx(c,u,d)}));`,this.userCode=`
|
|
float getValue(${i.map(h=>"int "+h)}) {
|
|
${m}
|
|
}
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${a}), 0., 0., 0.);
|
|
|
|
${a[n-1]} = ${a[n-1]} + 1;
|
|
if (${a[n-1]} < ${o[n-1]}) {
|
|
result.g = getValue(${a});
|
|
}
|
|
|
|
${a[n-2]} = ${a[n-2]} + 1;
|
|
if (${a[n-2]} < ${o[n-2]}) {
|
|
result.a = getValue(${a});
|
|
}
|
|
|
|
${a[n-1]} = ${a[n-1]} - 1;
|
|
if (${a[n-2]} < ${o[n-2]} &&
|
|
${a[n-1]} < ${o[n-1]}) {
|
|
result.b = getValue(${a});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Mx(r,e,t){let o=r.indexOf(e);return r.map((s,a)=>a===o?`${s} - ${t}`:s).join()}function lc(r){let{inputs:e,backend:t}=r,{input:o}=e,n=t.texData.get(o.dataId);return jt({inputs:{x:n.complexTensorInfos.imag},backend:t})}var SF={kernelName:Ql,backendName:"webgl",kernelFunc:lc};function uc(r,e,t){let o=r[0].dtype;if(o==="complex64"){let u=r.map(d=>Ga({inputs:{input:d},backend:t})),c=r.map(d=>lc({inputs:{input:d},backend:t})),p=uc(u,e,t),m=uc(c,e,t),f=go({inputs:{real:p,imag:m},backend:t});return u.forEach(d=>t.disposeIntermediateTensorInfo(d)),c.forEach(d=>t.disposeIntermediateTensorInfo(d)),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),f}if(o==="string"){let{tensors2D:u,outShape:c}=TF(r,e,t),p=u.map(g=>({vals:t.readSync(g.dataId),shape:g.shape})),m=u[0].shape[0]===1,f=X$(p,c,o,m),d=N.computeOutShape(r.map(g=>g.shape),e),h=t.makeTensorInfo(d,o,f);return u.forEach(g=>t.disposeIntermediateTensorInfo(g)),h}if(r.length>W().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let u=Math.floor(r.length/2),c=uc(r.slice(0,u),e,t),p=uc(r.slice(u),e,t),m=uc([c,p],e,t);return t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),m}if(W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&r[0].shape.length>1){let u=new Zv(r.map(c=>c.shape),e);return t.runWebGLProgram(u,r,o)}let{tensors2D:n,outShape:s}=TF(r,e,t),a=new Yv(n.map(u=>u.shape)),i=t.runWebGLProgram(a,n,o);n.forEach(u=>t.disposeIntermediateTensorInfo(u));let l=pe({inputs:{x:i},attrs:{shape:s},backend:t});return t.disposeIntermediateTensorInfo(i),l}function TF(r,e,t){let o=N.computeOutShape(r.map(s=>s.shape),e);return{tensors2D:r.map(s=>pe({inputs:{x:s},attrs:{shape:[-1,x.sizeFromShape(s.shape.slice(e))]},backend:t})),outShape:o}}function Jv(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o,s=x.parseAxisParam(n,e[0].shape)[0],a=N.computeOutShape(e.map(u=>u.shape),s);if(x.sizeFromShape(a)===0)return t.makeTensorInfo(a,e[0].dtype,[]);let i=e.filter(u=>x.sizeFromShape(u.shape)>0);if(i.length===1)return jt({inputs:{x:i[0]},backend:t});let l=i.map(u=>u.shape);return N.assertParamsConsistent(l,s),uc(i,s,t)}var AF={kernelName:ls,backendName:"webgl",kernelFunc:Jv};var rh=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.padInfo.top,i=e.padInfo.left,l=e.strideHeight,u=e.strideWidth,c=e.dilationHeight,p=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4,g=e.dataFormat==="channelsLast",y=g?1:2,b=g?2:3,w=g?3:1,_="",C="";o&&(n?_=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${o}
|
|
}`:s?_=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${o}
|
|
}`:_=`
|
|
float activation(float x) {
|
|
${o}
|
|
}
|
|
`,C="result = activation(result);");let D=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${_}
|
|
|
|
const ivec2 strides = ivec2(${l}, ${u});
|
|
const ivec2 pads = ivec2(${a}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${w}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${y}], coords[${b}]) * 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 < ${m}; wR++) {
|
|
int xR = xRCorner + wR * ${c};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f}; wC++) {
|
|
int xC = xCCorner + wC * ${p};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${d}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${g}) {
|
|
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 (${h===1}) {
|
|
|
|
if (${g}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${d}) *
|
|
getW(wR, wC, ${d}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${d}, xR, xC) *
|
|
getW(wR, wC, ${d}, d2);
|
|
}
|
|
|
|
} else if (${h===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${d}, d2),
|
|
getW(wR, wC, ${d} + 1, d2)
|
|
);
|
|
|
|
if (${g}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xR, xC, ${d}),
|
|
getX(batch, xR, xC, ${d} + 1)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec2 xValues = vec2(
|
|
getX(batch, ${d}, xR, xC),
|
|
getX(batch, ${d} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${h===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${d}, d2),
|
|
getW(wR, wC, ${d} + 1, d2),
|
|
getW(wR, wC, ${d} + 2, d2)
|
|
);
|
|
|
|
if (${g}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xR, xC, ${d}),
|
|
getX(batch, xR, xC, ${d} + 1),
|
|
getX(batch, xR, xC, ${d} + 2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec3 xValues = vec3(
|
|
getX(batch, ${d}, xR, xC),
|
|
getX(batch, ${d} + 1, xR, xC),
|
|
getX(batch, ${d} + 2, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${D}
|
|
${C}
|
|
setOutput(result);
|
|
}
|
|
`}},Qv=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,o=e.padInfo.top,n=e.padInfo.left,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=e.dilationDepth,u=e.dilationHeight,c=e.dilationWidth,p=e.filterDepth,m=e.filterHeight,f=e.filterWidth,d=Math.floor(e.inChannels/4)*4,h=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${s}, ${a}, ${i});
|
|
const ivec3 pads = ivec3(${t}, ${o}, ${n});
|
|
|
|
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 < ${p}; wF++) {
|
|
int xF = xFCorner + wF * ${l};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${m}; wR++) {
|
|
int xR = xRCorner + wR * ${u};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${f}; wC++) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${d}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${h===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${d}) *
|
|
getW(wF, wR, wC, ${d}, d2);
|
|
} else if (${h===2}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xF, xR, xC, ${d}),
|
|
getX(batch, xF, xR, xC, ${d} + 1)
|
|
);
|
|
vec2 wValues = vec2(
|
|
getW(wF, wR, wC, ${d}, d2),
|
|
getW(wF, wR, wC, ${d} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${h===3}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xF, xR, xC, ${d}),
|
|
getX(batch, xF, xR, xC, ${d} + 1),
|
|
getX(batch, xF, xR, xC, ${d} + 2)
|
|
);
|
|
vec3 wValues = vec3(
|
|
getW(wF, wR, wC, ${d}, d2),
|
|
getW(wF, wR, wC, ${d} + 1, d2),
|
|
getW(wF, wR, wC, ${d} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};var eC=class{constructor(e,t,o){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:n,inChannels:s,strideWidth:a,strideHeight:i,padInfo:l,outWidth:u,dilationWidth:c,dilationHeight:p,dataFormat:m}=o,{left:f,top:d}=l,h=s*n,g=Ot(),y=m==="channelsLast",b=y?0:1,w=y?1:2,_="";for(let C=0;C<=1;C++)for(let D=0;D<=1;D++)_+=`
|
|
blockIndex = rc.y + ${D};
|
|
pos = rc.x + ${C};
|
|
|
|
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
|
|
offsetY = int(blockIndex / (${u})) * ${i} - ${d};
|
|
d0 = offsetY + ${p} * (pos / ${h});
|
|
|
|
if(d0 < ${t[b]} && d0 >= 0) {
|
|
|
|
offsetX = int(mod(float(blockIndex), ${u}.) * ${a}. - ${f}.);
|
|
d1 = offsetX + ${c} * (int(mod(float(pos), ${h}.) / ${s}.));
|
|
|
|
if(d1 < ${t[w]} && d1 >= 0) {
|
|
|
|
ch = int(mod(float(pos), ${s}.));
|
|
|
|
if (${y}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${C*2+D}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${C*2+D}] = 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;
|
|
|
|
${_}
|
|
|
|
${g.output} = result;
|
|
}
|
|
`}};function Lx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let l=r.shape,u=o.texData.get(r.dataId),c=t.inChannels,p=l[0]*l[1]*l[2],m=t.outChannels,f=t.dataFormat==="channelsLast",d=!1,h=!1,g,y=[],b=(p===1||m===1)&&c>Rv,w=l[2]%2!=0&&!!u.isPacked;if(b||!W().getBool("WEBGL_LAZILY_UNPACK")||!W().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!w){let _=f?l[0]*l[1]*l[2]:l[0]*l[2]*l[3],C=pe({inputs:{x:r},backend:o,attrs:{shape:[1,_,t.inChannels]}}),D=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}}),T=ic({a:C,b:D,transposeA:d,transposeB:h,backend:o,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a});g=pe({inputs:{x:T},backend:o,attrs:{shape:t.outShape}}),y.push(C),y.push(D),y.push(T)}else{let _=f?l[0]*l[1]*(l[2]+1):l[0]*l[2]*(l[3]+1),C={dataId:r.dataId,shape:[1,_,t.inChannels],dtype:r.dtype},D=u.shape;u.shape=u.shape.slice(),u.shape[u.shape.length-2]++,x.assert(nc(u.shape,C.shape),()=>`packed reshape ${u.shape} to ${C.shape} isn't free`);let T=pe({inputs:{x:e},backend:o,attrs:{shape:[1,t.inChannels,t.outChannels]}});y.push(T);let R=ic({a:C,b:T,backend:o,transposeA:d,transposeB:h,bias:n,activation:i,preluActivationWeights:s,leakyreluAlpha:a}),P=o.texData.get(R.dataId);x.assert(P.isPacked,()=>"batchMatMul result is expected to be packed"),u.shape=D,P.shape=t.outShape,g=jt({inputs:{x:R},backend:o}),g.shape=t.outShape,y.push(R)}for(let _ of y)o.disposeIntermediateTensorInfo(_);return g}function zx({x:r,filter:e,convInfo:t,backend:o,bias:n=null,preluActivationWeights:s=null,leakyreluAlpha:a=0,activation:i=null}){let{filterWidth:l,filterHeight:u,inChannels:c,outWidth:p,outHeight:m,dataFormat:f}=t,d=f==="channelsLast",h=l*u*c,g=m*p,y=[h,g],b=!0,w=!1,_=[],C=pe({inputs:{x:r},backend:o,attrs:{shape:r.shape.slice(1)}}),D=pe({inputs:{x:e},backend:o,attrs:{shape:[1,h,x.sizeFromShape(e.shape)/h]}});_.push(C),_.push(D);let T=new eC(y,C.shape,t),R=o.runWebGLProgram(T,[C],"float32"),P=pe({inputs:{x:R},backend:o,attrs:{shape:[1,y[0],y[1]]}});_.push(R),_.push(P);let B=n!=null,G=s!=null,U=i==="leakyrelu",j=i?Tl(i,!0):null,H=new eh(P.shape,D.shape,[1,g,t.outChannels],b,w,B,j,G,U),K=[P,D];if(n&&K.push(n),G&&K.push(s),U){let re=o.makeTensorInfo([],"float32",x.createScalarValue(a,"float32"));K.push(re),_.push(re)}let X=o.runWebGLProgram(H,K,"float32"),oe=d?[1,m,p,t.outChannels]:[1,t.outChannels,m,p],Y=pe({inputs:{x:X},backend:o,attrs:{shape:oe}});_.push(X);for(let re of _)o.disposeIntermediateTensorInfo(re);return Y}function YY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dataFormat:l,dilations:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,s.shape,a,u,i,c,!1,p),f;if(m.filterHeight===1&&m.filterWidth===1&&m.dilationHeight===1&&m.dilationWidth===1&&m.strideHeight===1&&m.strideWidth===1&&(m.padInfo.type==="SAME"||m.padInfo.type==="VALID"))f=Lx({x:n,filter:s,convInfo:m,backend:t});else if(W().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)f=zx({x:n,filter:s,convInfo:m,backend:t});else{let h=new rh(m);f=t.runWebGLProgram(h,[n,s],"float32")}let d=pe({inputs:{x:f},backend:t,attrs:{shape:m.outShape}});return t.disposeIntermediateTensorInfo(f),d}var EF={kernelName:Zo,backendName:"webgl",kernelFunc:YY};var tC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=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} - ${n};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${o} - ${s};
|
|
|
|
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);
|
|
}
|
|
`}},rC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,l=o-1-e.padInfo.left,u=a?1:2,c=a?2:3,p=a?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${l});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${p}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${u}], coords[${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) / ${n}.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 < ${o}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${o} - 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);
|
|
}
|
|
`}},oC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.padInfo.front,a=e.padInfo.top,i=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} - ${s};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${o} - ${a};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${i};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},nC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,o=e.filterHeight,n=e.filterWidth,s=e.strideDepth,a=e.strideHeight,i=e.strideWidth,l=t-1-e.padInfo.front,u=o-1-e.padInfo.top,c=n-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${l}, ${u}, ${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) / ${s}.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 < ${o}; 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 = ${o} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${i}.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++) {
|
|
float xValue = getDy(batch, idyF, idyR, idyC, d2);
|
|
float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function ZY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,dataFormat:l,dimRoundingMode:u,filterShape:c}=o,p=N.convertConv2DDataFormat(l),m=N.computeConv2DInfo(n.shape,c,a,1,i,u,!1,p),f=new tC(m);return t.runWebGLProgram(f,[n,s],"float32")}var DF={kernelName:Wl,backendName:"webgl",kernelFunc:ZY};function JY(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{inputShape:a,strides:i,pad:l,dataFormat:u,dimRoundingMode:c}=o,p=N.convertConv2DDataFormat(u),m=N.computeConv2DInfo(a,s.shape,i,1,l,c,!1,p),f=new rC(m);return t.runWebGLProgram(f,[n,s],"float32")}var $F={kernelName:Jo,backendName:"webgl",kernelFunc:JY};function QY(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeConv3DInfo(n.shape,s.shape,a,l,i),c=new Qv(u);return t.runWebGLProgram(c,[n,s],"float32")}var RF={kernelName:na,backendName:"webgl",kernelFunc:QY};function e7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,pad:i,filterShape:l}=o,u=N.computeConv3DInfo(n.shape,l,a,1,i),c=new oC(u);return t.runWebGLProgram(c,[n,s],"float32")}var FF={kernelName:Ul,backendName:"webgl",kernelFunc:e7};function t7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{pad:a,strides:i,inputShape:l}=o,u=N.computeConv3DInfo(l,s.shape,i,1,a),c=new nC(u);return t.runWebGLProgram(c,[n,s],"float32")}var OF={kernelName:jl,backendName:"webgl",kernelFunc:t7};var r7=$x+`
|
|
return cos(x);
|
|
`,o7=_e({opSnippet:r7}),PF={kernelName:Qo,backendName:"webgl",kernelFunc:o7};var n7=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,s7=_e({opSnippet:n7}),MF={kernelName:Js,backendName:"webgl",kernelFunc:s7};var sC=class{constructor(e,t,o,n,s){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[a,i,l,u]=e,[c]=t,[p,m]=o;this.outputShape=[c,p,m,u];let f=n==="bilinear"?1:0,[d,h]=[`${i-1}.0`,`${l-1}.0`],[g,y,b]=p>1?[`${(i-1)/(p-1)}`,"(y2-y1) * height_ratio",`y1*${d} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${d}`],[w,_,C]=m>1?[`${(l-1)/(m-1)}`,"(x2-x1) * width_ratio",`x1*${h} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${h}`];this.userCode=`
|
|
const float height_ratio = float(${g});
|
|
const float width_ratio = float(${w});
|
|
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 = ${y};
|
|
float width_scale = ${_};
|
|
|
|
float in_y = ${b};
|
|
if( in_y < 0.0 || in_y > ${d} ) {
|
|
setOutput(float(${s}));
|
|
return;
|
|
}
|
|
float in_x = ${C};
|
|
if( in_x < 0.0 || in_x > ${h} ) {
|
|
setOutput(float(${s}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${f} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}};var i7=r=>{let{inputs:e,backend:t,attrs:o}=r,{image:n,boxes:s,boxInd:a}=e,{cropSize:i,method:l,extrapolationValue:u}=o,c=new sC(n.shape,s.shape,i,l,u);return t.runWebGLProgram(c,[n,s,a],"float32")},LF={kernelName:Qs,backendName:"webgl",kernelFunc:i7};var Bx=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=e;let n=e.length,s=t?"0.0":`getX(${zF(n,"coords")})`,a=e[e.length-1],i="",l="";t?(i=o?`end != ${a-1}`:"end != 0",l=o?"end + 1":"end - 1"):(i=o?`end + pow2 < ${a}`:"end >= pow2",l=o?"end + pow2":"end - pow2"),this.userCode=`
|
|
uniform float index;
|
|
void main() {
|
|
${Le(n)} coords = getOutputCoords();
|
|
int end = ${BF(n,"coords")};
|
|
float val = ${s};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${i}) {
|
|
int idx = ${l};
|
|
${BF(n,"coords")} = idx;
|
|
val += getX(${zF(n,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,o)=>{this.index==null&&(this.index=t.getUniformLocation(o,"index")),t.gl.uniform1f(this.index,e)}}};function zF(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.x, ${e}.y`;if(r===3)return`${e}.x, ${e}.y, ${e}.z`;if(r===4)return`${e}.x, ${e}.y, ${e}.z, ${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function BF(r,e){if(r===1)return`${e}`;if(r===2)return`${e}.y`;if(r===3)return`${e}.z`;if(r===4)return`${e}.w`;throw Error(`Cumulative sum for rank ${r} is not yet supported`)}function a7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,exclusive:a,reverse:i}=o,l=n.shape.length,u=N.getAxesPermutation([s],l),c=n;u!=null&&(c=Mt({inputs:{x:n},backend:t,attrs:{perm:u}}));let p=N.getInnerMostAxes(1,l)[0];if(p!==l-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${n.shape.length-1} but got axis=${s}`);let m=c.shape[p],f=jt({inputs:{x:c},backend:t});for(let d=0;d<=Math.ceil(Math.log2(m))-1;d++){let h=new Bx(c.shape,!1,i),g=h.getCustomSetupFunc(d),y=f;f=t.runWebGLProgram(h,[f],f.dtype,g),t.disposeIntermediateTensorInfo(y)}if(a){let d=new Bx(c.shape,a,i),h=f;f=t.runWebGLProgram(d,[f],f.dtype),t.disposeIntermediateTensorInfo(h)}if(u!=null){let d=N.getUndoAxesPermutation(u),h=Mt({inputs:{x:f},backend:t,attrs:{perm:d}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(c),h}return f}var VF={kernelName:en,backendName:"webgl",kernelFunc:a7};function l7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,weights:s}=e,{size:a,binaryOutput:i}=o;if(n.shape.length===1){let l=t.readSync(n.dataId),u=t.readSync(s.dataId),c=Tx(l,u,s.dtype,s.shape,a);return t.makeTensorInfo([a],s.dtype,c)}else if(n.shape.length===2){let l=t.bufferSync(n),u=t.bufferSync(s),c=q$(l,u,a,i);return t.makeTensorInfo(c.shape,s.dtype,c.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${n.shape.length}.`)}var GF={kernelName:Hl,backendName:"webgl",kernelFunc:l7};var iC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=o,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 u7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockSize:s,dataFormat:a}=o;x.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let i=n.shape[0],l=a==="NHWC"?n.shape[1]:n.shape[2],u=a==="NHWC"?n.shape[2]:n.shape[3],c=a==="NHWC"?n.shape[3]:n.shape[1],p=l*s,m=u*s,f=c/(s*s),d=a==="NHWC"?[i,p,m,f]:[i,f,p,m],h=new iC(d,s,a);return t.runWebGLProgram(h,[n],n.dtype)}var WF={kernelName:ei,backendName:"webgl",kernelFunc:u7};var oh=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=e.outChannels/e.inChannels,y="",b="";o&&(n?y=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${o}
|
|
}`:s?y=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${o}
|
|
}`:y=`
|
|
float activation(float x) {
|
|
${o}
|
|
}
|
|
`,b="result = activation(result);");let w=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${y}
|
|
|
|
const ivec2 strides = ivec2(${c}, ${p});
|
|
const ivec2 pads = ivec2(${l}, ${u});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${g};
|
|
int q = d2 - d1 * ${g};
|
|
|
|
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 < ${d}; wR++) {
|
|
int xR = xRCorner + wR * ${m};
|
|
|
|
if (xR < 0 || xR >= ${a}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${h}; wC++) {
|
|
int xC = xCCorner + wC * ${f};
|
|
|
|
if (xC < 0 || xC >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float xVal = getX(batch, xR, xC, d1);
|
|
float wVal = getW(wR, wC, d1, q);
|
|
dotProd += xVal * wVal;
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${w}
|
|
${b}
|
|
setOutput(result);
|
|
}
|
|
`}};var nh=class{constructor(e,t=!1,o=null,n=!1,s=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let a=e.inHeight,i=e.inWidth,l=e.padInfo.top,u=e.padInfo.left,c=e.strideHeight,p=e.strideWidth,m=e.dilationHeight,f=e.dilationWidth,d=e.filterHeight,h=e.filterWidth,g=h,y="int xR; int xC; int xCOffset;";for(let C=0;C<d;C++)for(let D=0;D<h;D++)y+=`
|
|
vec4 xTexelR${C}C${D*2} = vec4(0.);
|
|
vec4 wR${C}C${D} = vec4(0.);
|
|
vec4 xR${C}C${D} = vec4(0.);`;for(let C=0;C<d;C++)for(let D=0;D<g;D++){let T=D*2;if(y+=`
|
|
xR = xRCorner + ${C*m};
|
|
xC = xCCorner + ${T*f};
|
|
`,p===1){if(T<h&&(u%2==1?y+=`
|
|
xCOffset = xC + 1;
|
|
if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${C}C${T} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if(xCOffset + 1 >= ${i}) {
|
|
xTexelR${C}C${T}.zw = vec2(0.);
|
|
}
|
|
} else {
|
|
xTexelR${C}C${T} = vec4(0.);
|
|
}
|
|
|
|
xCOffset = xC + 1 - 2;
|
|
if(xR >= 0 && xR < ${a} && xCOffset >= 0 && xCOffset < ${i}) {
|
|
vec4 previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if(xCOffset + 1 >= ${i}) {
|
|
previous.zw = vec2(0.);
|
|
}
|
|
|
|
xR${C}C${T} = vec4(previous.zw, xTexelR${C}C${T}.xy);
|
|
} else {
|
|
xR${C}C${T} = vec4(0, 0, xTexelR${C}C${T}.xy);
|
|
}
|
|
`:y+=`
|
|
if(xR >= 0 && xR < ${a} && xC >= 0 && xC < ${i}) {
|
|
xTexelR${C}C${T} = getX(batch, xR, xC, d1);
|
|
} else {
|
|
xTexelR${C}C${T} = vec4(0.);
|
|
}
|
|
|
|
xR${C}C${T} = xTexelR${C}C${T};
|
|
`,T+1<h)){let R=u%2==0?x.nearestLargerEven(f):f;f%2==0&&u%2==1||f%2!=0&&u%2!=1?(y+=`
|
|
xCOffset = xC + ${u%2} + ${R};
|
|
|
|
if(xR >= 0 && xR < ${a} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${C}C${T+2} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
`,f>1&&(y+=`
|
|
xCOffset -= 2;
|
|
if(xR >= 0 && xR < ${a} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${C}C${T} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${C}C${T} = vec4(0.);
|
|
}
|
|
`),y+=`
|
|
xR${C}C${T+1} = vec4(
|
|
xTexelR${C}C${T}.zw, xTexelR${C}C${T+2}.xy);
|
|
`):y+=`
|
|
xCOffset = xC + ${R};
|
|
|
|
if(xR >= 0 && xR < ${a} &&
|
|
xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${C}C${T+2} = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
|
|
xR${C}C${T+1} = xTexelR${C}C${T+2};
|
|
`}}else T<h&&(y+=`
|
|
if(xR >= 0 && xR < ${a}) {
|
|
`,u%2==1?(y+=`
|
|
xCOffset = xC + 1 - ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${C}C${T} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${C}C${T} = vec4(0.);
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < ${i}) {
|
|
xTexelR${C}C${T+2} = getX(batch, xR, xC + 1, d1);
|
|
} else {
|
|
xTexelR${C}C${T+2} = vec4(0.);
|
|
}
|
|
|
|
xR${C}C${T} = vec4(
|
|
xTexelR${C}C${T}.zw, xTexelR${C}C${T+2}.zw);
|
|
`,T+1<h&&(y+=`
|
|
vec4 final = vec4(0.);
|
|
xCOffset = xC + 1 + ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xR${C}C${T+1} = vec4(xTexelR${C}C${T+2}.xy, final.xy);
|
|
`)):(y+=`
|
|
if(xC >= 0 && xC < ${i}) {
|
|
xTexelR${C}C${T} = getX(batch, xR, xC, d1);
|
|
} else {
|
|
xTexelR${C}C${T} = vec4(0.);
|
|
}
|
|
|
|
xCOffset = xC + ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${i}) {
|
|
xTexelR${C}C${T+2} = getX(batch, xR, xCOffset, d1);
|
|
} else {
|
|
xTexelR${C}C${T+2} = vec4(0.);
|
|
}
|
|
|
|
xR${C}C${T} = vec4(
|
|
xTexelR${C}C${T}.xy, xTexelR${C}C${T+2}.xy);
|
|
`,T+1<h&&(y+=`
|
|
xR${C}C${T+1} = vec4(
|
|
xTexelR${C}C${T}.zw, xTexelR${C}C${T+2}.zw);
|
|
`)),y+="}");T<h&&(y+=`
|
|
vec4 wTexelR${C}C${T} = getW(${C}, ${T}, d1, q);
|
|
wR${C}C${T} = vec4(wTexelR${C}C${T}.xz, wTexelR${C}C${T}.xz);
|
|
`,T+1<h&&(y+=`
|
|
vec4 wTexelR${C}C${T+1} = getW(${C}, ${T+1}, d1, q);
|
|
wR${C}C${T+1} =
|
|
vec4(wTexelR${C}C${T+1}.xz, wTexelR${C}C${T+1}.xz);`))}for(let C=0;C<d;C++)for(let D=0;D<h;D++)y+=`dotProd += xR${C}C${D} * wR${C}C${D};`;let b="",w="";o&&(n?b=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${o}
|
|
}`:s?b=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${o}
|
|
}`:b=`vec4 activation(vec4 x) {
|
|
${o}
|
|
}`,w="result = activation(result);");let _=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),n&&this.variableNames.push("preluActivationWeights"),s&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${b}
|
|
|
|
const ivec2 strides = ivec2(${c}, ${p});
|
|
const ivec2 pads = ivec2(${l}, ${u});
|
|
|
|
void main() {
|
|
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2;
|
|
int q = 0;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
vec4 dotProd = vec4(0.);
|
|
|
|
${y}
|
|
|
|
vec4 result = dotProd;
|
|
${_}
|
|
${w}
|
|
setOutput(result);
|
|
}
|
|
`}};function c7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l,dimRoundingMode:u}=o,c=l;c==null&&(c=[1,1]),x.assert(N.eitherStridesOrDilationsAreOne(a,c),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${a} and dilations '${c}'`);let p=N.computeConv2DInfo(n.shape,s.shape,a,c,i,u,!0),m;return W().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?m=new nh(p):m=new oh(p),t.runWebGLProgram(m,[n,s],"float32")}var UF={kernelName:tn,backendName:"webgl",kernelFunc:c7};var aC=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,o=e.strideWidth,n=e.padInfo.top,s=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} - ${n};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${o} - ${s};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},lC=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,o=e.filterWidth,n=e.strideHeight,s=e.strideWidth,a=t-1-e.padInfo.top,i=o-1-e.padInfo.left,l=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${a}, ${i});
|
|
|
|
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) / ${n}.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 < ${o}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${s}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${o} - 1 - wC;
|
|
|
|
// TO DO: Vec4 over the channelMul
|
|
for (int dm = 0; dm < ${l}; dm++) {
|
|
int d2 = d1 * ${l} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function p7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,dy:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,filterShape:c}=o,p=N.computeConv2DInfo(n.shape,c,a,i,l,u,!0),m=new aC(p);return t.runWebGLProgram(m,[n,s],"float32")}var jF={kernelName:ql,backendName:"webgl",kernelFunc:p7};function m7(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,filter:s}=e,{strides:a,dilations:i,pad:l,dimRoundingMode:u,inputShape:c}=o,p=N.computeConv2DInfo(c,s.shape,a,i,l,u,!0),m=new lC(p);return t.runWebGLProgram(m,[n,s],"float32")}var HF={kernelName:Kl,backendName:"webgl",kernelFunc:m7};var uC=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function f7(r){let{inputs:e,backend:t}=r,{x:o}=e,n=[...o.shape,...o.shape],s=x.sizeFromShape(o.shape),a=pe({inputs:{x:o},backend:t,attrs:{shape:[s]}}),i=new uC(s),l=t.runWebGLProgram(i,[a],a.dtype),u=pe({inputs:{x:l},backend:t,attrs:{shape:n}});return t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(l),u}var qF={kernelName:Xl,backendName:"webgl",kernelFunc:f7};var cC=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:o,padInfo:n,strideHeight:s,strideWidth:a,filterHeight:i,filterWidth:l,dilationHeight:u,dilationWidth:c}=e,{top:p,left:m}=n;this.userCode=`
|
|
const ivec2 strides = ivec2(${s}, ${a});
|
|
const ivec2 pads = ivec2(${p}, ${m});
|
|
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 < ${i}; h++) {
|
|
int hIn = hBeg + h * ${u};
|
|
|
|
if (hIn >= 0 && hIn < ${t}) {
|
|
for (int w = 0; w < ${l}; w++) {
|
|
int wIn = wBeg + w * ${c};
|
|
|
|
if (wIn >= 0 && wIn < ${o}) {
|
|
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 d7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s}=e,{strides:a,pad:i,dilations:l}=o,u=N.computeDilation2DInfo(n.shape,s.shape,a,i,"NHWC",l),c,p=new cC(u);c=t.runWebGLProgram(p,[n,s],"float32");let m=pe({inputs:{x:c},backend:t,attrs:{shape:u.outShape}});return t.disposeIntermediateTensorInfo(c),m}var KF={kernelName:sa,backendName:"webgl",kernelFunc:d7};var h7="return (x >= 0.0) ? x : (exp(x) - 1.0);",g7=`
|
|
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;
|
|
`,x7=_e({opSnippet:h7,packedOpSnippet:g7}),XF={kernelName:ti,backendName:"webgl",kernelFunc:x7};var y7="return (b >= 1.0) ? a : a * (b + 1.0);",b7=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,w7=r=>{let{inputs:e,backend:t}=r,{dy:o,y:n}=e,s=W().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new Vs(b7,o.shape,n.shape):new rs(y7,o.shape,n.shape);return t.runWebGLProgram(s,[o,n],o.dtype)},YF={kernelName:Yl,backendName:"webgl",kernelFunc:w7};var _7=`
|
|
return vec4(equal(a, b));
|
|
`,k7="return float(a == b);",v7=nt({opSnippet:k7,packedOpSnippet:_7,dtype:"bool"}),ZF={kernelName:oi,backendName:"webgl",kernelFunc:v7};var C7=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${N.ERF_P};
|
|
float a1 = ${N.ERF_A1};
|
|
float a2 = ${N.ERF_A2};
|
|
float a3 = ${N.ERF_A3};
|
|
float a4 = ${N.ERF_A4};
|
|
float a5 = ${N.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,I7=_e({opSnippet:C7}),JF={kernelName:ri,backendName:"webgl",kernelFunc:I7};var QF="return exp(x);",pC=_e({opSnippet:QF,packedOpSnippet:QF,cpuKernelImpl:Y$}),eO={kernelName:on,backendName:"webgl",kernelFunc:pC};function Vx(r){let{inputs:e,attrs:t,backend:o}=r,{dim:n}=t,{input:s}=e,a=s.shape.length,i=s.shape.slice(),l=n;return n<0&&(x.assert(-(a+1)<=n,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+n+1),i.splice(l,0,1),pe({inputs:{x:s},backend:o,attrs:{shape:i}})}var tO={kernelName:us,backendName:"webgl",kernelFunc:Vx};var rO="return exp(x) - 1.0;",N7=_e({opSnippet:rO,packedOpSnippet:rO,cpuKernelImpl:Z$}),oO={kernelName:ni,backendName:"webgl",kernelFunc:N7};var Gx=class{constructor(e,t,o){this.variableNames=["real","imag"];let n=t[1];this.outputShape=t;let s=o?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,a=o?`${n}.0`:"1.0",i;if(e==="real")i="return real * expR - imag * expI;";else if(e==="imag")i="return real * expI + imag * expR;";else throw new Error(`FFT component must be either "real" or "imag", got ${e}.`);this.userCode=`
|
|
const float exponentMultiplier = ${s};
|
|
|
|
float unaryOpComplex(float real, float expR, float imag, float expI) {
|
|
${i}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${n});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${n}; 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 Wx(r,e,t){let o=t.texData.get(r.dataId),n=x.sizeFromShape(r.shape),s=r.shape[r.shape.length-1],a=n/s,i=pe({inputs:{x:r},backend:t,attrs:{shape:[a,s]}}),l=i.shape,u=new Gx("real",l,e),c=new Gx("imag",l,e),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:l},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:l}],m=t.runWebGLProgram(u,p,"float32"),f=t.runWebGLProgram(c,p,"float32"),d=go({inputs:{real:m,imag:f},backend:t});t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f);let h=pe({inputs:{x:d},backend:t,attrs:{shape:r.shape}});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(d),h}function S7(r){let{inputs:e,backend:t}=r,{input:o}=e;return Wx(o,!1,t)}var nO={kernelName:Zl,backendName:"webgl",kernelFunc:S7};var mC=class{constructor(e,t){this.outputShape=[],this.variableNames=["x"],this.outputShape=e,this.userCode=`
|
|
uniform float value;
|
|
void main() {
|
|
// Input can be obtained from uniform value.
|
|
setOutput(value);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,o)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(o,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function sh(r){let{backend:e,attrs:t}=r,{shape:o,value:n}=t,{dtype:s}=t;if(s=s||x.inferDtype(n),s==="string"){let a=x.getArrayFromDType(s,x.sizeFromShape(o));return a.fill(n),e.makeTensorInfo(o,s,a)}else{let a=new mC(o,n),i=a.getCustomSetupFunc(n);return e.runWebGLProgram(a,[],s,i)}}var sO={kernelName:ia,backendName:"webgl",kernelFunc:sh};var fC=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;
|
|
float outputValue;
|
|
if(coordX >= 0 && coordX < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};var iO={kernelName:si,backendName:"webgl",kernelFunc:({inputs:r,backend:e})=>{let{image:t}=r,o=e,n=new fC(t.shape);return o.runWebGLProgram(n,[t],t.dtype)}};var aO="return floor(x);",T7=_e({opSnippet:aO,packedOpSnippet:aO,cpuKernelImpl:J$}),lO={kernelName:nn,backendName:"webgl",kernelFunc:T7};var A7=`
|
|
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;
|
|
}
|
|
`,E7=`
|
|
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);
|
|
`,D7=nt({opSnippet:A7,packedOpSnippet:E7,dtype:"int32"}),uO={kernelName:sn,backendName:"webgl",kernelFunc:D7};var dC=class{constructor(e){this.variableNames=["A"];let t=Ot(),[o,n]=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(${n}.0, ${o}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}};var hC=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=Ot(),[o,n]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${n}.0, ${o}.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);
|
|
}
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}};var cO={kernelName:$c,backendName:"webgl",kernelFunc:$7},Hp;function $7(r){let{inputs:e,backend:t,attrs:o}=r,{pixels:n}=e,{numChannels:s}=o,a=typeof HTMLVideoElement!="undefined"&&n instanceof HTMLVideoElement,i=typeof HTMLImageElement!="undefined"&&n instanceof HTMLImageElement,l=typeof ImageBitmap!="undefined"&&n instanceof ImageBitmap,[u,c]=a?[n.videoWidth,n.videoHeight]:[n.width,n.height],p=[c,u],m=[c,u,s];(i||a||l)&&(Hp==null&&(Hp=document.createElement("canvas").getContext("2d")),Hp.canvas.width=u,Hp.canvas.height=c,Hp.drawImage(n,0,0,u,c),n=Hp.canvas);let f=t.makeTensorInfo(p,"int32");t.texData.get(f.dataId).usage=Er.PIXELS,t.gpgpu.uploadPixelDataToTexture(t.getTexture(f.dataId),n);let d=W().getBool("WEBGL_PACK")?new hC(m):new dC(m),h=t.runWebGLProgram(d,[f],"int32");return t.disposeData(f.dataId),h}function R7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dataFormat:c,dilations:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=o,h=N.convertConv2DDataFormat(c),g=N.computeConv2DInfo(n.shape,s.shape,l,p,u,m,!1,h),y,b=[];if(g.filterHeight===1&&g.filterWidth===1&&g.dilationHeight===1&&g.dilationWidth===1&&g.strideHeight===1&&g.strideWidth===1&&(g.padInfo.type==="SAME"||g.padInfo.type==="VALID"))y=Lx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else if(W().getBool("WEBGL_CONV_IM2COL")&&n.shape[0]===1)y=zx({x:n,filter:s,convInfo:g,backend:t,bias:a,activation:f,preluActivationWeights:i,leakyreluAlpha:d});else{let _=a!=null,C=i!=null,D=f==="leakyrelu",T=f?Tl(f,!1):null,R=new rh(g,_,T,C,D),P=[n,s];if(a&&P.push(a),i&&P.push(i),D){let B=t.makeTensorInfo([],"float32",x.createScalarValue(d,"float32"));P.push(B),b.push(B)}y=t.runWebGLProgram(R,P,"float32")}let w=pe({inputs:{x:y},backend:t,attrs:{shape:g.outShape}});return b.push(y),b.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var pO={kernelName:_s,backendName:"webgl",kernelFunc:R7};function F7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dimRoundingMode:p,activation:m,leakyreluAlpha:f}=o,d=[],h=c;h==null&&(h=[1,1]),x.assert(N.eitherStridesOrDilationsAreOne(l,h),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${l} and dilations '${h}'`);let g=N.computeConv2DInfo(n.shape,s.shape,l,h,u,p,!0),y=W().getBool("WEBGL_PACK_DEPTHWISECONV")&&g.strideWidth<=2&&g.outChannels/g.inChannels==1,b=m?Tl(m,y):null,w=[n,s],_=a!=null,C=i!=null,D=m==="leakyrelu";if(_&&w.push(a),C&&w.push(i),D){let P=t.makeTensorInfo([],"float32",x.createScalarValue(f,"float32"));w.push(P),d.push(P)}let T;y?T=new nh(g,_,b,C,D):T=new oh(g,_,b,C,D);let R=t.runWebGLProgram(T,w,"float32");return d.forEach(P=>t.disposeIntermediateTensorInfo(P)),R}var mO={kernelName:ks,backendName:"webgl",kernelFunc:F7};var gC=class{constructor(e,t,o){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=o;let n=Le(t.length),s=Le(o.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${n} strides = ${n}(${this.strides});
|
|
void main() {
|
|
${s} 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 O7(r){let{inputs:e,backend:t}=r,{params:o,indices:n}=e,s=n.shape,a=s[s.length-1],[i,l,u,c]=N.prepareAndValidate(o,n),p=pe({inputs:{x:n},backend:t,attrs:{shape:[l,a]}}),m=pe({inputs:{x:o},backend:t,attrs:{shape:[x.sizeFromShape(o.shape)/u,u]}}),f=new gC(a,c,[l,u]),d=t.runWebGLProgram(f,[m,p],m.dtype),h=pe({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(d),h}var fO={kernelName:ii,backendName:"webgl",kernelFunc:O7};var xC=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let o=Le(this.rank),n=P7(e,2);this.userCode=`
|
|
void main() {
|
|
${o} resRC = getOutputCoords();
|
|
setOutput(getA(${n}));
|
|
}
|
|
`}};function P7(r,e){let t=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[];for(let n=0;n<r.length;n++)n===2?o.push("int(getIndices(resRC.x, resRC.z))"):o.push(`${t[n]}`);return o.join()}function M7(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,indices:s}=e,{axis:a,batchDims:i}=o,l=x.parseAxisParam(a,n.shape)[0],u=N.segment_util.collectGatherOpShapeInfo(n,s,l,i),c=x.sizeFromShape(s.shape),p=[],m=pe({inputs:{x:n},backend:t,attrs:{shape:[u.batchSize,u.outerSize,u.dimSize,u.sliceSize]}}),f=pe({inputs:{x:s},backend:t,attrs:{shape:[u.batchSize,c/u.batchSize]}});p.push(m),p.push(f);let d=[u.batchSize,u.outerSize,c/u.batchSize,u.sliceSize];if(t.shouldExecuteOnCPU([n,s])||n.dtype==="string"){let b=t.bufferSync(f),w=t.bufferSync(m),_=Q$(w,b,d);return p.forEach(C=>t.disposeIntermediateTensorInfo(C)),t.makeTensorInfo(u.outputShape,_.dtype,_.values)}let h=new xC(m.shape,d),g=t.runWebGLProgram(h,[m,f],m.dtype);p.push(g);let y=pe({inputs:{x:g},backend:t,attrs:{shape:u.outputShape}});return p.forEach(b=>t.disposeIntermediateTensorInfo(b)),y}var dO={kernelName:cs,backendName:"webgl",kernelFunc:M7};var L7="return float(a > b);",z7=`
|
|
return vec4(greaterThan(a, b));
|
|
`,B7=nt({opSnippet:L7,packedOpSnippet:z7,cpuKernelImpl:eR,dtype:"bool"}),hO={kernelName:ai,backendName:"webgl",kernelFunc:B7};var V7="return float(a >= b);",G7=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,W7=nt({opSnippet:V7,packedOpSnippet:G7,dtype:"bool"}),gO={kernelName:ln,backendName:"webgl",kernelFunc:W7};function U7(r){let{inputs:e,backend:t}=r,{input:o}=e;return Wx(o,!0,t)}var xO={kernelName:Jl,backendName:"webgl",kernelFunc:U7};var j7="return float(!isnan(x) && !isinf(x));",H7=_e({opSnippet:j7,dtype:"bool"}),yO={kernelName:li,backendName:"webgl",kernelFunc:H7};var q7="return float(isinf(x));",K7=_e({opSnippet:q7,dtype:"bool"}),bO={kernelName:ui,backendName:"webgl",kernelFunc:K7};var X7="return float(isnan(x));",Y7=_e({opSnippet:X7,dtype:"bool"}),wO={kernelName:ci,backendName:"webgl",kernelFunc:Y7};var Z7="return float(a < b);",J7=`
|
|
return vec4(lessThan(a, b));
|
|
`,Q7=nt({opSnippet:Z7,packedOpSnippet:J7,cpuKernelImpl:tR,dtype:"bool"}),_O={kernelName:pi,backendName:"webgl",kernelFunc:Q7};var eZ="return float(a <= b);",tZ=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,rZ=nt({opSnippet:eZ,packedOpSnippet:tZ,dtype:"bool"}),kO={kernelName:mi,backendName:"webgl",kernelFunc:rZ};function oZ(r){let{backend:e,attrs:t}=r,{start:o,stop:n,num:s}=t,a=rR(o,n,s);return e.makeTensorInfo([a.length],"float32",a)}var vO={kernelName:eu,backendName:"webgl",kernelFunc:oZ};var nZ=`if (x < 0.0) return NAN;
|
|
return log(x);`,sZ=`
|
|
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;
|
|
`,iZ=_e({opSnippet:nZ,packedOpSnippet:sZ,cpuKernelImpl:oR}),CO={kernelName:cn,backendName:"webgl",kernelFunc:iZ};var aZ="return log(1.0 + x);",lZ=_e({opSnippet:aZ}),IO={kernelName:fi,backendName:"webgl",kernelFunc:lZ};var uZ="return float(a >= 1.0 && b >= 1.0);",cZ=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,pZ=nt({opSnippet:uZ,packedOpSnippet:cZ,dtype:"bool"}),NO={kernelName:di,backendName:"webgl",kernelFunc:pZ};var mZ="return float(!(x >= 1.0));",fZ=_e({opSnippet:mZ}),SO={kernelName:Ya,backendName:"webgl",kernelFunc:fZ};var dZ="return float(a >= 1.0 || b >= 1.0);",hZ=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,gZ=nt({opSnippet:dZ,packedOpSnippet:hZ,dtype:"bool"}),TO={kernelName:Za,backendName:"webgl",kernelFunc:gZ};var yC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[];let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,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 <= ${i}) {
|
|
float z = getX(b, r, c, idx);
|
|
sum += z * z;
|
|
}
|
|
}
|
|
float val = x * ${l};
|
|
setOutput(val);
|
|
}
|
|
`}};var bC=class{constructor(e,t,o,n,s){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let a=t,i=e[3]-1;this.outputShape=e;let l,u=`float(${o}) + float(${n}) * sum`;s===.5?l=`inversesqrt(${u})`:s===1?l=`1.0/(${u})`:l=`exp(log(${u}) * float(-${s}));`,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 <= ${a}; j++) {
|
|
ivec2 idx = depth + j;
|
|
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
|
|
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${i}));
|
|
|
|
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 * ${l};
|
|
setOutput(result);
|
|
}
|
|
`}};var xZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{depthRadius:s,bias:a,alpha:i,beta:l}=o,u=W().getBool("WEBGL_PACK_NORMALIZATION")?new bC(n.shape,s,a,i,l):new yC(n.shape,s,a,i,l);return t.runWebGLProgram(u,[n],n.dtype)},AO={kernelName:aa,backendName:"webgl",kernelFunc:xZ};var wC=class{constructor(e,t,o,n,s){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=o,this.alpha=n,this.beta=s,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 = ${this.depth};
|
|
|
|
float norm = 0.0;
|
|
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd) {
|
|
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
norm = float(${n}) * norm + float(${o});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${n})
|
|
* float(${s})
|
|
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${s});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};var yZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n,y:s,dy:a}=e,{depthRadius:i,bias:l,alpha:u,beta:c}=o,p=new wC(n.shape,i,l,u,c);return t.runWebGLProgram(p,[n,s,a],n.dtype)},EO={kernelName:tu,backendName:"webgl",kernelFunc:yZ};function DO(r,e,t,o){let n=x.sizeFromShape(e),a=x.sizeFromShape(r.shape)/n,i=pe({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=To(i,r.dtype,"max",o),u=pe({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}function _C(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reductionIndices:s,keepDims:a}=o,i=n.shape.length,l=x.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=t.shouldExecuteOnCPU([n]),f=n;if(p){if(m){let w=t.texData.get(f.dataId).values,_=new Array(i);for(let T=0;T<_.length;T++)_[T]=n.shape[c[T]];let C=Up(w,n.shape,n.dtype,c,_);f=t.makeTensorInfo(_,n.dtype);let D=t.texData.get(f.dataId);D.values=C}else f=Al(n,c,t);u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("max",u,i);let[d,h]=N.computeOutAndReduceShapes(f.shape,u),g=d;a&&(g=N.expandShapeToKeepDim(d,l));let y;if(m){let w=t.texData.get(f.dataId).values,_=nR(w,x.sizeFromShape(h),g,n.dtype);y=t.makeTensorInfo(g,n.dtype);let C=t.texData.get(y.dataId);C.values=_}else y=DO(f,h,g,t);return p&&t.disposeIntermediateTensorInfo(f),y}var $O={kernelName:pn,backendName:"webgl",kernelFunc:_C};var bZ=Dx+`
|
|
return max(a, b);
|
|
`,wZ=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Sl+`
|
|
return result;
|
|
`,_Z=nt({opSnippet:bZ,packedOpSnippet:wZ,cpuKernelImpl:sR}),RO={kernelName:mn,backendName:"webgl",kernelFunc:_Z};function kZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e;qi(n,"maxPool");let{filterSize:s,strides:a,pad:i,dimRoundingMode:l}=o,u=1;x.assert(N.eitherStridesOrDilationsAreOne(a,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${a} and dilations '${u}'`);let c=N.computePool2DInfo(n.shape,s,a,u,i,l);if(c.filterWidth===1&&c.filterHeight===1&&x.arraysEqual(c.inShape,c.outShape))return jt({inputs:{x:n},backend:t});let p=new Ki(c,"max",!1);return t.runWebGLProgram(p,[n],n.dtype)}var FO={kernelName:fn,backendName:"webgl",kernelFunc:kZ};function vZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{filterSize:s,strides:a,pad:i,dataFormat:l,dimRoundingMode:u}=o,c=[1,1,1],p=N.computePool3DInfo(n.shape,s,a,c,i,u,l),m=new ac(p,"max",!1);return t.runWebGLProgram(m,[n],n.dtype)}var OO={kernelName:la,backendName:"webgl",kernelFunc:vZ};var kC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,o=e.strideWidth,n=e.dilationHeight,s=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=s-1-e.padInfo.top,l=a-1-e.padInfo.left,u=s*a-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${l});
|
|
|
|
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 < ${s};
|
|
wR += ${n}) {
|
|
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) / ${o}.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 = ${u} - int(getMaxPos(b, idyR, idyC, d));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue = wR * ${a} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},vC=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,o=e.strideHeight,n=e.strideWidth,s=e.dilationDepth,a=e.dilationHeight,i=e.dilationWidth,l=e.effectiveFilterDepth,u=e.effectiveFilterHeight,c=e.effectiveFilterWidth,p=l-1-e.padInfo.front,m=u-1-e.padInfo.top,f=c-1-e.padInfo.left,d=l*u*c-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${p}, ${m}, ${f});
|
|
|
|
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 < ${l};
|
|
wD += ${s}) {
|
|
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 < ${u};
|
|
wR += ${a}) {
|
|
float dyR = float(dyRCorner + wR) / ${o}.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 += ${i}) {
|
|
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(batch, idyD, idyR, idyC, ch);
|
|
int maxPosValue = ${d} -
|
|
int(getMaxPos(batch, idyD, idyR, idyC, ch));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue =
|
|
wD * ${u} * ${c} +
|
|
wR * ${c} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function CZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s}=e,a=s,{filterSize:i,strides:l,pad:u,dimRoundingMode:c}=o,p=[1,1,1],m=N.computePool3DInfo(a.shape,i,l,p,u,c),f=new ac(m,"max",!0),d=t.runWebGLProgram(f,[a],a.dtype),h=new vC(m),g=t.runWebGLProgram(h,[n,d],a.dtype);return t.disposeIntermediateTensorInfo(d),g}var PO={kernelName:ou,backendName:"webgl",kernelFunc:CZ};function IZ(r){let{inputs:e,backend:t,attrs:o}=r,{dy:n,input:s,output:a}=e,i=s;qi([s,a],"maxPoolGrad");let{filterSize:l,strides:u,pad:c,dimRoundingMode:p}=o,m=N.computePool2DInfo(i.shape,l,u,1,c,p),f=!0,d=new Ki(m,"max",f),h=t.runWebGLProgram(d,[i],i.dtype),g=new kC(m),y=t.runWebGLProgram(g,[n,h],i.dtype);return t.disposeIntermediateTensorInfo(h),y}var MO={kernelName:ru,backendName:"webgl",kernelFunc:IZ};function LO(r,e,t,o){let n=new Ki(t,"max",!1),s=o.runWebGLProgram(n,[r],"float32");n=new Ki(t,"max",!0,!0,e);let a=o.runWebGLProgram(n,[r],"float32");return[s,a]}var zO={kernelName:nu,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{filterSize:n,strides:s,pad:a,includeBatchInIndex:i}=e,l=t;x.assert(o.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${o.shape.length}.`);let u=[1,1];x.assert(N.eitherStridesOrDilationsAreOne(s,u),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${u}'`);let c=N.computePool2DInfo(o.shape,n,s,u,a),[p,m]=LO(o,i,c,l);return[p,m]}};function BO(r,e,t,o){let n=x.sizeFromShape(e),a=x.sizeFromShape(r.shape)/n,i=pe({inputs:{x:r},attrs:{shape:[a,n]},backend:o}),l=To(i,"float32","mean",o),u=pe({inputs:{x:l},attrs:{shape:t},backend:o});return o.disposeIntermediateTensorInfo(i),o.disposeIntermediateTensorInfo(l),u}var VO={kernelName:dn,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{x:o}=r,{keepDims:n,axis:s}=e,a=t,i=o.shape.length,l=x.parseAxisParam(s,o.shape),u=l,c=N.getAxesPermutation(u,i),p=c!=null,m=a.shouldExecuteOnCPU([o]),f=[],d=o;if(p){if(m){let _=a.texData.get(d.dataId).values,C=new Array(i);for(let R=0;R<C.length;R++)C[R]=o.shape[c[R]];let D=Up(_,o.shape,o.dtype,c,C);d=a.makeTensorInfo(C,o.dtype);let T=a.texData.get(d.dataId);T.values=D}else d=Al(o,c,a);f.push(d),u=N.getInnerMostAxes(u.length,i)}N.assertAxesAreInnerMostDims("sum",u,i);let[h,g]=N.computeOutAndReduceShapes(d.shape,u),y=h;n&&(y=N.expandShapeToKeepDim(h,l));let b=BO(d,g,y,a);for(let w of f)a.disposeIntermediateTensorInfo(w);return b}};function NZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=x.parseAxisParam(s,n.shape),u=l,c=N.getAxesPermutation(u,i),p=n;c!=null&&(p=Mt({inputs:{x:n},backend:t,attrs:{perm:c}}),u=N.getInnerMostAxes(u.length,n.shape.length)),N.assertAxesAreInnerMostDims("min",u,i);let[m,f]=N.computeOutAndReduceShapes(p.shape,u),d=x.sizeFromShape(f),h=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,d]}}),g=To(h,h.dtype,"min",t),y;if(a){let b=N.expandShapeToKeepDim(m,l);y=pe({inputs:{x:g},backend:t,attrs:{shape:b}})}else y=pe({inputs:{x:g},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(h),t.disposeIntermediateTensorInfo(g),c!=null&&t.disposeIntermediateTensorInfo(p),y}var GO={kernelName:hn,backendName:"webgl",kernelFunc:NZ};var SZ=Dx+`
|
|
return min(a, b);
|
|
`,TZ=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+Sl+`
|
|
return result;
|
|
`,AZ=nt({opSnippet:SZ,packedOpSnippet:TZ,cpuKernelImpl:iR}),WO={kernelName:gn,backendName:"webgl",kernelFunc:AZ};var CC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((c,p)=>c[0]+e[p]+c[1]);let n=e.length,s=Le(n),a=t.map(c=>c[0]).join(","),i=t.map((c,p)=>c[0]+e[p]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n),u=o==="reflect"?0:1;if(n===1){this.userCode=`
|
|
int start = ${a};
|
|
int end = ${i};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start) {
|
|
outC = start * 2 - outC - ${u};
|
|
} else if(outC >= end) {
|
|
outC = (end - 1) * 2 - outC + ${u};
|
|
}
|
|
setOutput(getX(outC - start));
|
|
}
|
|
`;return}this.userCode=`
|
|
${s} start = ${s}(${a});
|
|
${s} end = ${s}(${i});
|
|
|
|
void main() {
|
|
${s} outC = getOutputCoords();
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (outC[i] < start[i]) {
|
|
outC[i] = start[i] * 2 - outC[i] - ${u};
|
|
} else if(outC[i] >= end[i]) {
|
|
outC[i] = (end[i] - 1) * 2 - outC[i] + ${u};
|
|
}
|
|
}
|
|
${s} coords = outC - start;
|
|
setOutput(getX(${l}));
|
|
}
|
|
`}};var IC=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((d,h)=>d[0]+e[h]+d[1]);let n=e.length,s=Le(n),a=t.map(d=>d[0]).join(","),i=t.map((d,h)=>d[0]+e[h]).join(","),l=Ut("rc",n),u=Ut("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=o==="reflect"?0:1,f="";if(n===1){let d=`
|
|
${s} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${m};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${m};
|
|
}
|
|
source -= start;
|
|
`;f=`
|
|
${s} rc = outputLoc;
|
|
${d}
|
|
result[0] = getChannel(getX(${u.join()}), ${p});
|
|
${l[n-1]} += 1;
|
|
if(${c}) {
|
|
${d}
|
|
result[1] = getChannel(getX(${u.join()}), ${p});
|
|
}
|
|
`}else{let d=`
|
|
${s} source = rc;
|
|
${s} lt = ${s}(lessThan(source, start));
|
|
${s} gte = ${s}(greaterThanEqual(source, end));
|
|
${s} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${m}) +
|
|
gte * ((end - 1) * 2 - source + ${m});
|
|
source -= start;
|
|
`;f=`
|
|
${s} rc = outputLoc;
|
|
${d}
|
|
result[0] = getChannel(getX(${u.join()}), ${p});
|
|
${l[n-1]} += 1;
|
|
if(${c}) {
|
|
${d}
|
|
result[1] = getChannel(getX(${u.join()}), ${p});
|
|
}
|
|
rc = outputLoc;
|
|
${l[n-2]} += 1;
|
|
if(${l[n-2]} < ${this.outputShape[n-2]}) {
|
|
${d}
|
|
result[2] = getChannel(getX(${u.join()}), ${p});
|
|
${l[n-1]} += 1;
|
|
if(${c}) {
|
|
${d}
|
|
result[3] = getChannel(getX(${u.join()}), ${p});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${s} start = ${s}(${a});
|
|
const ${s} end = ${s}(${i});
|
|
|
|
void main() {
|
|
${s} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${f}
|
|
setOutput(result);
|
|
}
|
|
`}};var EZ=({inputs:r,backend:e,attrs:t})=>{let{x:o}=r,{paddings:n,mode:s}=t,a=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new IC(o.shape,n,s):new CC(o.shape,n,s);return e.runWebGLProgram(a,[o],o.dtype)},UO={kernelName:ua,backendName:"webgl",kernelFunc:EZ};var DZ=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,$Z=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+Sl+`
|
|
return result;
|
|
`,RZ=nt({opSnippet:DZ,packedOpSnippet:$Z}),jO={kernelName:hi,backendName:"webgl",kernelFunc:RZ};var NC=class{constructor(e,t,o){this.variableNames=["probs"],this.outputShape=[e,o],this.userCode=`
|
|
uniform float seed;
|
|
|
|
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}));
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,o)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(o,"seed")),t.gl.uniform1f(this.seedLoc,e)}}};var FZ=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,OZ=`
|
|
// 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;
|
|
`,SC=nt({opSnippet:FZ,packedOpSnippet:OZ,checkOutOfBounds:!0}),HO={kernelName:rn,backendName:"webgl",kernelFunc:SC};var qO="return a - b;",TC=nt({opSnippet:qO,packedOpSnippet:qO,supportsComplex:!0,cpuKernelImpl:dR}),KO={kernelName:Fn,backendName:"webgl",kernelFunc:TC};function AC(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{dim:s}=o,a=x.parseAxisParam([s],n.shape),i=_C({inputs:{x:n},backend:t,attrs:{reductionIndices:a,keepDims:!1}}),l=N.expandShapeToKeepDim(i.shape,a),u=pe({inputs:{x:i},backend:t,attrs:{shape:l}}),c=TC({inputs:{a:n,b:u},backend:t}),p=pC({inputs:{x:c},backend:t}),m=th({inputs:{x:p},backend:t,attrs:{axis:a,keepDims:!1}}),f=pe({inputs:{x:m},backend:t,attrs:{shape:l}}),d=SC({inputs:{a:p,b:f},backend:t});return t.disposeIntermediateTensorInfo(i),t.disposeIntermediateTensorInfo(u),t.disposeIntermediateTensorInfo(c),t.disposeIntermediateTensorInfo(p),t.disposeIntermediateTensorInfo(m),t.disposeIntermediateTensorInfo(f),d}var XO={kernelName:$n,backendName:"webgl",kernelFunc:AC};function PZ(r){let{inputs:e,backend:t,attrs:o}=r,{logits:n}=e,{numSamples:s,seed:a,normalized:i}=o,l=i?n:AC({inputs:{logits:n},backend:t,attrs:{dim:n.shape.length-1}}),u=l.shape[0],c=l.shape[1],p=new NC(u,c,s),m=p.getCustomSetupFunc(a),f=t.runWebGLProgram(p,[l],"int32",m);return i||t.disposeIntermediateTensorInfo(l),f}var YO={kernelName:su,backendName:"webgl",kernelFunc:PZ};var ZO="return -x;";function MZ(r){let{inputs:e,backend:t}=r,{x:o}=e;if(t.shouldExecuteOnCPU([o])){let s=t.texData.get(o.dataId),[a,i]=lR(s.values,o.shape,o.dtype);return t.makeTensorInfo(i,o.dtype,a)}let n;return W().getBool("WEBGL_PACK_UNARY_OPERATIONS")?n=new Bs(o.shape,ZO):n=new ho(o.shape,ZO),t.runWebGLProgram(n,[o],o.dtype)}var JO={kernelName:ps,backendName:"webgl",kernelFunc:MZ};var LZ=Tr.nonMaxSuppressionV3Impl;function zZ(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l}=o,u=t.readSync(n.dataId),c=t.readSync(s.dataId),{selectedIndices:p}=LZ(u,c,a,i,l);return t.makeTensorInfo([p.length],"int32",new Int32Array(p))}var QO={kernelName:xi,backendName:"webgl",kernelFunc:zZ};var BZ=Tr.nonMaxSuppressionV4Impl;function VZ(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,padToMaxOutputSize:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),{selectedIndices:m,validOutputs:f}=BZ(c,p,a,i,l,u);return[t.makeTensorInfo([m.length],"int32",new Int32Array(m)),t.makeTensorInfo([],"int32",new Int32Array([f]))]}var eP={kernelName:yi,backendName:"webgl",kernelFunc:VZ};var GZ=Tr.nonMaxSuppressionV5Impl;function WZ(r){N.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:e,backend:t,attrs:o}=r,{boxes:n,scores:s}=e,{maxOutputSize:a,iouThreshold:i,scoreThreshold:l,softNmsSigma:u}=o,c=t.readSync(n.dataId),p=t.readSync(s.dataId),m=a,f=i,d=l,h=u,{selectedIndices:g,selectedScores:y}=GZ(c,p,m,f,d,h);return[t.makeTensorInfo([g.length],"int32",new Int32Array(g)),t.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var tP={kernelName:bi,backendName:"webgl",kernelFunc:WZ};var EC=class{constructor(e,t,o,n){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${n}), float(${o}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}};var UZ=r=>{let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=x.sizeFromShape(n.shape),u=new EC(l,s,a,i),c=pe({inputs:{x:n},backend:t,attrs:{shape:[l]}}),p=t.runWebGLProgram(u,[c],n.dtype);t.disposeIntermediateTensorInfo(c);let m=[...n.shape,s],f=pe({inputs:{x:p},backend:t,attrs:{shape:m}});return t.disposeIntermediateTensorInfo(p),f},rP={kernelName:yn,backendName:"webgl",kernelFunc:UZ};function ih(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="complex64"){let n=Ga({inputs:{input:o},backend:t}),s=ih({inputs:{x:n},backend:t}),a=lc({inputs:{input:o},backend:t}),i=ih({inputs:{x:a},backend:t}),l=go({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return sh({attrs:{shape:o.shape,dtype:o.dtype,value:o.dtype==="string"?"":0},backend:t})}var oP={kernelName:bs,backendName:"webgl",kernelFunc:ih};function nP(r){let{inputs:e,backend:t}=r,{x:o}=e;if(o.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(o.dtype==="complex64"){let n=Ga({inputs:{input:o},backend:t}),s=nP({inputs:{x:n},backend:t}),a=lc({inputs:{input:o},backend:t}),i=ih({inputs:{x:a},backend:t}),l=go({inputs:{real:s,imag:i},backend:t});return t.disposeIntermediateTensorInfo(n),t.disposeIntermediateTensorInfo(s),t.disposeIntermediateTensorInfo(a),t.disposeIntermediateTensorInfo(i),l}else return sh({attrs:{shape:o.shape,dtype:o.dtype,value:1},backend:t})}var sP={kernelName:ms,backendName:"webgl",kernelFunc:nP};function jZ(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Vx({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{x.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),x.assert(a===c.dtype,()=>"All tensors passed to stack must have matching dtypes")});let i=[],l=e.map(c=>{let p=Vx({inputs:{input:c},backend:t,attrs:{dim:n}});return i.push(p),p}),u=Jv({inputs:l,backend:t,attrs:{axis:n}});return i.forEach(c=>t.disposeIntermediateTensorInfo(c)),u}var iP={kernelName:fs,backendName:"webgl",kernelFunc:jZ};var DC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=t.map((u,c)=>u[0]+e[c]+u[1]);let n=e.length,s=Le(n),a=t.map(u=>u[0]).join(","),i=t.map((u,c)=>u[0]+e[c]).join(","),l=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,n);if(n===1){this.userCode=`
|
|
int start = ${a};
|
|
int end = ${i};
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(float(${o}));
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${s} start = ${s}(${a});
|
|
${s} end = ${s}(${i});
|
|
|
|
void main() {
|
|
${s} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(float(${o}));
|
|
} else {
|
|
${s} coords = outC - start;
|
|
setOutput(getX(${l}));
|
|
}
|
|
}
|
|
`}};var $C=class{constructor(e,t,o){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,g)=>h[0]+e[g]+h[1]);let n=e.length,s=Le(n),a=t.map(h=>h[0]).join(","),i=t.map((h,g)=>h[0]+e[g]).join(","),l=Ut("rc",n),u=Ut("source",n),c=`${l[n-1]} < ${this.outputShape[n-1]}`,p=n===1?"source":`vec2(${u.slice(-2).join()})`,m=[`${s} rc = outputLoc;`,`${l[n-1]} += 1;
|
|
if(${c}) {
|
|
`,n===1?"":`}
|
|
rc = outputLoc;
|
|
${l[n-2]} += 1;
|
|
if(${l[n-2]} < ${this.outputShape[n-2]}) {`,n===1?"":` ${l[n-1]} += 1;
|
|
if(${c}) {`],f=n===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",d="";for(let h=0,g=n===1?2:4;h<g;h++)d+=`
|
|
${m[h]}
|
|
if (${f}) {
|
|
result[${h}] = float(${o});
|
|
} else {
|
|
${s} source = rc - start;
|
|
result[${h}] = getChannel(getX(${u.join()}), ${p});
|
|
}
|
|
`;d+=n===1?"} ":"}}",this.userCode=`
|
|
const ${s} start = ${s}(${a});
|
|
const ${s} end = ${s}(${i});
|
|
|
|
void main() {
|
|
${s} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${d}
|
|
setOutput(result);
|
|
}
|
|
`}};var RC=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{paddings:s,constantValue:a}=o,i=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new $C(n.shape,s,a):new DC(n.shape,s,a);return t.runWebGLProgram(i,[n],n.dtype)},aP={kernelName:bn,backendName:"webgl",kernelFunc:RC};var HZ=`
|
|
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);
|
|
`,qZ=`
|
|
// 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));
|
|
`+Sl+`
|
|
return result;
|
|
`,KZ=nt({opSnippet:HZ,packedOpSnippet:qZ}),lP={kernelName:wn,backendName:"webgl",kernelFunc:KZ};function XZ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{axis:s,keepDims:a}=o,i=n.shape.length,l=[],u=x.parseAxisParam(s,n.shape),c=u,p=N.getAxesPermutation(c,i),m=n;p!=null&&(m=Mt({inputs:{x:n},backend:t,attrs:{perm:p}}),c=N.getInnerMostAxes(c.length,i),l.push(m)),N.assertAxesAreInnerMostDims("prod",c,i);let f;if(t.shouldExecuteOnCPU([m])){let d=t.texData.get(m.dataId).values,{outVals:h,outShape:g,outDtype:y}=uR(m.shape,m.dtype,d,c);f=t.makeTensorInfo(g,y,h)}else{let[d,h]=N.computeOutAndReduceShapes(m.shape,c),g=x.sizeFromShape(h),y=pe({inputs:{x:m},backend:t,attrs:{shape:[-1,g]}}),b=mu(n.dtype),w=To(y,b,"prod",t);f=pe({inputs:{x:w},backend:t,attrs:{shape:d}}),l.push(y),l.push(w)}if(a){l.push(f);let d=N.expandShapeToKeepDim(f.shape,u);f=pe({inputs:{x:f},backend:t,attrs:{shape:d}})}return l.forEach(d=>t.disposeIntermediateTensorInfo(d)),f}var uP={kernelName:wi,backendName:"webgl",kernelFunc:XZ};var FC=r=>{let{backend:e,attrs:t}=r,{start:o,stop:n,step:s,dtype:a}=t,i=cR(o,n,s,a);return e.makeTensorInfo([i.length],a,i)},cP={kernelName:ca,backendName:"webgl",kernelFunc:FC};var YZ="return 1.0 / x;",ZZ=_e({opSnippet:YZ}),pP={kernelName:_i,backendName:"webgl",kernelFunc:ZZ};var JZ=xr+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,QZ=`
|
|
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;
|
|
`,eJ=_e({opSnippet:JZ,packedOpSnippet:QZ}),mP={kernelName:kn,backendName:"webgl",kernelFunc:eJ};var tJ=xr+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,rJ=`
|
|
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;
|
|
`,oJ=_e({opSnippet:tJ,packedOpSnippet:rJ}),fP={kernelName:Cn,backendName:"webgl",kernelFunc:oJ};var OC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":m="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${c[0]/p[0]},
|
|
${c[1]/p[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${m};
|
|
|
|
// 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);
|
|
}
|
|
`}};var PC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m;s?m="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":m="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${c[0]/p[0]},
|
|
${c[1]/p[1]},
|
|
${c[1]/p[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${l}.0,
|
|
${l}.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 = ${m};
|
|
|
|
// 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 < ${u-1};
|
|
bool hasNextRow = coords.z < ${o-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 nJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=W().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new PC(n.shape,l,u,s,a):new OC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],"float32")}var dP={kernelName:vn,backendName:"webgl",kernelFunc:nJ};var MC=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*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(${p});
|
|
|
|
const float invHeightScale = float(${m});
|
|
const float invWidthScale = float(${f});
|
|
|
|
const int winHeight = int(${d});
|
|
const int winWidth = int(${h});
|
|
|
|
// 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 >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float dxR = float(dyR) * heightScale;
|
|
int topDxRIndex = int(floor(dxR));
|
|
int bottomDxRIndex = int(min(ceil(dxR), ${n-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), ${s-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 sJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new MC(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var hP={kernelName:lu,backendName:"webgl",kernelFunc:sJ};var LC=class{constructor(e,t,o,n,s){this.variableNames=["A"],this.outputShape=[];let[a,i,l,u]=e;this.outputShape=[a,t,o,u];let c=[n&&t>1?i-1:i,n&&o>1?l-1:l],p=[n&&t>1?t-1:t,n&&o>1?o-1:o],m=n?"0.5":"0.0",f;s?f="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":f="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${c[0]/p[0]},
|
|
${c[1]/p[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${l}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${f};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${m})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function iJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n}=e,{alignCorners:s,halfPixelCenters:a,size:i}=o,[l,u]=i,c=new LC(n.shape,l,u,s,a);return t.runWebGLProgram(c,[n],n.dtype)}var gP={kernelName:pa,backendName:"webgl",kernelFunc:iJ};var zC=class{constructor(e,t,o){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,n,s]=t,[,a,i]=e,l=[o&&a>1?n-1:n,o&&i>1?s-1:s],u=[o&&a>1?a-1:a,o&&i>1?i-1:i],c=l[0]/u[0],p=l[1]/u[1],m=1/c,f=1/p,d=Math.ceil(m)*2+2,h=Math.ceil(f)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${c});
|
|
const float widthScale = float(${p});
|
|
|
|
const float invHeightScale = float(${m});
|
|
const float invWidthScale = float(${f});
|
|
|
|
const int winHeight = int(${d});
|
|
const int winWidth = int(${h});
|
|
|
|
// 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 >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${l[0]}) *
|
|
(float(dyR) / float(${u[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${l[1]}) *
|
|
(float(dyC) / float(${u[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${n}) - 1),
|
|
${o} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${s}) - 1),
|
|
${o} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function aJ(r){let{inputs:e,backend:t,attrs:o}=r,{images:n,dy:s}=e,{alignCorners:a}=o,i=new zC(s.shape,n.shape,a);return t.runWebGLProgram(i,[s],s.dtype)}var xP={kernelName:au,backendName:"webgl",kernelFunc:aJ};var BC=class{constructor(e,t){this.variableNames=["x"];let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);if(this.outputShape=e,o===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let n=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,s=e.map((i,l)=>n(l)).join(","),a=Le(o);this.userCode=`
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${s}));
|
|
}
|
|
`}};var VC=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let o=e.length;if(o>4)throw new Error(`WebGL backend: Reverse of rank-${o} tensor is not yet supported`);this.outputShape=e;let n=Ut("rc",o),s=`${n[o-1]} + 1 < ${this.outputShape[o-1]}`,a=`${n[o-2]} + 1 < ${this.outputShape[o-2]}`,i=Le(o);o===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${e[0]} - rc - 1),
|
|
${e[0]} - rc - 1);
|
|
if(${s}){
|
|
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
|
|
${e[0]} - (rc + 1) - 1);
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`:this.userCode=`
|
|
void main() {
|
|
${i} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${l(n.slice())};
|
|
if(${s}){
|
|
result.g = ${u(n.slice())};
|
|
}
|
|
if(${a}) {
|
|
result.b = ${c(n.slice())};
|
|
if(${s}) {
|
|
result.a = ${p(n.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function l(d){return m(d)}function u(d){return d[o-1]="("+d[o-1]+" + 1)",m(d)}function c(d){return d[o-2]="("+d[o-2]+" + 1)",m(d)}function p(d){return d[o-1]="("+d[o-1]+" + 1)",d[o-2]="("+d[o-2]+" + 1)",m(d)}function m(d){let h=e.map((b,w)=>f(w,d)),g=h.join(","),y=h.slice(-2).join(",");return`getChannel(getX(${g}), vec2(${y}))`}function f(d,h){return t.indexOf(d)!==-1&&e[d]!==1?`${e[d]} - ${h[d]} - 1`:`${h[d]}`}}};function lJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{dims:s}=o,a=n.shape.length,i=x.parseAxisParam(s,n.shape);if(a===0)return jt({inputs:{x:n},backend:t});let l=W().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new VC(n.shape,i):new BC(n.shape,i);return t.runWebGLProgram(l,[n],n.dtype)}var yP={kernelName:In,backendName:"webgl",kernelFunc:lJ};var GC=class{constructor(e,t,o,n){this.variableNames=["Image"],this.outputShape=[];let s=e[1],a=e[2],i=Math.sin(t).toFixed(3),l=Math.cos(t).toFixed(3);this.outputShape=e;let[u,c]=N.getImageCenter(n,s,a),p=u.toFixed(3),m=c.toFixed(3),f="";typeof o=="number"?f=`float outputValue = ${o.toFixed(2)};`:f=`
|
|
vec3 fill = vec3(${o.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) - ${p}) * ${l} - (float(y) - ${m}) * ${i};
|
|
float coordYFloat = (float(x) - ${p}) * ${i} + (float(y) - ${m}) * ${l};
|
|
int coordX = int(round(coordXFloat + ${p}));
|
|
int coordY = int(round(coordYFloat + ${m}));
|
|
${f}
|
|
if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${s}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};var bP={kernelName:Ei,backendName:"webgl",kernelFunc:({inputs:r,attrs:e,backend:t})=>{let{image:o}=r,{radians:n,fillValue:s,center:a}=e,i=t,l=new GC(o.shape,n,s,a);return i.runWebGLProgram(l,[o],o.dtype)}};var uJ=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,cJ=_e({opSnippet:uJ}),wP={kernelName:Nn,backendName:"webgl",kernelFunc:cJ};var pJ="return inversesqrt(x);",mJ=_e({opSnippet:pJ,cpuKernelImpl:pR}),_P={kernelName:Sn,backendName:"webgl",kernelFunc:mJ};var ah=class{constructor(e,t,o,n,s,a,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;let l=Le(s.length),u=Le(a.length),c="";o===1?c="i":o===2&&(c="i, j");let p=`getIndices(${c})`,m="";n===1?m="i":n===2&&(m="i, coords[1]");let f=`getUpdates(${m})`,d=t>1?"strides[j]":"strides";this.userCode=`
|
|
${l} strides = ${l}(${s});
|
|
|
|
void main() {
|
|
${u} coords = getOutputCoords();
|
|
float sum = 0.0;
|
|
bool found = false;
|
|
for (int i = 0; i < ${e}; i++) {
|
|
int flattenedIndex = 0;
|
|
for (int j = 0; j < ${t}; j++) {
|
|
int index = round(${p});
|
|
flattenedIndex += index * ${d};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${f};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function fJ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n,updates:s}=e,{shape:a}=o,{sliceRank:i,numUpdates:l,sliceSize:u,strides:c,outputSize:p}=N.calculateShapes(s,n,a),m=[p/u,u];if(p===0)return t.makeTensorInfo(a,n.dtype);let f=pe({inputs:{x:n},backend:t,attrs:{shape:[l,i]}}),d=pe({inputs:{x:s},backend:t,attrs:{shape:[l,u]}}),h=t.makeTensorInfo([],"float32",new Float32Array([0])),g=new ah(l,i,f.shape.length,d.shape.length,c,m),y=t.runWebGLProgram(g,[d,f,h],d.dtype),b=pe({inputs:{x:y},backend:t,attrs:{shape:a}});return t.disposeIntermediateTensorInfo(f),t.disposeIntermediateTensorInfo(d),t.disposeIntermediateTensorInfo(y),t.disposeIntermediateTensorInfo(h),b}var kP={kernelName:ki,backendName:"webgl",kernelFunc:fJ};var WC=class{constructor(e,t,o){this.variableNames=["c","a","b"],this.outputShape=t;let n,s;if(o>4)throw Error(`Where for rank ${o} is not yet supported`);if(o===1)s="resRC",n="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],l=[],u=[];for(let c=0;c<t.length;c++)u.push(`${i[c]}`),c<e&&l.push(`${i[c]}`);n=l.join(),s=u.join()}let a=Le(o);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
float cVal = getC(${n});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${s}));
|
|
} else {
|
|
setOutput(getB(${s}));
|
|
}
|
|
}
|
|
`}};function dJ(r){let{inputs:e,backend:t}=r,{condition:o,t:n,e:s}=e,a=new WC(o.shape.length,n.shape,n.shape.length);return t.runWebGLProgram(a,[o,n,s],fr(n.dtype,s.dtype))}var vP={kernelName:hs,backendName:"webgl",kernelFunc:dJ};var hJ=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${N.SELU_SCALEALPHA};
|
|
float scale = ${N.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,gJ=_e({opSnippet:hJ}),CP={kernelName:vi,backendName:"webgl",kernelFunc:gJ};var xJ="return 1.0 / (1.0 + exp(-1.0 * x));",yJ=_e({opSnippet:xJ}),IP={kernelName:An,backendName:"webgl",kernelFunc:yJ};var bJ=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,wJ=_e({opSnippet:bJ}),NP={kernelName:Ii,backendName:"webgl",kernelFunc:wJ};var _J=$x+`
|
|
return sin(x);
|
|
`,kJ=_e({opSnippet:_J}),SP={kernelName:Tn,backendName:"webgl",kernelFunc:kJ};var vJ=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,CJ=_e({opSnippet:vJ}),TP={kernelName:Ci,backendName:"webgl",kernelFunc:CJ};var IJ=`
|
|
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;
|
|
`,NJ=_e({opSnippet:IJ}),AP={kernelName:Ni,backendName:"webgl",kernelFunc:NJ};var SJ=r=>{let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{blockShape:s,paddings:a}=o;x.assert(n.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let i=s.reduce((y,b)=>y*b),l=[[0,0]];l.push(...a);for(let y=1+s.length;y<n.shape.length;++y)l.push([0,0]);let u=[],c=RC({inputs:{x:n},backend:t,attrs:{paddings:l,constantValue:0}}),p=N.getReshaped(c.shape,s,i,!1),m=N.getPermuted(p.length,s.length,!1),f=N.getReshapedPermuted(c.shape,s,i,!1),d=pe({inputs:{x:c},backend:t,attrs:{shape:p}}),h=Mt({inputs:{x:d},backend:t,attrs:{perm:m}}),g=pe({inputs:{x:h},backend:t,attrs:{shape:f}});return u.push(c),u.push(d),u.push(h),u.forEach(y=>t.disposeIntermediateTensorInfo(y)),g},EP={kernelName:ma,backendName:"webgl",kernelFunc:SJ};function TJ(r){let{inputs:e,backend:t,attrs:o}=r,{sparseIndices:n,sparseValues:s,defaultValue:a}=e,{outputShape:i}=o,{sliceRank:l,numUpdates:u,strides:c,outputSize:p}=N.calculateShapes(s,n,i),m=!1,f=new ah(u,l,n.shape.length,s.shape.length,c,[p,1],m),d=t.runWebGLProgram(f,[s,n,a],s.dtype),h=pe({inputs:{x:d},backend:t,attrs:{shape:i}});return t.disposeIntermediateTensorInfo(d),h}var DP={kernelName:uu,backendName:"webgl",kernelFunc:TJ};function AJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{numOrSizeSplits:s,axis:a}=o,i=x.parseAxisParam(a,n.shape)[0],l=N.prepareSplitSize(n,s,i),u=n.shape.length,c=new Array(u).fill(0),p=n.shape.slice();return l.map(m=>{let f=[...p];f[i]=m;let d=Va({inputs:{x:n},backend:t,attrs:{begin:c,size:f}});return c[i]+=m,d})}var $P={kernelName:xs,backendName:"webgl",kernelFunc:AJ};var EJ="return sqrt(x);",DJ=_e({opSnippet:EJ}),RP={kernelName:En,backendName:"webgl",kernelFunc:DJ};var $J="return x * x;",RJ=_e({opSnippet:$J}),FP={kernelName:fa,backendName:"webgl",kernelFunc:RJ};var OP="return (a - b) * (a - b);",FJ=nt({opSnippet:OP,packedOpSnippet:OP}),PP={kernelName:Rn,backendName:"webgl",kernelFunc:FJ};function OJ({inputs:r,attrs:e,backend:t}){let{x:o}=r,n=xr+`
|
|
return x > 0.0 ? 1.0 : float(${e.alpha});
|
|
`,s=new ho(o.shape,n);return t.runWebGLProgram(s,[o],o.dtype)}var MP={kernelName:Fo,backendName:"webgl",kernelFunc:OJ};var UC=class{constructor(e,t,o){this.variableNames=["x"],this.outputShape=o;let n=o.length,s=Le(o.length),a=Le(o.length),i="";if(n===1)i="coords * strides + begin";else{let l=0;i=o.map((u,c)=>(l++,o.length===1?`coords * strides[${c}] + begin[${c}]`:`coords[${l-1}] * strides[${c}] + begin[${c}]`)).join(",")}this.userCode=`
|
|
${s} begin = ${s}(${e});
|
|
${s} strides = ${s}(${t});
|
|
|
|
void main() {
|
|
${a} coords = getOutputCoords();
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}};function PJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{begin:s,end:a,strides:i,beginMask:l,endMask:u,ellipsisMask:c,newAxisMask:p,shrinkAxisMask:m}=o,{nonStrided:f,$begin:d,$strides:h,size:g,newShape:y,outShape:b}=nr.sliceInfo(n.shape,s,a,i,l,u,c,p,m),w=pe({inputs:{x:n},backend:t,attrs:{shape:y}}),_;if(f){let D=Va({inputs:{x:w},backend:t,attrs:{begin:d,size:g}});_=pe({inputs:{x:D},backend:t,attrs:{shape:b}}),t.disposeIntermediateTensorInfo(D)}else if(b.some(D=>D===0))_=t.makeTensorInfo(b,n.dtype,[]);else if(t.shouldExecuteOnCPU([w])){let R=t.texData.get(w.dataId).values,P=ve(w.shape,w.dtype,R),B=fR(b,P,h,d);_=t.makeTensorInfo(b,w.dtype,B.values)}else{let T=new UC(d,h,b);_=t.runWebGLProgram(T,[w],w.dtype)}let C=pe({inputs:{x:_},backend:t,attrs:{shape:b}});return t.disposeIntermediateTensorInfo(w),t.disposeIntermediateTensorInfo(_),C}var LP={kernelName:Si,backendName:"webgl",kernelFunc:PJ};var MJ="return tan(x);",LJ=_e({opSnippet:MJ}),zP={kernelName:Ti,backendName:"webgl",kernelFunc:LJ};var zJ=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,BJ=_e({opSnippet:zJ}),BP={kernelName:On,backendName:"webgl",kernelFunc:BJ};var jC=class{constructor(e,t){this.variableNames=["A"];let o=new Array(e.length);for(let a=0;a<o.length;a++)o[a]=e[a]*t[a];this.outputShape=o,this.rank=o.length;let n=Le(this.rank),s=VJ(e);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${s}));
|
|
}
|
|
`}};function VJ(r){let e=r.length;if(e>5)throw Error(`Tile for rank ${e} is not yet supported`);if(e===1)return`imod(resRC, ${r[0]})`;let t=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],o=[];for(let n=0;n<r.length;n++)o.push(`imod(${t[n]}, ${r[n]})`);return o.join()}function HC(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{reps:s}=o;if(n.dtype==="string"){let u=t.readSync(n.dataId).map(m=>x.decodeString(m)),c=ve(n.shape,n.dtype,u),p=hR(c,s);return t.makeTensorInfo(p.shape,p.dtype,p.values)}let a=new jC(n.shape,s);return t.runWebGLProgram(a,[n],n.dtype)}var VP={kernelName:_o,backendName:"webgl",kernelFunc:HC};function GJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n}=e,{k:s,sorted:a}=o,i=t.readSync(n.dataId),[l,u]=gR(i,n.shape,n.dtype,s,a);return[t.makeTensorInfo(l.shape,l.dtype,l.values),t.makeTensorInfo(u.shape,u.dtype,u.values)]}var GP={kernelName:Ai,backendName:"webgl",kernelFunc:GJ};function WJ(r){let{inputs:e,attrs:t,backend:o}=r,{axis:n}=t,{x:s}=e;qi(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let a=o.readSync(s.dataId),{outputValues:i,outputShape:l,indices:u}=xR(a,n,s.shape,s.dtype);return[o.makeTensorInfo(l,s.dtype,i),o.makeTensorInfo([u.length],"int32",u)]}var WP={kernelName:cu,backendName:"webgl",kernelFunc:WJ};function UJ(r){let{inputs:e,backend:t,attrs:o}=r,{value:n}=e,{axis:s}=o;s<0&&(s+=n.shape.length);let a=n,i=a.shape.length,l=n.shape[s],u=new Array(i-1),c=0;for(let h=0;h<i;h++)h!==s&&(u[c++]=a.shape[h]);let p=[],m=new Array(i).fill(0),f=a.shape.slice();f[s]=1;let d=new Array(l);for(let h=0;h<d.length;h++){m[s]=h;let g=Va({inputs:{x:a},backend:t,attrs:{begin:m,size:f}}),y=pe({inputs:{x:g},backend:t,attrs:{shape:u}});d[h]=y,p.push(g)}return p.forEach(h=>t.disposeIntermediateTensorInfo(h)),d}var UP={kernelName:ys,backendName:"webgl",kernelFunc:UJ};var qC=class{constructor(e,t){this.variableNames=["x","segmentIds"];let o=e.windowSize,n=e.batchSize,s=e.inSize,a=e.numSegments,i=a*Math.ceil(s/o);this.outputShape=[n,i];let l="0.0",u="sumValue",c=Math.floor(o/4)*4,p=o%4,m=`
|
|
sumValue += dot(values, segFilter);
|
|
`,f="";s%o>0&&(f=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
return initializationValue;
|
|
}
|
|
`);let d="";s%o>0&&(d=`
|
|
if (inIdx < 0 || inIdx >= ${s}) {
|
|
return -1.0;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${l};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${f}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
float getSegmentIdAtIndex(int inIdx) {
|
|
${d}
|
|
return getSegmentIds(inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = int(floor(float(outIdx) / float(
|
|
${a})) * float(${o}));
|
|
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
|
|
);
|
|
|
|
${m}
|
|
}
|
|
|
|
int inIdx = inOffset + ${c};
|
|
if (${p===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
|
|
);
|
|
|
|
${m}
|
|
} else if (${p===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
|
|
);
|
|
|
|
${m}
|
|
} else if (${p===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
|
|
);
|
|
|
|
${m}
|
|
}
|
|
setOutput(${u});
|
|
}
|
|
`}};function jJ(r){let{inputs:e,backend:t,attrs:o}=r,{x:n,segmentIds:s}=e,{numSegments:a}=o,i=n.shape.length,l=[],u=0,c=N.getAxesPermutation([u],i),p=n;c!=null&&(p=Mt({inputs:{x:n},backend:t,attrs:{perm:c}}),l.push(p),u=N.getInnerMostAxes(1,i)[0]);let m=N.segment_util.computeOutShape(p.shape,u,a),f=x.sizeFromShape([p.shape[u]]),d=pe({inputs:{x:p},backend:t,attrs:{shape:[-1,f]}});l.push(d);let h=mu(n.dtype),g=(_,C,D,T,R)=>{let P=_.shape[0],B=_.shape[1],G=N.segment_util.segOpComputeOptimalWindowSize(B,R),U={windowSize:G,inSize:B,batchSize:P,numSegments:R},j=new qC(U,C),H=t.compileAndRun(j,[_,D],T);if(l.push(H),H.shape[1]===R)return H;let K=FC({backend:t,attrs:{start:0,stop:R,step:1,dtype:"float32"}}),X=HC({inputs:{x:K},backend:t,attrs:{reps:[B/G]}});return l.push(K),l.push(X),g(H,C,X,T,R)},y=g(d,"unsortedSegmentSum",s,h,a),b=pe({inputs:{x:y},backend:t,attrs:{shape:m}}),w=b;if(c!=null){l.push(b);let _=N.getUndoAxesPermutation(c);w=Mt({inputs:{x:w},backend:t,attrs:{perm:_}})}return l.forEach(_=>t.disposeIntermediateTensorInfo(_)),w}var jP={kernelName:da,backendName:"webgl",kernelFunc:jJ};var HJ=[AO,EO,UR,HR,qR,KR,YR,ZR,JR,QR,rF,oF,nF,sF,aF,iF,lF,cF,uF,pF,mF,fF,dF,gF,xF,_F,vF,CF,NF,$R,AF,DF,$F,EF,FF,OF,RF,PF,MF,LF,VF,GF,WF,jF,HF,UF,qF,KF,XF,YF,ZF,JF,eO,tO,oO,nO,sO,iO,lO,uO,cO,pO,mO,fO,dO,hO,gO,DR,xO,SF,yO,bO,wO,RR,_O,kO,vO,IO,CO,NO,SO,TO,$O,OO,FO,PO,MO,zO,RO,VO,GO,WO,UO,jO,YO,LR,JO,QO,eP,tP,yF,rP,sP,iP,aP,lP,FR,uP,cP,bF,HO,pP,fP,mP,BR,dP,hP,gP,xP,yP,bP,wP,_P,kP,vP,CP,IP,NP,SP,TP,hF,XO,AP,EP,DP,$P,RP,FP,PP,MP,LP,KO,GR,zP,BP,VP,GP,WR,WP,UP,jP,oP];for(let r of HJ)Ja(r);var HP="3.2.0";var qJ={"tfjs-core":Jb,"tfjs-backend-cpu":TE,"tfjs-backend-webgl":ER,"tfjs-data":gx,"tfjs-layers":dl,"tfjs-converter":ux,tfjs:HP};var Lt;(function(r){r[r.float32=0]="float32",r[r.int32=1]="int32",r[r.bool=2]="bool",r[r.string=3]="string",r[r.complex64=4]="complex64"})(Lt||(Lt={}));var El;(function(r){r[r.linear=0]="linear",r[r.relu=1]="relu",r[r.relu6=2]="relu6",r[r.prelu=3]="prelu",r[r.leakyrelu=4]="leakyrelu"})(El||(El={}));var qP;function KJ(r){qP=r.wasm.cwrap(ws,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function XJ(r){let{inputs:e,backend:t,attrs:o}=r,{a:n,b:s,bias:a,preluActivationWeights:i}=e;if(n.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:l,transposeB:u,activation:c,leakyreluAlpha:p}=o,m=t.dataIdMap.get(n.dataId).id,f=t.dataIdMap.get(s.dataId).id,d=0;if(a!=null){let R=t.dataIdMap.get(a.dataId);if(R.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${R.shape.length}.`);d=R.id}let h=i==null?0:t.dataIdMap.get(i.dataId).id,g=El[c];if(g==null)throw new Error(`${c} activation not yet supported for FusedConv2D in the wasm backend.`);let y=l?n.shape[2]:n.shape[1],b=u?s.shape[1]:s.shape[2],w=n.shape[0],_=t.makeOutput([w,y,b],n.dtype),C=t.dataIdMap.get(_.dataId).id,D=new Uint8Array(new Int32Array(n.shape).buffer),T=new Uint8Array(new Int32Array(s.shape).buffer);return qP(m,D,n.shape.length,f,T,s.shape.length,l,u,g,d,h,p||0,C),_}var KP={kernelName:ws,backendName:"wasm",setupFunc:KJ,kernelFunc:XJ};function St(r){let e;function t(n){e=n.wasm.cwrap(r,null,["number","number"])}function o(n){let{backend:s,inputs:{x:a}}=n,i=s.dataIdMap.get(a.dataId).id,l=s.makeOutput(a.shape,a.dtype),u=s.dataIdMap.get(l.dataId).id;return x.sizeFromShape(l.shape)===0||e(i,u),l}return{kernelName:r,backendName:"wasm",setupFunc:t,kernelFunc:o}}var XP=St(as);function bt(r,e,t){let o;function n(a){o=a.wasm.cwrap(r,null,["number","array","number","number","array","number","number","number"])}function s(a){let{backend:i,inputs:l}=a,{a:u,b:c}=l,p=i.dataIdMap.get(u.dataId).id,m=i.dataIdMap.get(c.dataId).id,f=t!=null?t:u.dtype,d=N.assertAndGetBroadcastShape(u.shape,c.shape),h=i.makeOutput(d,f);if(x.sizeFromShape(d)===0)return h;let g=new Uint8Array(new Int32Array(u.shape).buffer),y=new Uint8Array(new Int32Array(c.shape).buffer),b=i.dataIdMap.get(h.dataId).id,w=()=>o(p,g,u.shape.length,m,y,c.shape.length,Lt[u.dtype],b);if(e&&u.dtype==="float32")return w(),h;let _=N.getBroadcastDims(u.shape,d),C=N.getBroadcastDims(c.shape,d),D=_.every((R,P)=>R===P),T=C.every((R,P)=>R===P);if(D&&T)return w(),h;throw new Error(`Broadcasting along outer dims is not yet supported for ${u.dtype} ${r}.`)}return{kernelName:r,backendName:"wasm",setupFunc:n,kernelFunc:s}}var YJ=!0,YP=bt(wo,YJ);var ZP;function ZJ(r){ZP=r.wasm.cwrap(Ho,null,["array","number","number","number"])}function JJ(r){let{inputs:e,backend:t}=r,o=t.makeOutput(e[0].shape,e[0].dtype);if(x.sizeFromShape(o.shape)===0)return o;let n=e.map(i=>t.dataIdMap.get(i.dataId).id),s=new Uint8Array(new Int32Array(n).buffer),a=t.dataIdMap.get(o.dataId).id;return ZP(s,n.length,Lt[o.dtype],a),o}var JP={kernelName:Ho,backendName:"wasm",setupFunc:ZJ,kernelFunc:JJ};function cc(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype),n=t.typedArrayFromHeap(e);return t.typedArrayFromHeap(o).set(n),o}var QP={kernelName:Ro,backendName:"wasm",kernelFunc:cc};var eM;function QJ(r){eM=r.wasm.cwrap(Pn,null,["number","array","number","number","number","array","number"])}function qp(r){let{inputs:e,backend:t,attrs:o}=r,[n,s]=t9(e.x.shape,o.perm),a=!0;for(let d=0;d<s.length;d++)s[d]!==d&&(a=!1);let i=e9(e.x.shape,o.perm),l={dataId:e.x.dataId,shape:n,dtype:e.x.dtype};if(a){let d=cc({inputs:e,backend:t});return d.shape=i,d}let u=t.makeOutput(i,l.dtype),c=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(u.dataId).id,m=new Uint8Array(new Int32Array(s).buffer),f=new Uint8Array(new Int32Array(l.shape).buffer);return eM(c,f,l.shape.length,Lt[l.dtype],p,m,s.length),u}function e9(r,e){let t=new Array(r.length);for(let o=0;o<t.length;o++)t[o]=r[e[o]];return t}function t9(r,e){let t=[],o=[];for(let n=0;n<r.length;++n)r[n]!==1&&t.push(r[n]),r[e[n]]!==1&&o.push(e[n]);for(let n=0;n<o.length;++n){let s=-1;for(let a=0;a<o.length;++a)o[a]>=n&&(s===-1||o[s]>o[a])&&(s=a);o[s]=n}return[t,o]}var tM={kernelName:Pn,backendName:"wasm",kernelFunc:qp,setupFunc:QJ};function os(r,e,t){let o=r.shape,n=r.shape.length,s=x.parseAxisParam(e,o),a=s,i=N.getAxesPermutation(a,n),l=null,u=!1;if(i!=null){let c=new Array(n);for(let f=0;f<c.length;f++)c[f]=o[i[f]];a=N.getInnerMostAxes(a.length,n),l=qp({inputs:{x:r},attrs:{perm:i},backend:t});let p=t.dataIdMap.get(r.dataId).id;t.dataIdMap.get(l.dataId).id!==p&&(u=!0)}return{transposed:l,originalAxes:s,axes:a,inputWasTransposed:u}}var rM;function r9(r){rM=r.wasm.cwrap(qo,null,["number","number","number","number","number"])}function 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Please use 'channelsLast'.`);let G=o.makeOutput(f.outShape,"float32"),U=o.dataIdMap.get(G.dataId).id;return IM(a,n.shape[0],n.shape[1],n.shape[2],i,d,h,g,y,b,w,B,_,C,D,T,R,P,U),G}var NM={kernelName:tn,backendName:"wasm",setupFunc:w9,kernelFunc:_9};var k9=!1,SM=bt(oi,k9,"bool");var TM=St(on);function Ux(r){let{inputs:e,attrs:t,backend:o}=r,{input:n}=e,{dim:s}=t,a=n.shape.length,i=n.shape.slice(),l=s;return s<0&&(x.assert(-(a+1)<=s,()=>`Axis must be in the interval [${-(a+1)}, ${a}]`),l=a+s+1),i.splice(l,0,1),Br({inputs:{x:n},backend:o,attrs:{shape:i}})}var AM={kernelName:us,backendName:"wasm",kernelFunc:Ux};function v9(r){let{attrs:{shape:e,value:t,dtype:o},backend:n}=r,s=n.makeOutput(e,o);return n.typedArrayFromHeap(s).fill(t),s}var EM={kernelName:ia,backendName:"wasm",kernelFunc:v9};var DM;function C9(r){DM=r.wasm.cwrap(si,null,["number","number","number","number","number","number"])}function I9(r){let{inputs:e,backend:t}=r,{image:o}=e,n=t.makeOutput(o.shape,o.dtype),s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,[i,l,u,c]=o.shape;return DM(s,i,l,u,c,a),n}var $M={kernelName:si,backendName:"wasm",kernelFunc:I9,setupFunc:C9};var RM=St(nn);var N9=!1,FM=bt(sn,N9);var OM;function S9(r){OM=r.wasm.cwrap(an,null,["number","number","number","number","number","number","number"])}function T9(r){let{backend:e,inputs:t,attrs:o}=r,{varianceEpsilon:n}=o,{x:s,mean:a,variance:i,offset:l,scale:u}=t,c=e.dataIdMap.get(s.dataId).id,p=e.dataIdMap.get(a.dataId).id,m=e.dataIdMap.get(i.dataId).id,f=l!=null?e.dataIdMap.get(l.dataId).id:0,d=u!=null?e.dataIdMap.get(u.dataId).id:0,h=e.makeOutput(s.shape,s.dtype);if(x.sizeFromShape(s.shape)===0)return h;let g=e.dataIdMap.get(h.dataId).id;return OM(c,p,m,f,d,n,g),h}var PM={kernelName:an,backendName:"wasm",setupFunc:S9,kernelFunc:T9};var MM;function A9(r){MM=r.wasm.cwrap(_s,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function E9(r){let{inputs:e,attrs:t,backend:o}=r,{x:n,filter:s,bias:a,preluActivationWeights:i}=e,{strides:l,pad:u,dilations:c,dataFormat:p,dimRoundingMode:m,activation:f,leakyreluAlpha:d}=t,h=N.computeConv2DInfo(n.shape,s.shape,l,c,u,m),g=El[f];if(g==null)throw new Error(`${f} activation not yet supported for FusedConv2D in the wasm backend.`);let y=o.dataIdMap.get(n.dataId).id,b=o.dataIdMap.get(s.dataId).id,w=h.outChannels,_=0;if(a!=null){let ae=o.dataIdMap.get(a.dataId);if(ae.shape.length!==1)throw new Error(`FusedConv2D only supports rank-1 bias but got rank ${ae.shape.length}.`);if(ae.shape[0]!==w)throw new Error(`FusedConv2D bias shape (${ae.shape}) does not match the number of output channels (${w})`);_=ae.id}let C=h.filterHeight,D=h.filterWidth,T=h.padInfo.top,R=h.padInfo.right,P=h.padInfo.bottom,B=h.padInfo.left,G=h.dilationHeight,U=h.dilationWidth,j=h.strideHeight,H=h.strideWidth,K=h.inChannels,X=h.padInfo.type==="SAME"?1:0,oe=h.batchSize,Y=h.inHeight,re=h.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedConv2D does not support dataFormat:'${p}'. 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gL={kernelName:bi,backendName:"wasm",setupFunc:sQ,kernelFunc:iQ};var aQ=!1,xL=bt(gi,aQ,"bool");var yL;function lQ(r){yL=r.wasm.cwrap(yn,null,["number","number","number","number","number"])}function uQ(r){let{inputs:e,backend:t,attrs:o}=r,{indices:n}=e,{depth:s,onValue:a,offValue:i}=o,l=t.makeOutput([...n.shape,s],"int32"),u=t.dataIdMap.get(l.dataId).id,p=t.dataIdMap.get(n.dataId).id;return yL(p,s,a,i,u),l}var bL={kernelName:yn,backendName:"wasm",setupFunc:lQ,kernelFunc:uQ};function cQ(r){let{inputs:{x:e},backend:t}=r,o=t.makeOutput(e.shape,e.dtype);return t.typedArrayFromHeap(o).fill(1),o}var wL={kernelName:ms,backendName:"wasm",kernelFunc:cQ};function pQ(r){let{inputs:e,backend:t,attrs:o}=r,{axis:n}=o;if(e.length===1)return Ux({inputs:{input:e[0]},backend:t,attrs:{dim:n}});let s=e[0].shape,a=e[0].dtype;e.forEach(c=>{x.assertShapesMatch(s,c.shape,"All tensors passed to stack must have matching shapes"),x.assert(a===c.dtype,()=>"All tensors passed to stack must have matching 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gQ(r){let{inputs:e,backend:t}=r,{x:o,alpha:n}=e,s=t.dataIdMap.get(o.dataId).id,a=t.dataIdMap.get(n.dataId).id,i=t.makeOutput(o.shape,"float32"),l=t.dataIdMap.get(i.dataId).id;return IL(s,a,l),i}var NL={kernelName:_n,backendName:"wasm",setupFunc:hQ,kernelFunc:gQ};var SL;function xQ(r){SL=r.wasm.cwrap(wi,null,["number","number","number","number"])}function yQ(r){let{backend:e,inputs:t,attrs:o}=r,{axis:n,keepDims:s}=o,{x:a}=t,i=e.dataIdMap.get(a.dataId).id,l=i,u=a,{transposed:c,axes:p,originalAxes:m,inputWasTransposed:f}=os(a,n,e),d=p;if(f){let w=e.dataIdMap.get(c.dataId).id;w!==i&&(u=c,l=w,d=N.getInnerMostAxes(d.length,u.shape.length))}N.assertAxesAreInnerMostDims("prod",d,u.shape.length);let[h,g]=N.computeOutAndReduceShapes(u.shape,d),y=x.sizeFromShape(g),b=e.makeOutput(h,u.dtype);if(x.sizeFromShape(u.shape)!==0){let w=e.dataIdMap.get(b.dataId).id;SL(l,y,Lt[b.dtype],w)}if(f&&e.disposeData(c.dataId),s){let w=N.expandShapeToKeepDim(b.shape,m);b.shape=w}return b}var 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Make sure you call `setWasmPath()` before you call `tf.setBackend()` or `tf.ready()`");Kx=r,rI=e}function nee(r,e=!1){if(ch)throw new Error("The WASM backend was already initialized. Make sure you call `setWasmPaths()` before you call `tf.setBackend()` or `tf.ready()`");if(typeof r=="string")uh=r;else{lh=r;let t=ree.filter(o=>lh[o]==null);if(t.length>0)throw new Error(`There were no entries found for the following binaries: ${t.join(",")}. Please either call setWasmPaths with a map providing a path for each binary, or with a string indicating the directory where all the binaries can be found.`)}rI=e}var see="3.2.0";var iee=2;hu("wasm",async()=>{let{wasm:r}=await kz();return new qx(r)},iee);export{as as Abs,js as Acos,Hs as Acosh,Qc as AdadeltaOptimizer,ep as AdagradOptimizer,tp as AdamOptimizer,rp as AdamaxOptimizer,wo as Add,Ho as AddN,Ml as All,Ll as Any,qo as ArgMax,ea as ArgMin,qs as Asin,Ks as Asinh,Xs as Atan,Zs as Atan2,Ys as Atanh,Ko as AvgPool,ta as AvgPool3D,Bl as AvgPool3DGrad,zl as AvgPoolGrad,qx as BackendWasm,Xo as BatchMatMul,ra as BatchToSpaceND,Vl as Bincount,yb as BroadcastTo,Ug as Callback,Ng as CallbackList,Do as Cast,Yo as Ceil,$o as ClipByValue,Gl as Complex,oa as ComplexAbs,ls as Concat,Zo as Conv2D,Wl as Conv2DBackpropFilter,Jo as Conv2DBackpropInput,na as Conv3D,Ul as Conv3DBackpropFilterV2,jl as Conv3DBackpropInputV2,Qo as Cos,Js as Cosh,Qs as CropAndResize,en as Cumsum,Tg as CustomCallback,Xa as DataStorage,Hl as DenseBincount,ei as DepthToSpace,tn as DepthwiseConv2dNative,ql as DepthwiseConv2dNativeBackpropFilter,Kl as DepthwiseConv2dNativeBackpropInput,Xl as Diag,sa as Dilation2D,Dc as Dilation2DBackpropFilter,Ec as Dilation2DBackpropInput,hb as ENV,Hg as EarlyStopping,ti as Elu,Yl as EluGrad,Fh as Environment,oi as Equal,ri as Erf,on as Exp,us as ExpandDims,ni as Expm1,Zl as FFT,ia as Fill,si as FlipLeftRight,nn as Floor,sn as FloorDiv,$c as FromPixels,an as FusedBatchNorm,_s as FusedConv2D,ks as FusedDepthwiseConv2D,ii as GatherNd,cs as GatherV2,lx as GraphModel,ai as Greater,ln as GreaterEqual,Sg as History,Jl as IFFT,Ro as Identity,Ql as Imag,Nt as InputSpec,li as IsFinite,ui as IsInf,ci as IsNan,Gs as KernelBackend,aa as LRN,tu as LRNGrad,gf as LayerVariable,No as LayersModel,un as LeakyRelu,pi as Less,mi as LessEqual,eu as LinSpace,cn as Log,fi as Log1p,bb as LogSoftmax,di as LogicalAnd,Ya as LogicalNot,Za as LogicalOr,pn as Max,fn as MaxPool,la as MaxPool3D,ou as MaxPool3DGrad,ru as MaxPoolGrad,nu as MaxPoolWithArgmax,mn as Maximum,dn as Mean,hn as Min,gn as Minimum,ua as MirrorPad,hi as Mod,op as MomentumOptimizer,su as Multinomial,xn as Multiply,ps as Neg,xi as NonMaxSuppressionV3,yi as NonMaxSuppressionV4,bi as NonMaxSuppressionV5,gi as NotEqual,RI as OP_SCOPE_SUFFIX,yn as OneHot,ms as OnesLike,Or as Optimizer,fs as Pack,bn as PadV2,WB as Pool,wn as Pow,_n as Prelu,wi as Prod,np as RMSPropOptimizer,mo as RNN,ca as Range,Cb as Rank,iu as Real,rn as RealDiv,_i as Reciprocal,Gt as Reduction,kn as Relu,Cn as Relu6,ds as Reshape,vn as ResizeBilinear,lu as ResizeBilinearGrad,pa as ResizeNearestNeighbor,au as ResizeNearestNeighborGrad,In as Reverse,Ei as RotateWithOffset,Nn as Round,Sn as Rsqrt,al as SGDOptimizer,ki as ScatterNd,hs as Select,vi as Selu,Wi as Sequential,An as Sigmoid,Ii as Sign,Tn as Sin,Ci as Sinh,gs as Slice,$n as Softmax,Ni as Softplus,ma as SpaceToBatchND,uu as SparseToDense,xs as SplitV,En as Sqrt,fa as Square,Rn as SquaredDifference,Fo as Step,Si as StridedSlice,Fn as Sub,Dn as Sum,zr as SymbolicTensor,Ti as Tan,On as Tanh,Ve as Tensor,lt as TensorBuffer,_o as Tile,Ai as TopK,Pn as Transpose,cu as Unique,ys as Unpack,da as UnsortedSegmentSum,el as Variable,bs as ZerosLike,ws as _FusedMatMul,It as abs,xm as acos,ym as acosh,Q as add,ew as addN,xu as all,ol as any,nl as argMax,bm as argMin,wm as asin,_m as asinh,km as atan,vm as atan2,Cm as atanh,wa as avgPool,Im as avgPool3d,Qb as backend,N as backend_util,VV as basicLSTMCell,Ln as batchNorm,nw as batchNorm2d,sw as batchNorm3d,iw as batchNorm4d,_a as batchToSpaceND,aw as bincount,XU as booleanMaskAsync,sl as broadcastTo,Uh as browser,ve as buffer,N1 as callbacks,ne as cast,Nm as ceil,sr as clipByValue,Oo as clone,ko as complex,Ye as concat,lw as concat1d,uw as concat2d,cw as concat3d,pw as concat4d,r_ as constraints,wu as conv1d,Kr as conv2d,_u as conv2dTranspose,Sm as conv3d,lG as conv3dTranspose,HB as copyRegisteredKernels,ka as cos,ku as cosh,Jm as cosineWindow,vu as cumsum,Xr as customGrad,Ek as data,mw as denseBincount,Yh as deprecationWarn,Tm as depthToSpace,Cs as depthwiseConv2d,A1 as deregisterOp,Lc as device_util,gG as diag,Am as dilation2d,eV as disableDeprecationWarnings,Te as dispose,tV as disposeVariables,me as div,Em as divNoNan,fw as dot,zw as dropout,Is as elu,Q3 as enableDebugMode,J3 as enableProdMode,Bw as enclosingPowerOfTwo,Po as engine,W as env,vo as equal,Dm as erf,Yt as exp,ir as expandDims,$m as expm1,Hc as eye,Ea as fft,va as fill,aV as findBackend,lV as findBackendFactory,Ns as floor,gu as floorDiv,Gn as fused,zn as gather,Lw as gatherND,jh as gather_util,sV as getBackend,Oh as getGradient,Fc as getKernel,pm as getKernelsForBackend,jG as grad,HG as grads,Qt as greater,io as greaterEqual,Pi as ifft,Cu as imag,Ds as image,n4 as inTopKAsync,a_ as initializers,Gg as input,Cr as io,Pu as irfft,dw as isFinite,hw as isInf,gw as isNaN,Et as keep,Tr as kernel_impls,W_ as layers,Ca as leakyRelu,Iu as less,zo as lessEqual,jw as linalg,xw as linspace,nA as loadGraphModel,f1 as loadLayersModel,Rm as localResponseNormalization,ar as log,Nu as log1p,yw as logSigmoid,Su as logSoftmax,Om as logSumExp,dr as logicalAnd,Ia as logicalNot,Tu as logicalOr,kw as logicalXor,Y4 as losses,We as matMul,cN as math,lr as max,Na as maxPool,Pm as maxPool3d,vw as maxPoolWithArgmax,Yr as maximum,dt as mean,Wc as memory,q_ as metrics,Oi as min,Ts as minimum,Mm as mirrorPad,Lm as mod,p1 as model,K_ as models,qc as moments,JU as movingAverage,O as mul,_W as multiRNNCell,Cw as multinomial,He as neg,Qm as nextFrame,zu as norm,Vn as notEqual,vs as oneHot,Ir as ones,er as onesLike,S as op,NW as outerProduct,Rr as pad,AW as pad1d,DW as pad2d,RW as pad3d,OW as pad4d,Iw as pool,Fr as pow,Ta as prelu,Gb as print,Au as prod,rV as profile,UW as rand,JW as randomGamma,og as randomNormal,As as randomUniform,Xc as range,nV as ready,il as real,Bm as reciprocal,hu as registerBackend,d1 as registerCallbackConstructor,_b as registerGradient,Ja as registerKernel,T1 as registerOp,X_ as regularizers,Nr as relu,Du as relu6,iV as removeBackend,M as reshape,Ht as reverse,aU as reverse1d,uU as reverse2d,pU as reverse3d,fU as reverse4d,Da as rfft,Vm as round,$u as rsqrt,ue as scalar,Mw as scatterND,Hh as scatter_util,Ru as selu,Gm as separableConv2d,m1 as sequential,J as serialization,NN as setBackend,uV as setPlatform,oee as setWasmPath,nee as setWasmPaths,Fw as setdiff1dAsync,qr as sigmoid,Wm as sign,X4 as signal,Fu as sin,Ou as sinh,Re as slice,Um as slice1d,ng as slice2d,jm as slice3d,Yc as slice4d,nr as slice_util,Aa as softmax,Ss as softplus,Sa as spaceToBatchND,Zm as sparseToDense,K4 as spectral,ur as split,gt as sqrt,Oe as square,Mu as squaredDifference,Co as squeeze,Bt as stack,Es as step,Hm as stridedSlice,ce as sub,ge as sum,mu as sumOutType,qm as tan,Fi as tanh,$r as tensor,Vt as tensor1d,Mi as tensor2d,Hb as tensor3d,zU as tensor4d,BU as tensor5d,VU as tensor6d,Mn as tensor_util,vN as test_util,V as tidy,Lo as tile,oV as time,Km as topk,ll as train,je as transpose,Lu as truncatedNormal,Zc as unique,jB as unregisterGradient,UB as unregisterKernel,Xm as unsortedSegmentSum,cr as unstack,fr as upcastType,x as util,qG as valueAndGrad,KG as valueAndGrads,Ow as variable,eg as variableGrads,qJ as version,ux as version_converter,Jb as version_core,dl as version_layers,see as version_wasm,Dt as where,Ym as whereAsync,ht as zeros,Ce as zerosLike};
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/**
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* @license
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* Copyright 2017 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2018 Google LLC
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*
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2018 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*
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|
* =============================================================================
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*/
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/**
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* @license
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* Copyright 2018 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2019 Google LLC
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*
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2019 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
|
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
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|
* =============================================================================
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*/
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/**
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* @license
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* Copyright 2019 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
|
|
*
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* http://www.apache.org/licenses/LICENSE-2.0
|
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
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|
* limitations under the License.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2020 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
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|
* limitations under the License.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2020 Google LLC
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*
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* Use of this source code is governed by an MIT-style
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* license that can be found in the LICENSE file or at
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* https://opensource.org/licenses/MIT.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2020 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
|
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
|
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
|
|
* limitations under the License.
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|
* =============================================================================
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*/
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/**
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* @license
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* Copyright 2020 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the License);
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
|
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an AS IS BASIS,
|
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
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* =============================================================================
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*/
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/**
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* @license
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* Copyright 2021 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
|
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*
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* http://www.apache.org/licenses/LICENSE-2.0
|
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*
|
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
|
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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* See the License for the specific language governing permissions and
|
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* limitations under the License.
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* =============================================================================
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*/
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/** @license See the LICENSE file. */
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//# sourceMappingURL=tfjs.esm.js.map
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