mirror of https://github.com/vladmandic/human
5188 lines
1.3 MiB
5188 lines
1.3 MiB
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/*
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Human library
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homepage: <https://github.com/vladmandic/human>
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author: <https://github.com/vladmandic>'
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*/
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Reusing existing backend factory.`),!1):(this.registryFactory[e]={factory:t,priority:n},!0)}async setBackend(e){if(this.registryFactory[e]==null)throw new Error(`Backend name '${e}' not found in registry`);if(this.backendName=e,this.registry[e]==null){this.backendInstance=null;let{success:t,asyncInit:n}=this.initializeBackend(e);if(!(n?await t:t))return!1}return this.backendInstance=this.registry[e],this.setupRegisteredKernels(),this.profiler=new sN(this.backendInstance),!0}setupRegisteredKernels(){Al(this.backendName).forEach(e=>{e.setupFunc!=null&&e.setupFunc(this.backendInstance)})}disposeRegisteredKernels(e){Al(e).forEach(t=>{t.disposeFunc!=null&&t.disposeFunc(this.registry[e])})}initializeBackend(e){let t=this.registryFactory[e];if(t==null)throw new Error(`Cannot initialize backend ${e}, no registration found.`);try{let n=t.factory();if(n&&!(n instanceof Cu)&&typeof n.then=="function"){let a=++this.pendingBackendInitId,r=n.then(s=>a<this.pendingBackendInitId?!1:(this.registry[e]=s,this.pendingBackendInit=null,!0)).catch(s=>(a<this.pendingBackendInitId||(this.pendingBackendInit=null,console.warn(`Initialization of backend ${e} failed`),console.warn(s.stack||s.message)),!1));return this.pendingBackendInit=r,{success:r,asyncInit:!0}}else return this.registry[e]=n,{success:!0,asyncInit:!1}}catch(n){return console.warn(`Initialization of backend ${e} failed`),console.warn(n.stack||n.message),{success:!1,asyncInit:!1}}}removeBackend(e){if(!(e in this.registryFactory))throw new Error(`${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 this.registryFactory[e],this.backendName===e&&(this.pendingBackendInit=null,this.backendName=null,this.backendInstance=null)}getSortedBackends(){if(Object.keys(this.registryFactory).length===0)throw new Error("No backend found in registry.");return Object.keys(this.registryFactory).sort((e,t)=>this.registryFactory[t].priority-this.registryFactory[e].priority)}initializeBackendsAndReturnBest(){let e=this.getSortedBackends();for(let t=0;t<e.length;t++){let n=e[t],{success:a,asyncInit:r}=this.initializeBackend(n);if(r||a)return{name:n,asyncInit:r}}throw new Error("Could not initialize any backends, all backend initializations failed.")}moveData(e,t){let n=this.state.tensorInfo.get(t),a=n.backend,r=this.readSync(t),s=a.refCount(t);a.disposeData(t,!0),n.backend=e,e.move(t,r,n.shape,n.dtype,s),this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack[this.state.numDataMovesStack.length-1]++}tidy(e,t){let n=null;if(t==null){if(typeof e!="function")throw new Error("Please provide a function to tidy()");t=e}else{if(typeof e!="string"&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if(typeof t!="function")throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");n=e}let a;return this.scopedRun(()=>this.startScope(n),()=>this.endScope(a),()=>(a=t(),a instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),a))}scopedRun(e,t,n){e();try{let a=n();return t(),a}catch(a){throw t(),a}}nextTensorId(){return td.nextTensorId++}nextVariableId(){return td.nextVariableId++}clone(e){let t=P.runKernel(_s,{x:e}),n={x:e},a=s=>({x:()=>{let i="float32",o={x:s},u={dtype:i};return P.runKernel(ws,o,u)}}),r=[];return this.addTapeNode(this.state.activeScope.name,n,[t],a,r,{}),t}runKernel(e,t,n){if(Rc(e,this.backendName)==null)throw new Error(`Kernel '${e}' not registered for backend '${this.backendName}'`);return this.runKernelFunc({kernelName:e,inputs:t,attrs:n})}shouldCheckForMemLeaks(){return this.ENV.getBool("IS_TEST")}checkKernelForMemLeak(e,t,n){let a=this.backend.numDataIds(),r=0;n.forEach(o=>{r+=o.dtype==="complex64"?3:1});let s=this.state.numDataMovesStack[this.state.numDataMovesStack.length-1],i=a-t-r-s;if(i>0)throw new Error(`Backend '${this.backendName}' has an internal memory leak (${i} data ids) after running '${e}'`)}runKernelFunc(e){let t,n=[],a=this.isTapeOn(),r=this.state.numBytes,s=this.state.numTensors;this.shouldCheckForMemLeaks()&&this.state.numDataMovesStack.push(0);let i;this.backendName==null&&this.backend;let o,u=r1(e)?e.kernelName:this.state.activeScope!=null?this.state.activeScope.name:"";if(r1(e)){let{kernelName:h,inputs:m,attrs:f}=e;this.backendName==null&&this.backend;let A=Rc(h,this.backendName);D(A!=null,()=>`Cannot find registered kernel '${h}' for backend '${this.backendName}'`),i=()=>{let y=this.backend.numDataIds();o=A.kernelFunc({inputs:m,attrs:f,backend:this.backend});let g=Array.isArray(o)?o:[o];this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(h,y,g);let x=g.map(v=>{if(v.rank!=null)return v;let{dataId:b,shape:w,dtype:N}=v;return this.makeTensorFromDataId(b,w,N)});if(a){let v=this.getTensorsForGradient(h,m,x);n=this.saveTensorsForBackwardMode(v)}return x}}else{let{forwardFunc:h}=e,m=f=>{!a||(n=f.map(A=>this.keep(this.clone(A))))};i=()=>{let f=this.backend.numDataIds();o=this.tidy(()=>h(this.backend,m));let A=Array.isArray(o)?o:[o];return this.shouldCheckForMemLeaks()&&this.checkKernelForMemLeak(u,f,A),A}}let{inputs:l,attrs:d}=e,p=r1(e)?null:e.backwardsFunc,c;return this.scopedRun(()=>this.state.kernelDepth++,()=>this.state.kernelDepth--,()=>{!this.ENV.getBool("DEBUG")&&!this.state.profiling?t=i():(c=this.profiler.profileKernel(u,l,()=>i()),this.ENV.getBool("DEBUG")&&this.profiler.logKernelProfile(c),t=c.outputs)}),a&&this.addTapeNode(u,l,t,p,n,d),this.state.profiling&&this.state.activeProfile.kernels.push({name:u,bytesAdded:this.state.numBytes-r,totalBytesSnapshot:this.state.numBytes,tensorsAdded:this.state.numTensors-s,totalTensorsSnapshot:this.state.numTensors,inputShapes:Object.keys(l).map(h=>l[h]!=null?l[h].shape:null),outputShapes:t.map(h=>h.shape),kernelTimeMs:c.timeMs,extraInfo:c.extraInfo}),Array.isArray(o)?t:t[0]}saveTensorsForBackwardMode(e){return e.map(t=>this.keep(this.clone(t)))}getTensorsForGradient(e,t,n){let a=Xm(e);if(a!=null){let r=a.inputsToSave||[],s=a.outputsToSave||[],i;a.saveAllInputs?(D(Array.isArray(t),()=>"saveAllInputs is true, expected inputs to be an array."),i=Object.keys(t).map(u=>t[u])):i=r.map(u=>t[u]);let o=n.filter((u,l)=>s[l]);return i.concat(o)}return[]}makeTensor(e,t,n,a){if(e==null)throw new Error("Values passed to engine.makeTensor() are null");n=n||"float32",a=a||this.backend;let r=e;n==="string"&&Fr(e[0])&&(r=e.map(o=>Zu(o)));let s=a.write(r,t,n),i=new We(t,n,s,this.nextTensorId());if(this.trackTensor(i,a),n==="string"){let o=this.state.tensorInfo.get(s),u=fb(r);this.state.numBytes+=u-o.bytes,o.bytes=u}return i}makeTensorFromDataId(e,t,n,a){n=n||"float32";let r=new We(t,n,e,this.nextTensorId());return this.trackTensor(r,a),r}makeVariable(e,t=!0,n,a){n=n||this.nextVariableId().toString(),a!=null&&a!==e.dtype&&(e=e.cast(a));let r=new ed(e,t,n,this.nextTensorId());if(this.state.registeredVariables[r.name]!=null)throw new Error(`Variable with name ${r.name} was already registered`);return this.state.registeredVariables[r.name]=r,this.incRef(r,this.backend),r}trackTensor(e,t){this.state.numTensors++,e.dtype==="string"&&this.state.numStringTensors++;let n=0;e.dtype!=="complex64"&&e.dtype!=="string"&&(n=e.size*Vm(e.dtype)),this.state.numBytes+=n,this.state.tensorInfo.has(e.dataId)||(this.state.numDataBuffers++,this.state.tensorInfo.set(e.dataId,{backend:t||this.backend,dtype:e.dtype,shape:e.shape,bytes:n})),e instanceof ed||this.track(e)}incRef(e,t){this.trackTensor(e,t),this.backend.incRef(e.dataId)}removeDataId(e,t){this.state.tensorInfo.has(e)&&this.state.tensorInfo.get(e).backend===t&&(this.state.tensorInfo.delete(e),this.state.numDataBuffers--)}disposeTensor(e){if(!this.state.tensorInfo.has(e.dataId))return;let t=this.state.tensorInfo.get(e.dataId);if(this.state.numTensors--,e.dtype==="string"&&(this.state.numStringTensors--,this.state.numBytes-=t.bytes),e.dtype!=="complex64"&&e.dtype!=="string"){let n=e.size*Vm(e.dtype);this.state.numBytes-=n}t.backend.disposeData(e.dataId)&&this.removeDataId(e.dataId,t.backend)}disposeVariables(){for(let e in this.state.registeredVariables){let 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,n=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(a=>a.totalBytesSnapshot)),this.state.activeProfile.newBytes=this.state.numBytes-t,this.state.activeProfile.newTensors=this.state.numTensors-n;for(let a of this.state.activeProfile.kernels)a.kernelTimeMs=await a.kernelTimeMs,a.extraInfo=await a.extraInfo;return this.state.activeProfile}isTapeOn(){return this.state.gradientDepth>0&&this.state.kernelDepth===0}addTapeNode(e,t,n,a,r,s){let i={id:this.state.nextTapeNodeId++,kernelName:e,inputs:t,outputs:n,saved:r},o=Xm(e);o!=null&&(a=o.gradFunc),a!=null&&(i.gradient=u=>(u=u.map((l,d)=>{if(l==null){let p=n[d],c=Gp(p.size,p.dtype);return this.makeTensor(c,p.shape,p.dtype)}return l}),a(u.length>1?u:u[0],r,s))),this.state.activeTape.push(i)}keep(e){return e.kept=!0,e}startTape(){this.state.gradientDepth===0&&(this.state.activeTape=[]),this.state.gradientDepth++}endTape(){this.state.gradientDepth--}startScope(e){let t={track:[],name:"unnamed scope",id:this.state.nextScopeId++};e&&(t.name=e),this.state.scopeStack.push(t),this.state.activeScope=t}endScope(e){let t=a1(e),n=new Set(t.map(r=>r.id));for(let r=0;r<this.state.activeScope.track.length;r++){let s=this.state.activeScope.track[r];!s.kept&&!n.has(s.id)&&s.dispose()}let a=this.state.scopeStack.pop();this.state.activeScope=this.state.scopeStack.length===0?null:this.state.scopeStack[this.state.scopeStack.length-1],t.forEach(r=>{!r.kept&&r.scopeId===a.id&&this.track(r)})}gradients(e,t,n,a=!1){if(D(t.length>0,()=>"gradients() received an empty list of xs."),n!=null&&n.dtype!=="float32")throw new Error(`dy must have 'float32' dtype, but has '${n.dtype}'`);let r=this.scopedRun(()=>this.startTape(),()=>this.endTape(),()=>this.tidy("forward",e));D(r instanceof We,()=>"The result y returned by f() must be a tensor.");let s=lN(this.state.activeTape,t,r);if(!a&&s.length===0&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. 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e[0]*1e3+e[1]/1e6}encode(e,t){if(t!=="utf-8"&&t!=="utf8")throw new Error(`Node built-in encoder only supports utf-8, but got ${t}`);return this.textEncoder.encode(e)}decode(e,t){return e.length===0?"":new this.util.TextDecoder(t).decode(e)}};te().get("IS_NODE")&&te().setPlatform("node",new QN);function Be(e,t="float32",n){return t=t||"float32",Um(e),new Lt(e,t,n)}function eT(e,t){let n=M(e,"x","cast");if(!hb(t))throw new Error(`Failed to cast to unknown dtype ${t}`);if(t==="string"&&n.dtype!=="string"||t!=="string"&&n.dtype==="string")throw new Error("Only strings can be casted to strings");let a={x:n},r={dtype:t};return P.runKernel(ws,a,r)}var me=L({cast_:eT});function tT(e){let t={x:M(e,"x","clone","string_or_numeric")};return P.runKernel(_s,t)}var Ua=L({clone_:tT});function qb(e,t=!1){console.log(e.toString(t))}$b();var nT={buffer:Be,cast:me,clone:Ua,print:qb};fN(nT);var 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i=this.weightDataAnchor==null?document.createElement("a"):this.weightDataAnchor;i.download=this.weightDataFileName,i.href=t,await Xb(()=>i.dispatchEvent(new MouseEvent("click")))}return{modelArtifactsInfo:rd(e)}}}};bl.URL_SCHEME="downloads://";var iT=class{constructor(e){if(e==null||e.length<1)throw new Error(`When calling browserFiles, at least 1 file is required, but received ${e}`);this.files=e}async load(){let e=this.files[0],t=this.files.slice(1);return new Promise((n,a)=>{let r=new FileReader;r.onload=s=>{let i=JSON.parse(s.target.result),o=i.modelTopology;if(o==null){a(new Error(`modelTopology field is missing from file ${e.name}`));return}t.length===0&&n({modelTopology:o});let u=i.weightsManifest;if(u==null){a(new Error(`weightManifest field is missing from file ${e.name}`));return}let l;try{l=this.checkManifestAndWeightFiles(u,t)}catch(h){a(h);return}let 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BrowserFiles supports loading Keras-style tf.Model artifacts only.`),r.readAsText(e)})}checkManifestAndWeightFiles(e,t){let n=[],a=t.map(s=>Wb(s.name)),r={};for(let s of e)s.paths.forEach(i=>{let o=Wb(i);if(n.indexOf(o)!==-1)throw new Error(`Duplicate file basename found in weights manifest: '${o}'`);if(n.push(o),a.indexOf(o)===-1)throw new Error(`Weight file with basename '${o}' is not provided.`);r[i]=t[a.indexOf(o)]});if(n.length!==t.length)throw new Error(`Mismatch in the number of files in weights manifest (${n.length}) and the number of weight files provided (${t.length}).`);return r}},oT=e=>te().getBool("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(bl.URL_SCHEME)?lT(e.slice(bl.URL_SCHEME.length)):null;Et.registerSaveRouter(oT);function lT(e="model"){return new bl(e)}function uT(e){return new iT(e)}function Kb(e,t,n,a){i(e),n=n==null?0:n,a=a==null?1:a,o(n,a);let r=0,s=u=>(u.then(l=>{let d=n+ ++r/e.length*(a-n);return t(d),l}),u);function i(u){D(u!=null&&Array.isArray(u)&&u.length>0,()=>"promises must be a none empty array")}function o(u,l){D(u>=0&&u<=1,()=>`Progress fraction must be in range [0, 1], but got startFraction ${u}`),D(l>=0&&l<=1,()=>`Progress fraction must be in range [0, 1], but got endFraction ${l}`),D(l>=u,()=>`startFraction must be no more than endFraction, but got startFraction ${u} and endFraction ${l}`)}return Promise.all(e.map(s))}async function Zb(e,t){t==null&&(t={});let n=t.fetchFunc==null?te().platform.fetch:t.fetchFunc,a=e.map(l=>n(l,t.requestInit,{isBinary:!0})),r=0,s=.5,i=(t.onProgress==null?await Promise.all(a):await Kb(a,t.onProgress,r,s)).map(l=>l.arrayBuffer()),o=.5,u=1;return t.onProgress==null?await Promise.all(i):await Kb(i,t.onProgress,o,u)}async function dT(e,t="",n,a){return Yb(r=>Zb(r,{requestInit:a}))(e,t,n)}function Yb(e){return async(t,n="",a)=>{let r=t.map(()=>!1),s={},i=a!=null?a.map(()=>!1):[],o=[];if(t.forEach((h,m)=>{let f=0;h.weights.forEach(A=>{let y="quantization"in A?A.quantization.dtype:A.dtype,g=s1[y]*Mt(A.shape),x=()=>{r[m]=!0,s[m]==null&&(s[m]=[]),s[m].push({manifestEntry:A,groupOffset:f,sizeBytes:g})};a!=null?a.forEach((v,b)=>{v===A.name&&(x(),i[b]=!0)}):x(),o.push(A.name),f+=g})}),!i.every(h=>h)){let h=a.filter((m,f)=>!i[f]);throw new Error(`Could not find weights in manifest with names: ${h.join(", ")}.
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m3={};Fe(m3,{TEST_EPSILON_FLOAT16:()=>A3,encodeStrings:()=>y3,expectArrayBuffersEqual:()=>WT,expectArraysClose:()=>_T,expectArraysEqual:()=>zT,expectNumbersClose:()=>PT,expectPromiseToFail:()=>OT,expectValuesInRange:()=>LT,testEpsilon:()=>b1});var DT=.001,A3=.1;function _T(e,t,n){return n==null&&(n=b1()),v1(e,t,(a,r)=>w1(a,r,n))}function b1(){return P.backend.floatPrecision()===32?DT:A3}function v1(e,t,n){let a=!0;if((sn(e)||sn(t))&&(a=!1),sn(e)&&sn(t)&&(a=!0),a){let i=e.constructor.name,o=t.constructor.name;if(i!==o)throw new Error(`Arrays are of different type. 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|
Actual: ${r}.
|
|
Expected: ${s}.`);for(let i=0;i<s.length;++i){let o=r[i],u=s[i];if(!n(o,u))throw new Error(`Arrays differ: actual[${i}] = ${o}, expected[${i}] = ${u}.
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Actual: ${r}.
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Expected: ${s}.`)}}function OT(e,t){e().then(()=>t.fail(),()=>t())}function zT(e,t){let n=typeof t=="string"||typeof t=="number"||typeof t=="boolean"?[t]:t;return Fr(e)||Fr(e[0])||Fr(t)||Fr(t[0])?v1(e,n,(a,r)=>a==r):v1(e,t,(a,r)=>w1(a,r,0))}function PT(e,t,n){if(n==null&&(n=b1()),!w1(e,t,n))throw new Error(`Numbers differ: actual === ${e}, expected === ${t}`)}function w1(e,t,n){return!isFinite(e)&&!isFinite(t)?!0:!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function LT(e,t,n){for(let a=0;a<e.length;a++)if(e[a]<t||e[a]>n)throw new Error(`Value out of range:${e[a]} low: ${t}, high: ${n}`)}function WT(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}function y3(e){for(let t=0;t<e.length;t++){let n=e[t];Array.isArray(n)?y3(n):e[t]=Zu(n)}return e}var BT="3.7.0";function VT(){te().set("PROD",!0)}function jT(){te().set("DEBUG",!0)}function UT(){te().set("DEPRECATION_WARNINGS_ENABLED",!1),console.warn("TensorFlow.js deprecation warnings have been disabled.")}function 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i=M(e,"x","batchNorm"),o=M(t,"mean","batchNorm"),u=M(n,"variance","batchNorm"),l;r!=null&&(l=M(r,"scale","batchNorm"));let d;return a!=null&&(d=M(a,"offset","batchNorm")),D(i.rank===4,()=>`Error in batchNorm4D: x must be rank 4 but got rank ${i.rank}.`),D(o.rank===4||o.rank===1,()=>`Error in batchNorm4D: mean must be rank 4 or rank 1 but got rank ${o.rank}.`),D(u.rank===4||u.rank===1,()=>`Error in batchNorm4D: variance must be rank 4 or rank 1 but got rank ${u.rank}.`),l!=null&&D(l.rank===4||l.rank===1,()=>`Error in batchNorm4D: scale must be rank 4 or rank 1 but got rank ${l.rank}.`),d!=null&&D(d.rank===4||d.rank===1,()=>`Error in batchNorm4D: offset must be rank 4 or rank 1 but got rank ${d.rank}.`),Ni(i,o,u,d,l,s)}var I3=L({batchNorm4d_:zC});function PC(e,t,n){let a=M(e,"x","bincount"),r=M(t,"weights","bincount");D(a.dtype==="int32",()=>`Error in bincount: input dtype must be int32, but got ${a.dtype}`),D(n>=0,()=>`size must be non-negative, but got 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${r} and ${t} for depthToSpace with input shape
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${a.shape}`),D(s*t>=0,()=>`Negative dimension size caused by overflow when multiplying
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${s} and ${t} for depthToSpace with input shape
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${a.shape}`),D(i%(t*t)==0,()=>`Dimension size must be evenly divisible by ${t*t} but is ${i} for depthToSpace with input shape ${a.shape}`);let o={x:a},u={blockSize:t,dataFormat:n};return P.runKernel(To,o,u)}var W1=L({depthToSpace_:aE});function rE(e,t,n,a,r="NHWC",s=[1,1],i){let o=M(e,"x","depthwiseConv2d"),u=M(t,"filter","depthwiseConv2d"),l=o,d=!1;o.rank===3&&(d=!0,l=q(o,[1,o.shape[0],o.shape[1],o.shape[2]])),D(l.rank===4,()=>`Error in depthwiseConv2d: input must be rank 4, but got rank ${l.rank}.`),D(u.rank===4,()=>`Error in depthwiseConv2d: filter must be rank 4, but got rank ${u.rank}.`),D(l.shape[3]===u.shape[2],()=>`Error in depthwiseConv2d: number of input channels (${l.shape[3]}) must match the inChannels dimension in filter ${u.shape[2]}.`),i!=null&&D(Gt(a),()=>`Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode ${i} but got pad ${a}.`);let p={x:l,filter:u},c={strides:n,pad:a,dataFormat:r,dilations:s,dimRoundingMode:i},h=P.runKernel(Cs,p,c);return d?q(h,[h.shape[1],h.shape[2],h.shape[3]]):h}var Tl=L({depthwiseConv2d_:rE});function sE(e){let t={x:M(e,"x","diag")};return P.runKernel(ac,t)}var iE=L({diag_:sE});function oE(e,t,n,a,r=[1,1],s="NHWC"){let i=M(e,"x","dilation2d"),o=M(t,"filter","dilation2d");D(i.rank===3||i.rank===4,()=>`Error in dilation2d: input must be rank 3 or 4, but got rank ${i.rank}.`),D(o.rank===3,()=>`Error in dilation2d: filter must be rank 3, but got rank ${o.rank}.`),D(s==="NHWC",()=>`Error in dilation2d: Only NHWC is currently supported, but got dataFormat of ${s}`);let u=i,l=!1;i.rank===3&&(u=q(i,[1,i.shape[0],i.shape[1],i.shape[2]]),l=!0);let d={x:u,filter:o},p={strides:n,pad:a,dilations:r},c=P.runKernel(Ou,d,p);return l?q(c,[c.shape[1],c.shape[2],c.shape[3]]):c}var B1=L({dilation2d_:oE});function lE(e,t){let n=e.length,a=[];for(let r=0;r<n;r++){let s=n-1-r,i=e[s]||1;(t[t.length-1-r]||1)>1&&i===1&&a.unshift(s)}return a}function Bt(e,t){let n=[];for(let a=0;a<t.length;a++){let 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t={x:M(e,"x","elu")};return P.runKernel(Co,t)}var Cl=L({elu_:mE});function AE(e){let t=M(e,"x","erf");D(t.dtype==="int32"||t.dtype==="float32",()=>"Input dtype must be `int32` or `float32`."),t.dtype==="int32"&&(t=me(t,"float32"));let n={x:t};return P.runKernel(Eo,n)}var j1=L({erf_:AE});function yE(e){let t={x:M(e,"x","exp")};return P.runKernel(Rs,t)}var sa=L({exp_:yE});function gE(e,t=0){let n=M(e,"x","expandDims","string_or_numeric");D(t<=n.rank,()=>"Axis must be <= rank of the tensor");let a={input:n},r={dim:t};return P.runKernel(Mo,a,r)}var fn=L({expandDims_:gE});function xE(e){let t={x:M(e,"x","expm1")};return P.runKernel(Fo,t)}var U1=L({expm1_:xE});function bE(e,t){let n=M(e,"x","tile","string_or_numeric");D(n.rank===t.length,()=>`Error in transpose: rank of input ${n.rank} must match length of reps ${t}.`);let a={x:n},r={reps:t};return P.runKernel(Or,a,r)}var Hr=L({tile_:bE});function vE(e,t,n,a="float32"){t==null&&(t=e);let r=Be([e,t],a),s=e<=t?e:t;for(let 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n=M(e,"a","greaterEqual","string_or_numeric"),a=M(t,"b","greaterEqual","string_or_numeric");[n,a]=It(n,a),ht(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Ds,r)}var Gr=L({greaterEqual_:SE});function NE(e){let t={input:M(e,"input","imag")};return P.runKernel(dc,t)}var Kc=L({imag_:NE});function TE(e){let t={x:M(e,"x","isFinite")};return P.runKernel(zo,t)}var $3=L({isFinite_:TE});function CE(e){let t={x:M(e,"x","isInf")};return P.runKernel(Po,t)}var D3=L({isInf_:CE});function EE(e){let t={x:M(e,"x","isNaN")};return P.runKernel(Lo,t)}var G1=L({isNaN_:EE});function RE(e,t=.2){let n={x:M(e,"x","leakyRelu")},a={alpha:t};return P.runKernel(Os,n,a)}var dd=L({leakyRelu_:RE});function ME(e,t){let n=M(e,"a","less","string_or_numeric"),a=M(t,"b","less","string_or_numeric");[n,a]=It(n,a),ht(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Wo,r)}var Zc=L({less_:ME});function FE(e,t){let n=M(e,"a","lessEqual","string_or_numeric"),a=M(t,"b","lessEqual","string_or_numeric");[n,a]=It(n,a),ht(n.shape,a.shape);let r={a:n,b:a};return P.runKernel(Bo,r)}var qr=L({lessEqual_:FE});function _3(e,t,n){if(n<=0)throw new Error("The number of values should be positive.");let a={start:e,stop:t,num:n};return P.runKernel(pc,{},a)}function $E(e,t=5,n=1,a=1,r=.5){let s=M(e,"x","localResponseNormalization");D(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}.`),D(Gt(t),()=>`Error in localResponseNormalization: depthRadius must be an integer but got depthRadius ${t}.`);let i=s,o=!1;s.rank===3&&(o=!0,i=q(s,[1,s.shape[0],s.shape[1],s.shape[2]]));let u={x:i},l={depthRadius:t,bias:n,alpha:a,beta:r},d=P.runKernel(Wu,u,l);return o?q(d,[d.shape[1],d.shape[2],d.shape[3]]):d}var q1=L({localResponseNormalization_:$E});function DE(e){let t={x:M(e,"x","log")};return P.runKernel(zs,t)}var Wn=L({log_:DE});function _E(e){let t={x:M(e,"x","log1p")};return P.runKernel(Vo,t)}var Yc=L({log1p_:_E});function OE(e){return D($r(e),()=>"The f passed in grad(f) must be a function"),(t,n)=>{let a=M(t,"x","tf.grad","string_or_numeric"),r=n!=null?M(n,"dy","tf.grad"):null;return P.tidy(()=>{let{value:s,grads:i}=P.gradients(()=>e(a),[a],r);return r!=null&&pn(s.shape,r.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),Jc(i),i[0]})}}function zE(e){return D($r(e),()=>"The f passed in grads(f) must be a function"),(t,n)=>{D(Array.isArray(t),()=>"The args passed in grads(f)(args) must be an array of `Tensor`s or `TensorLike`s");let a=ad(t,"args","tf.grads","string_or_numeric"),r=n!=null?M(n,"dy","tf.grads"):null;return P.tidy(()=>{let{value:s,grads:i}=P.gradients(()=>e(...a),a,r);return r!=null&&pn(s.shape,r.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Jc(i),i})}}function PE(e){return D($r(e),()=>"The f passed in valueAndGrad(f) must be a function"),(t,n)=>{D(t instanceof We,()=>"The x passed in valueAndGrad(f)(x) must be a tensor"),D(n==null||n instanceof We,()=>"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");let{grads:a,value:r}=P.gradients(()=>e(t),[t],n);return Jc(a),{grad:a[0],value:r}}}function LE(e){return D($r(e),()=>"The f passed in valueAndGrads(f) must be a function"),(t,n)=>{D(Array.isArray(t)&&t.every(r=>r instanceof We),()=>"The args passed in valueAndGrads(f)(args) must be array of tensors"),D(n==null||n instanceof We,()=>"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");let a=P.gradients(()=>e(...t),t,n);return n!=null&&pn(a.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),Jc(a.grads),a}}function O3(e,t){D($r(e),()=>"The f passed in variableGrads(f) must be a function"),D(t==null||Array.isArray(t)&&t.every(l=>l instanceof ed),()=>"The varList passed in variableGrads(f, varList) must be an array of variables");let n=t!=null;if(!n){t=[];for(let l in P.registeredVariables)t.push(P.registeredVariables[l])}let a=n?t.filter(l=>!l.trainable):null,r=t.length;t=t.filter(l=>l.trainable),D(t.length>0,()=>`variableGrads() expects at least one of the input variables to be trainable, but none of the ${r} variables is trainable.`);let s=!0,{value:i,grads:o}=P.gradients(e,t,null,s);D(o.some(l=>l!=null),()=>"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),D(i.rank===0,()=>`The f passed in variableGrads(f) must return a scalar, but it returned a rank-${i.rank} tensor`);let u={};return t.forEach((l,d)=>{o[d]!=null&&(u[l.name]=o[d])}),a!=null&&a.forEach(l=>u[l.name]=null),{value:i,grads:u}}function Ga(e){return P.customGrad(e)}function Jc(e){if(e.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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s=M(e,"multiClassLabels","sigmoidCrossEntropy"),i=M(t,"logits","sigmoidCrossEntropy"),o=null;if(n!=null&&(o=M(n,"weights","sigmoidCrossEntropy")),pn(s.shape,i.shape,"Error in sigmoidCrossEntropy: "),a>0){let l=we(a),d=we(1),p=we(.5);s=ie(B(s,Ae(d,l)),B(p,l))}let u=i$(s,i);return yr(u,o,r)}var l$=L({sigmoidCrossEntropy_:o$});function u$(e,t,n=-1){if(n===-1&&(n=t.rank-1),n!==t.rank-1)throw Error(`Softmax cross entropy along a non-last dimension is not yet supported. 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${r.shape}`);if(s.rank!==1)throw new Error(`Values should be Tensor1D but received shape ${s.shape}`);if(i.rank!==1)throw new Error(`Dense shape should be Tensor1D but received shape ${i.shape}`);if(o.rank!==0)throw new Error(`Default value should be a scalar but received shape ${o.shape}`);let u={indices:r,values:s,denseShape:i,defaultValue:o},l=P.runKernel(bc,u);return{outputIndices:l[0],outputValues:l[1],emptyRowIndicator:l[2],reverseIndexMap:l[3]}}var h$=L({sparseFillEmptyRows_:c$});function f$(e,t,n){let a=M(e,"inputIndices","sparseReshape"),r=M(t,"inputShape","sparseReshape"),s=M(n,"newShape","sparseReshape");if(a.rank!==2)throw new Error(`Input indices should be Tensor2D but received shape
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${a.shape}`);if(r.rank!==1)throw new Error(`Input shape should be Tensor1D but received shape ${r.shape}`);if(s.rank!==1)throw new Error(`New shape should be Tensor1D but received shape ${s.shape}`);let i={inputIndices:a,inputShape:r,newShape:s},o=P.runKernel(vc,i);return{outputIndices:o[0],outputShape:o[1]}}var m$=L({sparseReshape_:f$});function A$(e,t,n){let a=M(e,"data","sparseSegmentMean"),r=M(t,"indices","sparseSegmentMean"),s=M(n,"segmentIds","sparseSegmentMean");if(a.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${r.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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${s.shape}`);let i={data:a,indices:r,segmentIds:s};return P.runKernel(wc,i)}var y$=L({sparseSegmentMean_:A$});function g$(e,t,n){let a=M(e,"data","sparseSegmentSum"),r=M(t,"indices","sparseSegmentSum"),s=M(n,"segmentIds","sparseSegmentSum");if(a.rank<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.rank!==1)throw new Error(`Indices should be Tensor1D but received shape
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${r.shape}`);if(s.rank!==1)throw new Error(`Segment ids should be Tensor1D but received shape
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f=ie(B(fe(h,ie(nn(m),this.epsilon)),-this.learningRate),i);i.assign(f)}),this.accBeta1.assign(B(this.accBeta1,this.beta1)),this.accBeta2.assign(B(this.accBeta2,this.beta2))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.accBeta2.dispose(),this.accumulatedFirstMoment!=null&&Ie(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedSecondMoment!=null&&Ie(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(Ar(this.beta1,this.iterations_+1)),this.accBeta2.assign(Ar(this.beta2,this.iterations_+1))});let t=e.length/2,n=!1;this.accumulatedFirstMoment=e.slice(0,t).map(a=>({originalName:a.name,variable:a.tensor.variable(n)})),this.accumulatedSecondMoment=e.slice(t,t*2).map(a=>({originalName:a.name,variable:a.tensor.variable(n)}))}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)}};kh.className="Adam";Vr(kh);var Ih=class extends gr{constructor(e,t,n,a=null,r=0){super();this.learningRate=e,this.beta1=t,this.beta2=n,this.epsilon=a,this.decay=r,this.accumulatedFirstMoment=[],this.accumulatedWeightedInfNorm=[],V(()=>{this.iteration=we(0).variable(),this.accBeta1=we(t).variable()}),a==null&&(this.epsilon=P.backend.epsilon())}applyGradients(e){let t=Array.isArray(e)?e.map(n=>n.name):Object.keys(e);V(()=>{let n=Ae(1,this.accBeta1),a=fe(-this.learningRate,ie(B(this.iteration,this.decay),1));t.forEach((r,s)=>{let i=P.registeredVariables[r],o=!1;this.accumulatedFirstMoment[s]==null&&(this.accumulatedFirstMoment[s]={originalName:`${r}/m`,variable:Ge(i).variable(o)}),this.accumulatedWeightedInfNorm[s]==null&&(this.accumulatedWeightedInfNorm[s]={originalName:`${r}/v`,variable:Ge(i).variable(o)});let u=Array.isArray(e)?e[s].tensor:e[r];if(u==null)return;let l=this.accumulatedFirstMoment[s].variable,d=this.accumulatedWeightedInfNorm[s].variable,p=ie(B(l,this.beta1),B(u,1-this.beta1)),c=B(d,this.beta2),h=Wt(u),m=qa(c,h);l.assign(p),d.assign(m);let f=ie(B(fe(a,n),fe(p,ie(m,this.epsilon))),i);i.assign(f)}),this.iteration.assign(ie(this.iteration,1)),this.accBeta1.assign(B(this.accBeta1,this.beta1))}),this.incrementIterations()}dispose(){this.accBeta1.dispose(),this.iteration.dispose(),this.accumulatedFirstMoment!=null&&Ie(this.accumulatedFirstMoment.map(e=>e.variable)),this.accumulatedWeightedInfNorm!=null&&Ie(this.accumulatedWeightedInfNorm.map(e=>e.variable))}async getWeights(){throw new Error("getWeights() is not implemented for Adamax yet.")}async setWeights(e){throw new Error("setWeights() is not implemented for Adamax yet.")}getConfig(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}}static fromConfig(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)}};Ih.className="Adamax";Vr(Ih);var wd=class extends gr{constructor(e){super();this.learningRate=e,this.setLearningRate(e)}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=Array.isArray(e)?e[n].tensor:e[t];if(a==null)return;let r=P.registeredVariables[t];V(()=>{let s=ie(B(this.c,a),r);r.assign(s)})}),this.incrementIterations()}setLearningRate(e){this.learningRate=e,this.c!=null&&this.c.dispose(),this.c=Xt(we(-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.saveIterations()].concat(this.accumulations.map(e=>({name:e.originalName,tensor:e.variable})))}async setWeights(e){e=await this.extractIterations(e);let t=!1;this.accumulations=e.map(n=>({originalName:n.name,variable:n.tensor.variable(t)}))}getConfig(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}}static fromConfig(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)}};Sh.className="Momentum";Vr(Sh);var Nh=class extends gr{constructor(e,t=.9,n=0,a=null,r=!1){super();if(this.learningRate=e,this.decay=t,this.momentum=n,this.epsilon=a,this.accumulatedMeanSquares=[],this.accumulatedMoments=[],this.accumulatedMeanGrads=[],this.centered=r,a==null&&(this.epsilon=P.backend.epsilon()),e==null)throw new Error("learningRate for RMSPropOptimizer must be defined.")}applyGradients(e){(Array.isArray(e)?e.map(t=>t.name):Object.keys(e)).forEach((t,n)=>{let a=P.registeredVariables[t],r=!1;this.accumulatedMeanSquares[n]==null&&(this.accumulatedMeanSquares[n]={originalName:`${t}/rms`,variable:V(()=>Ge(a).variable(r))}),this.accumulatedMoments[n]==null&&(this.accumulatedMoments[n]={originalName:`${t}/momentum`,variable:V(()=>Ge(a).variable(r))}),this.accumulatedMeanGrads[n]==null&&this.centered&&(this.accumulatedMeanGrads[n]={originalName:`${t}/mg`,variable:V(()=>Ge(a).variable(r))});let s=Array.isArray(e)?e[n].tensor:e[t];if(s==null)return;let i=this.accumulatedMeanSquares[n].variable,o=this.accumulatedMoments[n].variable;V(()=>{let u=ie(B(i,this.decay),B(ot(s),1-this.decay));if(this.centered){let l=this.accumulatedMeanGrads[n].variable,d=ie(B(l,this.decay),B(s,1-this.decay)),p=fe(B(s,this.learningRate),nn(Ae(u,ie(ot(d),this.epsilon)))),c=ie(B(o,this.momentum),p);i.assign(u),l.assign(d),o.assign(c);let h=Ae(a,c);a.assign(h)}else{let l=ie(B(i,this.decay),B(ot(s),1-this.decay)),d=ie(B(o,this.momentum),fe(B(s,this.learningRate),nn(ie(l,this.epsilon))));i.assign(l),o.assign(d);let p=Ae(a,d);a.assign(p)}})}),this.incrementIterations()}dispose(){this.accumulatedMeanSquares!=null&&Ie(this.accumulatedMeanSquares.map(e=>e.variable)),this.accumulatedMeanGrads!=null&&this.centered&&Ie(this.accumulatedMeanGrads.map(e=>e.variable)),this.accumulatedMoments!=null&&Ie(this.accumulatedMoments.map(e=>e.variable))}async getWeights(){let e=[...this.accumulatedMeanSquares,...this.accumulatedMoments];return this.centered&&e.push(...this.accumulatedMeanGrads),[await this.saveIterations()].concat(e.map(t=>({name:t.originalName,tensor:t.variable})))}async setWeights(e){e=await this.extractIterations(e);let 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${s}).`);if(n<a)throw new Error(`batchDims (${a}) must be less than or equal to axis (${n}).`);for(let p=0;p<a;++p)if(e.shape[p]!==t.shape[p])throw new Error(`x.shape[${p}]: ${e.shape[p]} should be equal to indices.shape[${p}]: ${t.shape[p]}.`);let i=e.shape[n],o=[],u=1,l=1,d=1;for(let p=0;p<a;++p)o.push(e.shape[p]),u*=e.shape[p];for(let p=a;p<n;p++)o.push(e.shape[p]),l*=e.shape[p];for(let p=a;p<r;p++)o.push(t.shape[p]);for(let p=n+1;p<s;p++)o.push(e.shape[p]),d*=e.shape[p];return{batchSize:u,sliceSize:d,outerSize:l,dimSize:i,outputShape:o}}function cD(e){try{return e.map(t=>$c(t))}catch(t){throw new Error(`Failed to decode encoded string bytes into utf-8, error: ${t}`)}}function hD(e){return e.map(t=>Zu(t))}var Ka={};Fe(Ka,{nonMaxSuppressionV3Impl:()=>r7,nonMaxSuppressionV4Impl:()=>s7,nonMaxSuppressionV5Impl:()=>i7,whereImpl:()=>X3});function ve(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the CPU backend.`)})}var fD=Ka.whereImpl,Ch=class extends Cu{constructor(){super();this.blockSize=48,this.firstUse=!0,this.data=new jp(this,hr())}nextDataId(){return Ch.nextDataId++}write(e,t,n){this.firstUse&&(this.firstUse=!1,te().get("IS_NODE")&&F.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 a={id:this.nextDataId()};return this.data.set(a,{values:e,dtype:n,refCount:1}),a}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return{dataId:a,shape:e,dtype:t}}refCount(e){return this.data.has(e)?this.data.get(e).refCount:0}incRef(e){let t=this.data.get(e);t.refCount++}decRef(e){if(this.data.has(e)){let t=this.data.get(e);t.refCount--}}move(e,t,n,a,r){this.data.set(e,{values:t,dtype:a,refCount:r})}numDataIds(){return this.data.numDataIds()}async read(e){return this.readSync(e)}readSync(e){let{dtype:t,complexTensorInfos:n}=this.data.get(e);if(t==="complex64"){let a=this.readSync(n.real.dataId),r=this.readSync(n.imag.dataId);return F.mergeRealAndImagArrays(a,r)}return this.data.get(e).values}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}makeOutput(e,t,n){let a=this.write(e,t,n);return hr().makeTensorFromDataId(a,t,n,this)}disposeData(e,t=!1){if(this.data.has(e)){if(this.data.get(e).refCount--,!t&&this.data.get(e).refCount>0)return!1;let{complexTensorInfos:n}=this.data.get(e);n!=null&&(this.disposeData(n.real.dataId,!0),this.disposeData(n.imag.dataId,!0)),this.data.delete(e)}return!0}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}async time(e){let t=k.now();return e(),{kernelMs:k.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){ve([e],"where");let t=this.readSync(e.dataId);return fD(e.shape,t)}dispose(){}floatPrecision(){return 32}epsilon(){return super.epsilon()}};Ch.nextDataId=0;var yA={};Fe(yA,{addImpl:()=>g7,bincountImpl:()=>xA,bincountReduceImpl:()=>x7,ceilImpl:()=>b7,concatImpl:()=>bA,equalImpl:()=>v7,expImpl:()=>k7,expm1Impl:()=>S7,floorImpl:()=>N7,gatherNdImpl:()=>T7,gatherV2Impl:()=>C7,greaterEqualImpl:()=>R7,greaterImpl:()=>E7,lessEqualImpl:()=>F7,lessImpl:()=>M7,linSpaceImpl:()=>$7,logImpl:()=>D7,maxImpl:()=>_7,maximumImpl:()=>O7,minimumImpl:()=>z7,multiplyImpl:()=>vA,negImpl:()=>P7,notEqualImpl:()=>L7,prodImpl:()=>W7,rangeImpl:()=>kA,rsqrtImpl:()=>B7,simpleAbsImpl:()=>y7,sliceImpl:()=>Mh,sparseFillEmptyRowsImpl:()=>V7,sparseReshapeImpl:()=>j7,sparseSegmentReductionImpl:()=>IA,squaredDifferenceImpl:()=>U7,stridedSliceImpl:()=>H7,stringNGramsImpl:()=>G7,stringSplitImpl:()=>q7,stringToHashBucketFastImpl:()=>X7,subImpl:()=>K7,tileImpl:()=>Z7,topKImpl:()=>Y7,transposeImpl:()=>wA,uniqueImpl:()=>J7});function y7(e){let t=new Float32Array(e.length);for(let n=0;n<e.length;++n)t[n]=Math.abs(e[n]);return t}var mD=e=>{let{x:t}=e.inputs,n=e.backend;ve(t,"abs");let a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId).values;return a=y7(r),n.makeOutput(a,t.shape,"float32")},AD={kernelName:fo,backendName:"cpu",kernelFunc:mD};function _t(e){return(t,n,a,r,s)=>{let i=F.assertAndGetBroadcastShape(t,n),o=i.length,u=k.computeStrides(i),l=k.sizeFromShape(i),d=k.getTypedArrayFromDType(s,l),p=t.length,c=n.length,h=k.computeStrides(t),m=k.computeStrides(n),f=F.getBroadcastDims(t,i),A=F.getBroadcastDims(n,i);if(f.length+A.length===0)for(let y=0;y<d.length;++y)d[y]=e(a[y%a.length],r[y%r.length]);else for(let y=0;y<d.length;++y){let g=k.indexToLoc(y,o,u),x=g.slice(-p);f.forEach(N=>x[N]=0);let v=k.locToIndex(x,p,h),b=g.slice(-c);A.forEach(N=>b[N]=0);let w=k.locToIndex(b,c,m);d[y]=e(a[v],r[w])}return[d,i]}}function Hn(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.data.get(a.dataId).values,i=n.data.get(r.dataId).values,o=n.makeTensorInfo(a.shape,"complex64"),u=n.data.get(o.dataId);return u.complexTensorInfos={real:n.makeTensorInfo(a.shape,"float32",s),imag:n.makeTensorInfo(r.shape,"float32",i)},o}var yD={kernelName:Zp,backendName:"cpu",kernelFunc:Hn};function Eh(e,t,n="float32"){if(n==="complex64"){let r=Eh(e,t,"float32"),s=Eh(e,t,"float32");return Hn({inputs:{real:r,imag:s},backend:e})}let a=k.makeZerosTypedArray(k.sizeFromShape(t),n);return e.makeTensorInfo(t,n,a)}function Za(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var gD={kernelName:_s,backendName:"cpu",kernelFunc:Za};function $i(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.real,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var xD={kernelName:yc,backendName:"cpu",kernelFunc:$i};function Kr(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Za({inputs:{x:r},backend:n});let i=Eh(n,r.shape,r.dtype),o=Kr({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Hn({inputs:{real:o,imag:i},backend:n});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=$i({inputs:{input:r},backend:n}),o=Kr({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Za({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32"){let i=n.data.get(r.dataId).values,o=Int32Array.from(i);return n.makeTensorInfo(r.shape,"int32",o)}if(s==="bool"){let i=n.data.get(r.dataId).values,o=k.toTypedArray([0],r.dtype),[u,l]=_t((d,p)=>d!==p?1:0)(r.shape,[],i,o,"bool");return n.makeTensorInfo(l,"bool",u)}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var bD={kernelName:ws,backendName:"cpu",kernelFunc:Kr};function Zt(e,t,n,a){return n==null?({inputs:r,backend:s})=>{let{a:i,b:o}=r,u=s;ve([i,o],e);let 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v7=_t((e,t)=>e===t?1:0),w7=Zt(Ro,v7,null,"bool"),SD={kernelName:Ro,backendName:"cpu",kernelFunc:w7},k7=zl(e=>Math.exp(e)),I7=Pl(Rs,k7),ND={kernelName:Rs,backendName:"cpu",kernelFunc:I7},S7=zl(e=>Math.expm1(e)),TD=Pl(Fo,S7),CD={kernelName:Fo,backendName:"cpu",kernelFunc:TD},N7=zl(e=>Math.floor(e)),ED=Pl(Ms,N7),RD={kernelName:Ms,backendName:"cpu",kernelFunc:ED};function T7(e,t,n,a,r,s,i,o,u){let l=Be([a,s],n);for(let d=0;d<a;d++){let p=[],c=0;for(let h=0;h<r;h++){let m=e[d*r+h];c+=m*i[h],p.push(m)}if(c<0||c>=u/s)throw new Error(`Invalid indices: ${p} does not index into ${o}`);for(let h=0;h<s;h++)l.values[d*s+h]=t.get(...t.indexToLoc(c*s+h))}return l}function C7(e,t,n){let a=Be(n,e.dtype);for(let r=0;r<a.size;++r){let s=a.indexToLoc(r).slice(),i=s[0],o=s[2],u=t.locToIndex([i,o]);s[2]=t.values[u];let l=e.locToIndex(s);a.values[r]=e.values[l]}return a}var E7=_t((e,t)=>e>t?1:0),MD=Zt(Oo,E7,null,"bool"),FD={kernelName:Oo,backendName:"cpu",kernelFunc:MD},R7=_t((e,t)=>e>=t?1:0),$D=Zt(Ds,R7,null,"bool"),DD={kernelName:Ds,backendName:"cpu",kernelFunc:$D},M7=_t((e,t)=>e<t?1:0),_D=Zt(Wo,M7,null,"bool"),OD={kernelName:Wo,backendName:"cpu",kernelFunc:_D},F7=_t((e,t)=>e<=t?1:0),zD=Zt(Bo,F7,null,"bool"),PD={kernelName:Bo,backendName:"cpu",kernelFunc:zD};function $7(e,t,n){let a=(t-e)/(n-1),r=k.makeZerosTypedArray(n,"float32");r[0]=e;for(let s=1;s<r.length;s++)r[s]=r[s-1]+a;return r}var D7=zl(e=>Math.log(e)),LD=Pl(zs,D7),WD={kernelName:zs,backendName:"cpu",kernelFunc:LD};function _7(e,t,n,a){let r=k.getTypedArrayFromDType(a,k.sizeFromShape(n));for(let s=0;s<r.length;++s){let i=s*t,o=e[i];for(let u=0;u<t;++u){let l=e[i+u];(Number.isNaN(l)||l>o)&&(o=l)}r[s]=o}return r}var O7=_t((e,t)=>Math.max(e,t)),BD=Zt(Ls,O7),VD={kernelName:Ls,backendName:"cpu",kernelFunc:BD},z7=_t((e,t)=>Math.min(e,t)),jD=Zt(js,z7),UD={kernelName:js,backendName:"cpu",kernelFunc:jD},vA=_t((e,t)=>e*t),HD=gA((e,t,n,a)=>({real:e*n-t*a,imag:e*a+t*n})),Rh=Zt(Hs,vA,HD),GD={kernelName:Hs,backendName:"cpu",kernelFunc:Rh};function P7(e,t,n){let a=k.createScalarValue(-1,n);return vA([],t,a,e,n)}function qD(e){let{inputs:t,backend:n}=e,{x:a}=t;ve(a,"neg");let r=n.data.get(a.dataId).values,[s,i]=P7(r,a.shape,a.dtype);return n.makeTensorInfo(i,a.dtype,s)}var XD={kernelName:Ho,backendName:"cpu",kernelFunc:qD},L7=_t((e,t)=>e!==t?1:0),KD=Zt(Go,L7,null,"bool"),ZD={kernelName:Go,backendName:"cpu",kernelFunc:KD};function wA(e,t,n,a,r){let s=t.length,i=k.sizeFromShape(t),o=k.computeStrides(t),u=k.computeStrides(r),l=k.getTypedArrayFromDType(n,k.sizeFromShape(r));for(let d=0;d<i;++d){let p=k.indexToLoc(d,s,o),c=new Array(p.length);for(let m=0;m<c.length;m++)c[m]=p[a[m]];let 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h=n.data.get(p.dataId).values,{outVals:m,outShape:f,outDtype:A}=W7(p.shape,p.dtype,h,d),y=f;return i&&(y=F.expandShapeToKeepDim(f,u)),c.forEach(g=>n.disposeIntermediateTensorInfo(g)),n.makeTensorInfo(y,A,m)}var QD={kernelName:Jo,backendName:"cpu",kernelFunc:JD};function kA(e,t,n,a){let r=e===t,s=e<t&&n<0,i=t<e&&n>1;if(r||s||i)return k.makeZerosTypedArray(0,a);let o=Math.abs(Math.ceil((t-e)/n)),u=k.makeZerosTypedArray(o,a);t<e&&n===1&&(n=-1),u[0]=e;for(let l=1;l<u.length;l++)u[l]=u[l-1]+n;return u}var B7=zl(e=>1/Math.sqrt(e)),e_=Pl(ti,B7),t_={kernelName:ti,backendName:"cpu",kernelFunc:e_};function Mh(e,t,n,a,r){let s=hn.isSliceContinous(a,t,n),i=k.sizeFromShape(n),o=k.computeStrides(a);if(s){let p=hn.computeFlatOffset(t,o);return r==="string"?e.slice(p,p+i):e.subarray(p,p+i)}let u=r==="string"?F.fromUint8ToStringArray(e):e,l=Be(a,r,u),d=Be(n,r);for(let p=0;p<d.size;++p){let c=d.indexToLoc(p),h=c.map((m,f)=>m+t[f]);d.set(l.get(...h),...c)}return r==="string"?F.fromStringArrayToUint8(d.values):d.values}function Di(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a;ve(r,"slice");let[o,u]=hn.parseSliceParams(r,s,i);hn.assertParamsValid(r,o,u);let l=n.data.get(r.dataId).values,d=Mh(l,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,d)}var n_={kernelName:rl,backendName:"cpu",kernelFunc:Di};function V7(e,t,n,a,r,s,i){let o=t[0],u=s[0],l=new Array(u),d=new Array(o),p=t[1];if(u===0){if(o!==0)throw new Error(`Received SparseTensor with denseShape[0] = 0 but
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indices.shape[0] = ${o}`);let A=k.getArrayFromDType(n,0),y=k.getArrayFromDType(r,0);return[A,[0,p],y,l,d]}let c=!0,h=0,m=new Array(u).fill(0);for(let A=0;A<o;++A){let y=e[A*p];if(y<0)throw new Error(`indices(${A}, 0) is invalid: ${y} < 0`);if(y>=u)throw new Error(`indices(${A}, 0) is invalid: ${y} >= ${u}`);++m[y],c=c&&y>=h,h=y}let f=!0;for(let A=0;A<u;++A){let y=m[A]===0;l[A]=y,f=f&&!y,m[A]=Math.max(m[A],1),A>0&&(m[A]+=m[A-1])}if(f&&c){let A=e,y=a;for(let g=0;g<o;++g)d[g]=g;return[A,[o,p],y,l,d]}else{let A=m[u-1],y=k.getArrayFromDType(n,A*p),g=k.getArrayFromDType(r,A),x=new Array(u).fill(0);for(let v=0;v<o;++v){let b=e[v*p],w=x[b],N=(b===0?0:m[b-1])+w;x[b]++;for(let C=0;C<p;++C)y[N*p+C]=e[v*p+C];g[N]=a[v],d[v]=N}for(let v=0;v<u;++v)if(x[v]===0){let b=v===0?0:m[v-1];y[b*p+0]=v;for(let w=1;w<p;++w)y[b*p+w]=0;g[b]=i}return[y,[A,p],g,l,d]}}function j7(e,t,n,a,r){let s=k.sizeFromShape(a),i=t[0],o=r.length,u=[],l=1,d=-1;for(let A=0;A<o;++A){let y=r[A];if(y===-1){if(d!==-1)throw new Error(`only one output dimension may be -1, not both ${d} and ${A}`);d=A,u.push(1)}else{if(y<0)throw new Error(`size ${A} must be non-negative, not ${y}`);l*=y,u.push(y)}}if(d!==-1){if(l<=0)throw new Error("reshape cannot infer the missing input size for an empty tensor unless all specified input sizes are non-zero");let A=Math.trunc(s/l);if(l*A!==s)throw new Error(`Input to reshape is a SparseTensor with ${s}
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dense values, but the requested shape requires a multiple of ${l}. inputShape=${a} outputShape= ${u}`);u[d]=A}let p=k.sizeFromShape(u);if(p!==s)throw new Error(`Input to reshape is a tensor with ${s} dense values, but the requested shape has ${p}. inputShape=${a} outputShape=${u}`);let c=a.length,h=[];if(c>0){h[c-1]=1;for(let A=c-2;A>=0;--A)h[A]=h[A+1]*a[A+1]}let m=[];if(o>0){m[o-1]=1;for(let A=o-2;A>=0;--A)m[A]=m[A+1]*u[A+1]}let f=k.getArrayFromDType(n,i*o);for(let A=0;A<i;++A){let y=0;for(let g=0;g<c;++g)y+=e[A*c+g]*h[g];for(let g=0;g<o;++g)f[A*o+g]=Math.trunc(y/m[g]),y%=m[g]}return[f,[i,o],u]}function IA(e,t,n,a,r,s=!1,i=0){let o=a.length;if(o!==r.length)throw new Error("segmentIds and indices should have same size.");let u=[t[0],e.length/t[0]],l=u[1],d=o>0?r[o-1]+1:0;if(d<0)throw new Error("segment ids must be >= 0");let p=t.slice();p[0]=d;let c=p.reduce((g,x)=>g*x,1),h=k.getArrayFromDType(n,c);if(o===0)return d>0&&h.fill(i),[h,p];if(d<=0)throw new Error("segment ids must be >= 0");let m=0,f=1,A=0,y=r[m];for(;;){let g=0;if(f<o){if(g=r[f],y===g){++f;continue}if(y>=g)throw new Error("segment ids are not increasing")}if(y<0||y>=d)throw new Error(`Segment id ${y} out of range [0, ${d}), possibly because segmentIds input is not sorted.`);y>A&&h.fill(i,A*l,y*l);for(let x=m;x<f;++x){let v=a[x];if(v<0||v>=u[0])throw new Error(`Bad: indices[${x}] == ${a[x]} out of range [0, ${u[0]})`);for(let b=0;b<l;b++)h[y*l+b]+=e[v*l+b]}if(s)for(let x=0;x<l;x++)h[y*l+x]/=f-m;if(m=f,++f,A=y+1,y=g,f>o)break}return A<d&&h.fill(i,A*l,d*l),[h,p]}var U7=_t((e,t)=>{let n=e-t;return n*n}),a_=Zt(oi,U7),r_={kernelName:oi,backendName:"cpu",kernelFunc:a_};function H7(e,t,n,a){let r=Be(e,t.dtype);for(let s=0;s<r.size;s++){let i=r.indexToLoc(s),o=new Array(i.length);for(let u=0;u<o.length;u++)o[u]=i[u]*n[u]+a[u];r.set(t.get(...o),...i)}return r}var s_=class{constructor(e,t,n,a,r,s){this.separator=k.encodeString(e),this.nGramWidths=t,this.leftPad=k.encodeString(n),this.rightPad=k.encodeString(a),this.padWidth=r,this.preserveShort=s}getPadWidth(e){return Math.min(this.padWidth<0?e-1:this.padWidth,e-1)}getNumNGrams(e,t){let n=this.getPadWidth(t);return Math.max(0,e+2*n-t+1)}createNGrams(e,t,n,a,r,s){for(let i=0;i<r;++i){let o=this.getPadWidth(s),u=Math.max(0,o-i),l=Math.max(0,o-(r-(i+1))),d=s-(u+l),p=t+(u>0?0:i-o),c=0;c+=u*this.leftPad.length;for(let A=0;A<d;++A)c+=e[p+A].length;c+=l*this.rightPad.length,c+=(u+l+d-1)*this.separator.length,n[a+i]=new Uint8Array(c);let h=n[a+i],m=0,f=A=>A.forEach(y=>h[m++]=y);for(let A=0;A<u;++A)f(this.leftPad),f(this.separator);for(let A=0;A<d-1;++A)f(e[p+A]),f(this.separator);if(d>0){f(e[p+d-1]);for(let A=0;A<l;++A)f(this.separator),f(this.rightPad)}else{for(let A=0;A<l-1;++A)f(this.rightPad),f(this.separator);f(this.rightPad)}}}compute(e,t){let n=e.length,a=t.length;if(a>0){let o=t[0];if(o!==0)throw new Error(`First split value must be 0, got ${o}`);for(let u=1;u<a;++u){let l=t[u]>=o;if(l=l&&t[u]<=n,!l)throw new Error(`Invalid split value ${t[u]}, must be in [${o}, ${n}]`);o=t[u]}if(o!==n)throw new Error(`Last split value must be data size. 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The new shape and old shape must have the same number of elements.`),n.incRef(r.dataId);let l=n.data.get(r.dataId);if(l.complexTensorInfos!=null){let d=l.complexTensorInfos.real,p=l.complexTensorInfos.imag;d.shape=o,p.shape=o}return{dataId:r.dataId,shape:o,dtype:r.dtype}}var A_={kernelName:el,backendName:"cpu",kernelFunc:ft};function iv(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;ve([r,s],"matMul");let u=r.shape.length,l=s.shape.length,d=i?r.shape[u-2]:r.shape[u-1],p=o?s.shape[l-1]:s.shape[l-2],c=i?r.shape[u-1]:r.shape[u-2],h=o?s.shape[l-2]:s.shape[l-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),A=k.sizeFromShape(m),y=k.sizeFromShape(f),g=A===y||A===1||y===1;k.assert(u>=2&&l>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(A>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([c,h]);k.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[A,d,c]:[A,c,d],b=o?[y,h,p]:[y,p,h],w=ft({inputs:{x:r},backend:n,attrs:{shape:v}}),N=ft({inputs:{x:s},backend:n,attrs:{shape:b}}),C=i?w.shape[1]:w.shape[2],E=i?w.shape[2]:w.shape[1],z=o?N.shape[1]:N.shape[2],$=Math.max(A,y),S=n.data.get(w.dataId).values,O=n.data.get(N.dataId).values,_=k.computeStrides(w.shape),W=k.computeStrides(N.shape),[G,H,J]=i?[_[0],1,_[1]]:[_[0],_[1],1],[K,ne,Q]=o?[1,W[1],W[0]]:[W[1],1,W[0]],se=E*z,Z=Be([$,E,z],w.dtype),le=Z.values,oe=n.blockSize;for(let ge=0;ge<$;ge++)for(let he=0;he<E;he+=oe)for(let Ne=0;Ne<z;Ne+=oe)for(let Te=0;Te<C;Te+=oe){let De=Math.min(he+oe,E),ze=Math.min(Ne+oe,z),_e=Math.min(Te+oe,C);for(let tt=he;tt<De;tt++)for(let nt=Ne;nt<ze;nt++){let it=0;for(let Ze=Te;Ze<_e;Ze++){let ct=Math.min(ge,A-1)*G,Ue=Math.min(ge,y-1)*Q,kn=S[ct+tt*H+Ze*J],wt=O[Ze*K+nt*ne+Ue];it+=kn*wt}le[ge*se+(tt*z+nt)]+=it}}return n.disposeIntermediateTensorInfo(w),n.disposeIntermediateTensorInfo(N),n.makeTensorInfo(x,Z.dtype,Z.values)}var y_={kernelName:vs,backendName:"cpu",kernelFunc:iv};function g_(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:d,leakyreluAlpha:p}=a,c,h,m,f=[];c=iv({inputs:{a:r,b:s},attrs:{transposeA:u,transposeB:l},backend:n}),i&&(h=kd({inputs:{a:c,b:i},backend:n}),f.push(c),c=h),d&&(m=NA(n,c,d,o,p),f.push(c),c=m);for(let A of f)n.disposeIntermediateTensorInfo(A);return c}var x_={kernelName:ci,backendName:"cpu",kernelFunc:g_},b_=rt(mo,e=>Math.acos(e)),v_={kernelName:mo,backendName:"cpu",kernelFunc:b_},w_=rt(Ao,e=>Math.acosh(e)),k_={kernelName:Ao,backendName:"cpu",kernelFunc:w_};function I_(e){let{inputs:t,backend:n}=e,a=t;ve(t,"addN");let r=a.map(o=>n.data.get(o.dataId).values),s=Be(a[0].shape,a[0].dtype),i=s.values;for(let o=0;o<a.length;o++){let u=r[o];for(let l=0;l<i.length;l++)i[l]+=u[l]}return n.makeTensorInfo(s.shape,s.dtype,s.values)}var S_={kernelName:gs,backendName:"cpu",kernelFunc:I_};function N_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ve(r,"all");let o=k.parseAxisParam(s,r.shape),u=o,l=F.getAxesPermutation(u,r.shape.length),d=r;l!=null&&(d=ia({inputs:{x:r},backend:n,attrs:{perm:l}}),u=F.getInnerMostAxes(u.length,r.shape.length)),F.assertAxesAreInnerMostDims("all",u,d.shape.length);let[p,c]=F.computeOutAndReduceShapes(d.shape,u),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(p),d.dtype),f=n.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let g=y*h,x=f[g];for(let v=0;v<h;++v){let b=f[g+v];x=x&&b}m[y]=x}l!=null&&n.disposeIntermediateTensorInfo(d);let A=n.makeTensorInfo(p,d.dtype,m);if(i){let y=F.expandShapeToKeepDim(p,o),g=ft({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var T_={kernelName:yo,backendName:"cpu",kernelFunc:N_};function C_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ve(r,"any");let o=k.parseAxisParam(s,r.shape),u=o,l=F.getAxesPermutation(u,r.shape.length),d=r;l!=null&&(d=ia({inputs:{x:r},backend:n,attrs:{perm:l}}),u=F.getInnerMostAxes(u.length,r.shape.length)),F.assertAxesAreInnerMostDims("any",u,d.shape.length);let[p,c]=F.computeOutAndReduceShapes(d.shape,u),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(p),d.dtype),f=n.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let g=y*h,x=f[g];for(let v=0;v<h;++v){let b=f[g+v];x=x||b}m[y]=x}l!=null&&n.disposeIntermediateTensorInfo(d);let A=n.makeTensorInfo(p,d.dtype,m);if(i){let y=F.expandShapeToKeepDim(p,o),g=ft({inputs:{x:A},backend:n,attrs:{shape:y}});return n.disposeIntermediateTensorInfo(A),g}return A}var E_={kernelName:go,backendName:"cpu",kernelFunc:C_};function R_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ve(r,"argMax");let i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=ia({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=F.getInnerMostAxes(i.length,u.shape.length)),i=[i[0]],F.assertAxesAreInnerMostDims("argMax",i,u.shape.length);let[d,p]=F.computeOutAndReduceShapes(u.shape,i),c=k.sizeFromShape(d),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(p),f=n.data.get(u.dataId).values;for(let A=0;A<h.length;++A){let y=A*m,g=f[y],x=0;for(let v=0;v<m;++v){let b=f[y+v];b>g&&(g=b,x=v)}h[A]=x}return l.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(d,"int32",h)}var M_={kernelName:xs,backendName:"cpu",kernelFunc:R_};function F_(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a;ve(r,"argMin");let i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=ia({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=F.getInnerMostAxes(i.length,u.shape.length)),i=[i[0]],F.assertAxesAreInnerMostDims("argMin",i,u.shape.length);let[d,p]=F.computeOutAndReduceShapes(u.shape,i),c=k.sizeFromShape(d),h=k.makeZerosTypedArray(c,"int32"),m=k.sizeFromShape(p),f=n.data.get(u.dataId).values;for(let A=0;A<h.length;++A){let y=A*m,g=f[y],x=0;for(let v=0;v<m;++v){let b=f[y+v];b<g&&(g=b,x=v)}h[A]=x}return l.forEach(A=>n.disposeIntermediateTensorInfo(A)),n.makeTensorInfo(d,"int32",h)}var 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J_(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;ve([r,s],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=a,d=F.computePool2DInfo(i.shape,o,u,1,l),p=d.strideHeight,c=d.strideWidth,h=d.filterHeight,m=d.filterWidth,f=d.dilationHeight,A=d.dilationWidth,y=d.effectiveFilterHeight,g=d.effectiveFilterWidth,x=g-1-d.padInfo.left,v=y-1-d.padInfo.top,b=Be(i.shape,"float32"),w=1/(h*m),N=n.data.get(r.dataId).values,C=Be(r.shape,"float32",N);for(let E=0;E<d.batchSize;++E)for(let z=0;z<d.inChannels;++z)for(let $=0;$<d.inHeight;++$)for(let S=0;S<d.inWidth;++S){let O=$-v,_=S-x,W=0;for(let G=0;G<y;G+=f){let H=(O+G)/p;if(!(H<0||H>=d.outHeight||Math.floor(H)!==H))for(let J=0;J<g;J+=A){let K=(_+J)/c;K<0||K>=d.outWidth||Math.floor(K)!==K||(W+=C.get(E,H,K,z))}}b.set(W*w,E,$,S,z)}return n.makeTensorInfo(b.shape,b.dtype,b.values)}var Q_={kernelName:qp,backendName:"cpu",kernelFunc:J_};function eO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,scale:s,offset:i,mean:o,variance:u}=t;k.assert(o.shape.length===u.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||o.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(s==null||o.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks."),ve([r,o,u,s,i],"batchNorm");let{varianceEpsilon:l}=a;l==null&&(l=.001);let d=n.data.get(r.dataId).values,p=n.data.get(o.dataId).values,c=n.data.get(u.dataId).values,h=s?n.data.get(s.dataId).values:new Float32Array([1]),m=i?n.data.get(i.dataId).values:new Float32Array([0]),f=new Float32Array(d.length),A=m.length,y=h.length,g=c.length,x=p.length,v=0,b=0,w=0,N=0;for(let C=0;C<d.length;++C)f[C]=m[v++]+(d[C]-p[b++])*h[w++]/Math.sqrt(c[N++]+l),v>=A&&(v=0),b>=x&&(b=0),w>=y&&(w=0),N>=g&&(N=0);return n.makeTensorInfo(r.shape,r.dtype,f)}var tO={kernelName:$s,backendName:"cpu",kernelFunc:eO};function nO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;ve([r],"batchToSpaceND");let o=s.reduce((y,g)=>y*g),u=F.getReshaped(r.shape,s,o),l=F.getPermuted(u.length,s.length),d=F.getReshapedPermuted(r.shape,s,o),p=F.getSliceBeginCoords(i,s.length),c=F.getSliceSize(d,i,s.length),h=ft({inputs:{x:r},backend:n,attrs:{shape:u}}),m=ia({inputs:{x:h},backend:n,attrs:{perm:l}}),f=ft({inputs:{x:m},backend:n,attrs:{shape:d}}),A=Di({inputs:{x:f},backend:n,attrs:{begin:p,size:c}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(f),A}var aO={kernelName:$u,backendName:"cpu",kernelFunc:nO};function rO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,l=xA(o,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,l)}var sO={kernelName:Kp,backendName:"cpu",kernelFunc:rO},iO=rt(_r,(e,t)=>{let n=t;return e>n.clipValueMax?n.clipValueMax:e<n.clipValueMin?n.clipValueMin:e}),oO={kernelName:_r,backendName:"cpu",kernelFunc:iO},lO=e=>{let{x:t}=e.inputs,n=e.backend,a=new Float32Array(k.sizeFromShape(t.shape)),r=n.data.get(t.dataId),s=r.complexTensorInfos.real,i=r.complexTensorInfos.imag,o=n.data.get(s.dataId).values,u=n.data.get(i.dataId).values;for(let l=0;l<o.length;l++){let d=o[l],p=u[l];a[l]=Math.hypot(d,p)}return n.makeOutput(a,t.shape,"float32")},uO={kernelName:Du,backendName:"cpu",kernelFunc:lO};function Ll(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.data.get(a.dataId).complexTensorInfos.imag,s=n.data.get(r.dataId).values;return n.makeTensorInfo(r.shape,r.dtype,s)}var dO={kernelName:dc,backendName:"cpu",kernelFunc:Ll};function Wl(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=F.computeOutShape(t.map(f=>f.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(f=>k.sizeFromShape(f.shape)>0);if(o.length===1)return Za({inputs:{x:o[0]},backend:n});let u=o.map(f=>f.shape);if(F.assertParamsConsistent(u,s),o[0].dtype==="complex64"){let f=o.map(v=>$i({inputs:{input:v},backend:n})),A=o.map(v=>Ll({inputs:{input:v},backend:n})),y=Wl({inputs:f,backend:n,attrs:{axis:s}}),g=Wl({inputs:A,backend:n,attrs:{axis:s}}),x=Hn({inputs:{real:y,imag:g},backend:n});return f.forEach(v=>n.disposeIntermediateTensorInfo(v)),A.forEach(v=>n.disposeIntermediateTensorInfo(v)),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(g),x}let l=o.map(f=>{let A=k.sizeFromShape(f.shape.slice(s));return ft({inputs:{x:f},backend:n,attrs:{shape:[-1,A]}})}),d=l.map(f=>({vals:n.data.get(f.dataId).values,shape:f.shape}));i=F.computeOutShape(l.map(f=>f.shape),1);let p=l[0].shape[0]===1,c=bA(d,i,t[0].dtype,p),h=F.computeOutShape(o.map(f=>f.shape),s),m=n.makeTensorInfo(h,t[0].dtype,c);return l.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var pO={kernelName:Io,backendName:"cpu",kernelFunc:Wl};function uv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:d}=a;ve([r,s],"conv2d");let p=F.convertConv2DDataFormat(u),c=F.computeConv2DInfo(r.shape,s.shape,i,l,o,d,!1,p),h=c.filterHeight,m=c.filterWidth,f=c.dilationHeight,A=c.dilationWidth,y=c.padInfo.left,g=c.padInfo.top,x=c.dataFormat==="channelsLast",v=new Lt(c.outShape,r.dtype),b=k.computeStrides(r.shape),w=k.computeStrides(s.shape),N=b[0],C=x?b[1]:b[2],E=x?b[2]:1,z=x?1:b[1],$=v.strides[0],S=x?v.strides[1]:v.strides[2],O=x?v.strides[2]:1,_=x?1:v.strides[1],W=n.data.get(r.dataId).values,G=n.data.get(s.dataId).values,H=v.values;for(let J=0;J<c.batchSize;++J){let K=J*N,ne=J*$;for(let Q=0;Q<c.outHeight;++Q){let se=ne+Q*S,Z=Q*c.strideHeight-g;for(let le=0;le<h;++le){let oe=Z+le*f;if(oe<0||oe>=c.inHeight)continue;let ge=le*w[0],he=K+oe*C;for(let 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z=Math.max(0,Math.ceil((v-E)/h)),$=Math.min(c.outHeight,(c.inHeight+v-E)/h);for(let S=0;S<A;++S){let O=Math.max(0,Math.ceil((x-S)/m)),_=Math.min(c.outWidth,(c.inWidth+x-S)/m);for(let W=0;W<c.inChannels;++W)for(let G=0;G<c.outChannels;++G){let H=0;for(let J=0;J<c.batchSize;++J)for(let K=z;K<$;++K){let ne=E+K*h-v;for(let Q=O;Q<_;++Q){let se=S+Q*m-x;y?H+=N.get(J,ne,se,W)*C.get(J,K,Q,G):H+=N.get(J,W,ne,se)*C.get(J,G,K,Q)}}g.set(H,E,S,W,G)}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var fO={kernelName:Yp,backendName:"cpu",kernelFunc:hO};function mO(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:d}=a;ve([r,s],"conv2dBackpropInput");let p=k.computeStrides(s.shape),c=k.computeStrides(r.shape),h=F.convertConv2DDataFormat(l),m=F.computeConv2DInfo(i,s.shape,o,1,u,d,!1,h),f=new Lt(m.inShape,"float32"),A=f.values,y=n.data.get(r.dataId).values,g=n.data.get(s.dataId).values,[x,v,b]=p,{batchSize:w,filterHeight:N,filterWidth:C,inChannels:E,inHeight:z,inWidth:$,outChannels:S,outHeight:O,outWidth:_,strideHeight:W,strideWidth:G}=m;h=m.dataFormat;let H=N-1-m.padInfo.top,J=C-1-m.padInfo.left,K=h==="channelsLast",ne=f.strides[0],Q=K?f.strides[1]:f.strides[2],se=K?f.strides[2]:1,Z=K?1:f.strides[1],le=c[0],oe=K?c[1]:c[2],ge=K?c[2]:1,he=K?1:c[1];for(let Ne=0;Ne<w;++Ne)for(let Te=0;Te<E;++Te)for(let De=0;De<z;++De){let ze=De-H,_e=Math.max(0,Math.ceil(ze/W)),tt=Math.min(O,(N+ze)/W);for(let nt=0;nt<$;++nt){let it=nt-J,Ze=Math.max(0,Math.ceil(it/G)),ct=Math.min(_,(C+it)/G),Ue=0;for(let wt=_e;wt<tt;++wt){let Qn=wt*W-ze;for(let Qt=Ze;Qt<ct;++Qt){let In=Qt*G-it,ea=le*Ne+oe*wt+ge*Qt,zn=x*(N-1-Qn)+v*(C-1-In)+b*Te;for(let un=0;un<S;++un){let en=y[ea+he*un],La=g[zn+un];Ue+=en*La}}}let kn=ne*Ne+Q*De+se*nt+Z*Te;A[kn]=Ue}}return n.makeTensorInfo(f.shape,f.dtype,f.values)}var AO={kernelName:Ss,backendName:"cpu",kernelFunc:mO};function yO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:u}=a;ve([r,s],"conv3d");let l=F.computeConv3DInfo(r.shape,s.shape,i,u,o),{filterDepth:d,filterHeight:p,filterWidth:c,dilationDepth:h,dilationHeight:m,dilationWidth:f,padInfo:A}=l,y=A.front,g=A.left,x=A.top,v=new Lt(l.outShape,r.dtype),b=n.data.get(r.dataId).values,w=n.data.get(s.dataId).values,N=v.values,C=k.computeStrides(r.shape),E=k.computeStrides(s.shape);for(let z=0;z<l.batchSize;++z){let $=z*C[0],S=z*v.strides[0];for(let O=0;O<l.outDepth;++O){let _=S+O*v.strides[1],W=O*l.strideDepth-y;for(let G=0;G<d;++G){let H=W+G*h;if(H<0||H>=l.inDepth)continue;let J=G*E[0],K=$+H*C[1];for(let ne=0;ne<l.outHeight;++ne){let Q=_+ne*v.strides[2],se=ne*l.strideHeight-x;for(let Z=0;Z<p;++Z){let le=se+Z*m;if(le<0||le>=l.inHeight)continue;let oe=J+Z*E[1],ge=K+le*C[2];for(let he=0;he<l.outWidth;++he){let Ne=Q+he*l.outChannels,Te=he*l.strideWidth-g;for(let De=0;De<c;++De){let ze=Te+De*f;if(ze<0||ze>=l.inWidth)continue;let _e=oe+De*E[2],tt=ge+ze*l.inChannels,nt=_e;for(let it=0;it<l.inChannels;++it){let Ze=b[tt+it];for(let ct=0;ct<l.outChannels;++ct)N[Ne+ct]+=Ze*w[nt+ct];nt+=l.outChannels}}}}}}}}return n.makeTensorInfo(v.shape,v.dtype,v.values)}var gO={kernelName:_u,backendName:"cpu",kernelFunc:yO};function xO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:u}=a;ve([r,s],"conv3dBackpropFilterV2");let l=k.computeStrides(r.shape),d=k.computeStrides(s.shape),p=F.computeConv3DInfo(r.shape,u,i,1,o),c=p.strideDepth,h=p.strideHeight,m=p.strideWidth,f=p.filterDepth,A=p.filterHeight,y=p.filterWidth,g=new Lt(p.filterShape,"float32"),x=g.values,[v,b,w,N]=g.strides,C=n.data.get(s.dataId).values,[E,z,$,S]=d,O=n.data.get(r.dataId).values,[_,W,G,H]=l,J=p.padInfo.front,K=p.padInfo.left,ne=p.padInfo.top;for(let Q=0;Q<f;++Q){let se=Math.max(0,Math.ceil((J-Q)/c)),Z=Math.min(p.outDepth,(p.inDepth+J-Q)/c),le=Q*v;for(let oe=0;oe<A;++oe){let ge=Math.max(0,Math.ceil((ne-oe)/h)),he=Math.min(p.outHeight,(p.inHeight+ne-oe)/h),Ne=oe*b+le;for(let Te=0;Te<y;++Te){let De=Math.max(0,Math.ceil((K-Te)/m)),ze=Math.min(p.outWidth,(p.inWidth+K-Te)/m),_e=Te*w+Ne;for(let tt=0;tt<p.inChannels;++tt){let nt=tt*N+_e;for(let it=0;it<p.outChannels;++it){let Ze=0;for(let ct=0;ct<p.batchSize;++ct){let Ue=ct*_,kn=ct*E;for(let wt=se;wt<Z;++wt){let Qn=(Q+wt*c-J)*W+Ue,Qt=wt*z+kn;for(let In=ge;In<he;++In){let ea=(oe+In*h-ne)*G+Qn,zn=In*$+Qt;for(let un=De;un<ze;++un){let en=(Te+un*m-K)*H+ea,La=un*S+zn;Ze+=O[en+tt]*C[La+it]}}}}x[nt+it]=Ze}}}}}return n.makeTensorInfo(g.shape,g.dtype,g.values)}var bO={kernelName:Jp,backendName:"cpu",kernelFunc:xO};function vO(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:u}=a;ve([r],"conv3dBackpropInputV2");let l=k.computeStrides(r.shape),d=k.computeStrides(s.shape),p=F.computeConv3DInfo(u,s.shape,o,1,i),c=new Lt(p.inShape,"float32"),h=c.values,[m,f,A,y]=c.strides,g=n.data.get(r.dataId).values,[x,v,b,w]=l,N=n.data.get(s.dataId).values,[C,E,z,$]=d,{batchSize:S,filterDepth:O,filterHeight:_,filterWidth:W,inChannels:G,inDepth:H,inHeight:J,inWidth:K,outChannels:ne,outDepth:Q,outHeight:se,outWidth:Z,strideDepth:le,strideHeight:oe,strideWidth:ge}=p,he=O-1-p.padInfo.front,Ne=_-1-p.padInfo.top,Te=W-1-p.padInfo.left;for(let De=0;De<S;++De)for(let ze=0;ze<G;++ze)for(let _e=0;_e<H;++_e){let tt=_e-he,nt=Math.max(0,Math.ceil(tt/le)),it=Math.min(Q,(O+tt)/le);for(let Ze=0;Ze<J;++Ze){let ct=Ze-Ne,Ue=Math.max(0,Math.ceil(ct/oe)),kn=Math.min(se,(_+ct)/oe);for(let wt=0;wt<K;++wt){let Qn=wt-Te,Qt=Math.max(0,Math.ceil(Qn/ge)),In=Math.min(Z,(W+Qn)/ge),ea=0;for(let zn=nt;zn<it;++zn){let un=zn*le-tt;for(let en=Ue;en<kn;++en){let La=en*oe-ct;for(let da=Qt;da<In;++da){let pa=da*ge-Qn,Sr=x*De+v*zn+b*en+w*da,ir=C*(O-1-un)+E*(_-1-La)+z*(W-1-pa)+$*ze;for(let Nr=0;Nr<ne;++Nr){let Qi=g[Sr+Nr],Wa=N[ir+Nr];ea+=Qi*Wa}}}}h[m*De+f*_e+A*Ze+y*wt+ze]=ea}}}return n.makeTensorInfo(c.shape,c.dtype,c.values)}var wO={kernelName:Qp,backendName:"cpu",kernelFunc:vO},kO=rt(Ns,e=>Math.cos(e)),IO={kernelName:Ns,backendName:"cpu",kernelFunc:kO},SO=rt(So,e=>Math.cosh(e)),NO={kernelName:So,backendName:"cpu",kernelFunc:SO};function TO(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=a,[d,p,c,h]=r.shape,m=s.shape[0],[f,A]=o,y=Be([m,f,A,h],"float32"),g=n.data.get(s.dataId).values,x=n.data.get(i.dataId).values,v=n.data.get(r.dataId).values,b=k.computeStrides(r.shape),w=k.computeStrides(y.shape);for(let N=0;N<m;N++){let C=N*4,E=g[C],z=g[C+1],$=g[C+2],S=g[C+3],O=x[N];if(O>=d)continue;let _=f>1?($-E)*(p-1)/(f-1):0,W=A>1?(S-z)*(c-1)/(A-1):0;for(let G=0;G<f;G++){let H=f>1?E*(p-1)+G*_:.5*(E+$)*(p-1);if(H<0||H>p-1){for(let J=0;J<A;J++)for(let K=0;K<h;K++){let ne=K+J*w[2]+G*w[1]+N*w[0];y.values[ne]=l}continue}if(u==="bilinear"){let J=Math.floor(H),K=Math.ceil(H),ne=H-J;for(let Q=0;Q<A;Q++){let se=A>1?z*(c-1)+Q*W:.5*(z+S)*(c-1);if(se<0||se>c-1){for(let ge=0;ge<h;ge++){let he=ge+Q*w[2]+G*w[1]+N*w[0];y.values[he]=l}continue}let Z=Math.floor(se),le=Math.ceil(se),oe=se-Z;for(let ge=0;ge<h;ge++){let he=ge+Z*b[2]+J*b[1]+O*b[0],Ne=v[he];he=ge+le*b[2]+J*b[1]+O*b[0];let Te=v[he];he=ge+Z*b[2]+K*b[1]+O*b[0];let De=v[he];he=ge+le*b[2]+K*b[1]+O*b[0];let ze=v[he],_e=Ne+(Te-Ne)*oe,tt=De+(ze-De)*oe;he=ge+Q*w[2]+G*w[1]+N*w[0],y.values[he]=_e+(tt-_e)*ne}}}else for(let J=0;J<A;++J){let K=A>1?z*(c-1)+J*W:.5*(z+S)*(c-1);if(K<0||K>c-1){for(let se=0;se<h;se++){let Z=se+J*w[2]+G*w[1]+N*w[0];y.values[Z]=l}continue}let ne=Math.round(K),Q=Math.round(H);for(let se=0;se<h;se++){let Z=se+ne*b[2]+Q*b[1]+O*b[0],le=se+J*w[2]+G*w[1]+N*w[0];y.values[le]=v[Z]}}}}return n.makeTensorInfo(y.shape,y.dtype,y.values)}var CO={kernelName:No,backendName:"cpu",kernelFunc:TO};function EO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a;ve(r,"cumsum");let u=F.getAxesPermutation([s],r.shape.length),l=r;u!=null&&(l=ia({inputs:{x:r},backend:n,attrs:{perm:u}}));let d=F.getInnerMostAxes(1,r.shape.length)[0];if(d!==l.shape.length-1)throw new Error(`backend.cumsum in CPU expects an inner-most axis=${l.shape.length-1} but got axis=${d}`);let p=fa(l.dtype,"int32"),c=k.makeZerosTypedArray(k.sizeFromShape(l.shape),p),h=n.data.get(l.dataId).values,m=l.shape[l.shape.length-1],f=o?(y,g)=>y+m-g-1:(y,g)=>y+g;for(let y=0;y<h.length;y+=m)for(let g=0;g<m;g++){let x=f(y,g);if(g===0)c[x]=i?0:h[x];else{let v=f(y,g-1);c[x]=i?h[v]+c[v]:h[x]+c[v]}}let A=n.makeTensorInfo(l.shape,p,c);if(u!=null){let y=F.getUndoAxesPermutation(u),g=ia({inputs:{x:A},backend:n,attrs:{perm:y}});return n.disposeIntermediateTensorInfo(A),n.disposeIntermediateTensorInfo(l),g}return A}var RO={kernelName:Ts,backendName:"cpu",kernelFunc:EO};function MO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let u=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,d=xA(u,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(s),d=x7(u,l,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var FO={kernelName:ec,backendName:"cpu",kernelFunc:MO};function $O(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(i==="NHWC",()=>`Only NHWC dataFormat supported on CPU for depthToSpace. 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Got strides ${i} and dilations '${c}'`);let h=F.computeConv2DInfo(r.shape,s.shape,i,c,o,l,!0),{filterHeight:m,filterWidth:f,dilationHeight:A,dilationWidth:y,padInfo:g}=h,x=g.left,v=g.top,b=h.outChannels/h.inChannels,w=new Lt(h.outShape,r.dtype),N=n.data.get(r.dataId).values,C=n.data.get(s.dataId).values,E=w.values;for(let z=0;z<h.batchSize;++z){let $=z*d[0],S=z*w.strides[0];for(let O=0;O<h.outHeight;++O){let _=S+O*w.strides[1],W=O*h.strideHeight-v;for(let G=0;G<m;++G){let H=W+G*A;if(H<0||H>=h.inHeight)continue;let J=G*p[0],K=$+H*d[1];for(let ne=0;ne<h.outWidth;++ne){let Q=_+ne*w.strides[2],se=ne*h.strideWidth-x;for(let Z=0;Z<f;++Z){let le=se+Z*y;if(le<0||le>=h.inWidth)continue;let oe=J+Z*p[1],ge=K+le*h.inChannels,he=Q,Ne=oe;for(let Te=0;Te<h.inChannels;++Te){let De=N[ge+Te];for(let ze=0;ze<b;++ze)E[he+ze]+=De*C[Ne+ze];he+=b,Ne+=b}}}}}}return n.makeTensorInfo(w.shape,w.dtype,w.values)}var _O={kernelName:Cs,backendName:"cpu",kernelFunc:dv};function OO(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:d}=a;ve([r,s],"depthwiseConv2dNativeBackpropFilter");let p=F.computeConv2DInfo(r.shape,d,i,o,u,l,!0),{strideHeight:c,strideWidth:h,filterHeight:m,filterWidth:f}=p,A=new Lt(p.filterShape,"float32"),y=p.padInfo.left,g=p.padInfo.top,x=p.outChannels/p.inChannels,v=n.data.get(r.dataId).values,b=new Lt(r.shape,r.dtype,v),w=n.data.get(s.dataId).values,N=new Lt(s.shape,s.dtype,w);for(let C=0;C<m;++C){let E=Math.max(0,Math.ceil((g-C)/c)),z=Math.min(p.outHeight,(p.inHeight+g-C)/c);for(let $=0;$<f;++$){let S=Math.max(0,Math.ceil((y-$)/h)),O=Math.min(p.outWidth,(p.inWidth+y-$)/h);for(let _=0;_<p.outChannels;++_){let W=Math.trunc(_/x),G=_%x,H=0;for(let J=0;J<p.batchSize;++J)for(let K=E;K<z;++K){let ne=C+K*c-g;for(let Q=S;Q<O;++Q){let se=$+Q*h-y;H+=b.get(J,ne,se,W)*N.get(J,K,Q,_)}}A.set(H,C,$,W,G)}}}return n.makeTensorInfo(A.shape,A.dtype,A.values)}var zO={kernelName:tc,backendName:"cpu",kernelFunc:OO};function PO(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:d}=a;ve([r,s],"depthwiseConv2DNativeBackpropInput");let p=k.computeStrides(r.shape),c=k.computeStrides(s.shape),h=F.computeConv2DInfo(d,s.shape,i,o,u,l,!0),m=new Lt(h.inShape,"float32"),f=m.values,[A,y,g]=m.strides,x=n.data.get(r.dataId).values,[v,b,w]=p,N=n.data.get(s.dataId).values,[C,E,z]=c,{batchSize:$,filterHeight:S,filterWidth:O,inChannels:_,inHeight:W,inWidth:G,outChannels:H,outHeight:J,outWidth:K,strideHeight:ne,strideWidth:Q}=h,se=S-1-h.padInfo.top,Z=O-1-h.padInfo.left,le=H/_;for(let oe=0;oe<$;++oe)for(let ge=0;ge<_;++ge)for(let he=0;he<W;++he){let Ne=he-se,Te=Math.max(0,Math.ceil(Ne/ne)),De=Math.min(J,(S+Ne)/ne);for(let ze=0;ze<G;++ze){let _e=ze-Z,tt=Math.max(0,Math.ceil(_e/Q)),nt=Math.min(K,(O+_e)/Q),it=0;for(let Ze=Te;Ze<De;++Ze){let ct=Ze*ne-Ne;for(let Ue=tt;Ue<nt;++Ue){let kn=Ue*Q-_e,wt=v*oe+b*Ze+w*Ue,Qn=C*(S-1-ct)+E*(O-1-kn)+z*ge;for(let Qt=0;Qt<le;++Qt){let In=ge*le+Qt,ea=x[wt+In],zn=N[Qn+Qt];it+=ea*zn}}}f[A*oe+y*he+g*ze+ge]=it}}return n.makeTensorInfo(m.shape,m.dtype,m.values)}var LO={kernelName:nc,backendName:"cpu",kernelFunc:PO};function WO(e){let{inputs:t,backend:n}=e,{x:a}=t,r=k.sizeFromShape(a.shape),s=n.data.get(a.dataId).values,i=Be([r,r],a.dtype),o=i.values;for(let l=0;l<s.length;l++)o[l*r+l]=s[l];let u=[...a.shape,...a.shape];return n.makeTensorInfo(u,i.dtype,i.values)}var BO={kernelName:ac,backendName:"cpu",kernelFunc:WO},VO={kernelName:Ou,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r}=e,{strides:s,pad:i,dilations:o}=n,u=t,l=u.data.get(a.dataId).values,d=a.shape.length,p=u.data.get(r.dataId).values,c=r.shape.length,{batchSize:h,inHeight:m,inWidth:f,inChannels:A,outHeight:y,outWidth:g,padInfo:x,strideHeight:v,strideWidth:b,filterHeight:w,filterWidth:N,dilationHeight:C,dilationWidth:E,outShape:z}=F.computeDilation2DInfo(a.shape,r.shape,s,i,"NHWC",o),$=k.sizeFromShape(z),S=z.length,O=k.getArrayFromDType(a.dtype,$);for(let _=0;_<h;++_)for(let W=0;W<y;++W){let G=W*v-x.top;for(let H=0;H<g;++H){let J=H*b-x.left;for(let K=0;K<A;++K){let ne=Number.MIN_SAFE_INTEGER;for(let se=0;se<w;++se){let Z=G+se*C;if(Z>=0&&Z<m)for(let le=0;le<N;++le){let oe=J+le*E;if(oe>=0&&oe<f){let ge=k.locToIndex([_,Z,oe,K],d,k.computeStrides(a.shape)),he=k.locToIndex([se,le,K],c,k.computeStrides(r.shape)),Ne=l[ge]+p[he];Ne>ne&&(ne=Ne)}}}let Q=k.locToIndex([_,W,H,K],S,k.computeStrides(z));O[Q]=ne}}}return{dataId:u.write(k.toTypedArray(O,a.dtype),z,a.dtype),shape:z,dtype:a.dtype}}},jO={kernelName:sc,backendName:"cpu",kernelFunc:({inputs:e,backend:t,attrs:n})=>{let{x:a,filter:r,dy:s}=e,{strides:i,pad:o,dilations:u}=n,l=t,d=k.toNestedArray(a.shape,l.data.get(a.dataId).values),p=k.toNestedArray(r.shape,l.data.get(r.dataId).values),{batchSize:c,inHeight:h,inWidth:m,inChannels:f,outHeight:A,outWidth:y,padInfo:g,strideHeight:x,strideWidth:v,filterHeight:b,filterWidth:w,dilationHeight:N,dilationWidth:C,outShape:E}=F.computeDilation2DInfo(a.shape,r.shape,i,o,"NHWC",u);k.assert(s.rank===E.length,()=>`Error in ${sc}, dy must have the same rank as output ${E.length}, but got ${s.rank}`);let z=k.toNestedArray(E,l.data.get(s.dataId).values),$=k.makeZerosNestedTypedArray(r.shape,r.dtype);for(let S=0;S<c;++S)for(let O=0;O<A;++O){let _=O*x-g.top;for(let W=0;W<y;++W){let G=W*v-g.left;for(let H=0;H<f;++H){let 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A=f;f=kd({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(h){let A=f;f=NA(n,f,h,o,m),n.disposeIntermediateTensorInfo(A)}return f}var gz={kernelName:hi,backendName:"cpu",kernelFunc:yz};function xz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=dv({inputs:{x:r,filter:s},backend:n,attrs:{strides:u,pad:l,dataFormat:d,dilations:p,dimRoundingMode:c}});if(i){let A=f;f=kd({inputs:{a:f,b:i},backend:n}),n.disposeIntermediateTensorInfo(A)}if(h){let A=f;f=NA(n,f,h,o,m),n.disposeIntermediateTensorInfo(A)}return f}var bz={kernelName:fi,backendName:"cpu",kernelFunc:xz};function vz(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=k.sizeFromShape(a.shape),i=r.shape,o=i[i.length-1],[u,l,d,p]=F.prepareAndValidate(a,r);if(l===0)return n.makeTensorInfo(u,a.dtype,[]);let 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qz(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:d}=a;ve(i,"LRNGrad");let p=k.sizeFromShape(i.shape),c=i.shape[3],h=n.data.get(i.dataId).values,m=n.data.get(r.dataId).values,f=n.data.get(s.dataId).values,A=new Float32Array(p),y=p;for(let g=0;g<y;g++){let x=g%c,v=g-x+Math.max(0,x-o),b=g-x+Math.min(c,x+o+1),w=0;for(let N=v;N<b;N++)w+=Math.pow(m[N],2);w=l*w+u;for(let N=v;N<b;N++){let C=-2*l*d*m[N]*f[g]/w;g===N&&(C+=Math.pow(w,-d)),C*=h[g],A[N]+=C}}return n.makeTensorInfo(i.shape,r.dtype,A)}var Xz={kernelName:cc,backendName:"cpu",kernelFunc:qz};function cv(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=n,u=r.shape,l=u.length,d=k.parseAxisParam(s,u),p=d,c=F.getAxesPermutation(p,l),h=o.data.get(r.dataId).values;if(c!=null){let v=new Array(l);for(let 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d=F.computePool3DInfo(s.shape,i,o,1,u,l),p=n.bufferSync(s),c=H_(p,d),h=d.strideDepth,m=d.strideHeight,f=d.strideWidth,A=d.dilationDepth,y=d.dilationHeight,g=d.dilationWidth,x=d.effectiveFilterDepth,v=d.effectiveFilterHeight,b=d.effectiveFilterWidth,w=x-1-d.padInfo.front,N=b-1-d.padInfo.left,C=v-1-d.padInfo.top,E=Be(s.shape,"float32"),z=n.bufferSync(r);for(let $=0;$<d.batchSize;++$)for(let S=0;S<d.inChannels;++S)for(let O=0;O<d.inDepth;++O)for(let _=0;_<d.inHeight;++_)for(let W=0;W<d.inWidth;++W){let G=O-w,H=_-C,J=W-N,K=0;for(let ne=0;ne<x;ne+=A){let Q=(G+ne)/h;if(!(Q<0||Q>=d.outDepth||Math.floor(Q)!==Q))for(let se=0;se<v;se+=y){let Z=(H+se)/m;if(!(Z<0||Z>=d.outHeight||Math.floor(Z)!==Z))for(let le=0;le<b;le+=g){let oe=(J+le)/f;if(oe<0||oe>=d.outWidth||Math.floor(oe)!==oe)continue;let ge=x*v*b-1-c.get($,Q,Z,oe,S),he=ne*v*b+se*b+le,Ne=ge===he?1:0;Ne!==0&&(K+=z.get($,Q,Z,oe,S)*Ne)}}}E.set(K,$,O,_,W,S)}return n.makeTensorInfo(E.shape,E.dtype,E.values)}var tP={kernelName:fc,backendName:"cpu",kernelFunc:eP};function nP(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;ve([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:d,dimRoundingMode:p}=a,c=F.computePool2DInfo(o.shape,u,l,1,d,p),h=n.data.get(o.dataId).values,m=Be(c.outShape,o.dtype,ov(h,o.shape,o.dtype,c).values),f=c.strideHeight,A=c.strideWidth,y=c.dilationHeight,g=c.dilationWidth,x=c.effectiveFilterHeight,v=c.effectiveFilterWidth,b=v-1-c.padInfo.left,w=x-1-c.padInfo.top,N=Be(o.shape,"float32"),C=n.data.get(r.dataId).values,E=Be(r.shape,"float32",C);for(let z=0;z<c.batchSize;++z)for(let $=0;$<c.inChannels;++$)for(let S=0;S<c.inHeight;++S)for(let O=0;O<c.inWidth;++O){let _=S-w,W=O-b,G=0;for(let H=0;H<x;H+=y){let J=(_+H)/f;if(!(J<0||J>=c.outHeight||Math.floor(J)!==J))for(let K=0;K<v;K+=g){let ne=(W+K)/A;if(ne<0||ne>=c.outWidth||Math.floor(ne)!==ne)continue;let Q=x*v-1-m.get(z,J,ne,$),se=H*v+K,Z=Q===se?1:0;Z!==0&&(G+=E.get(z,J,ne,$)*Z)}}N.set(G,z,S,O,$)}return n.makeTensorInfo(N.shape,N.dtype,N.values)}var aP={kernelName:hc,backendName:"cpu",kernelFunc:nP};function rP(e,t,n,a,r){let s=k.computeStrides(t),i=TA(e,t,n,s,r,"max"),o=ov(e,t,n,r,!0,a);return[i.values,o.values]}var sP={kernelName:mc,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,u=n;ve(a,"MaxPoolWithArgmax");let l=u.data.get(a.dataId).values,d=F.computePool2DInfo(a.shape,r,s,[1,1],i),[p,c]=rP(l,a.shape,a.dtype,o,d),h=u.write(p,d.outShape,a.dtype),m=u.write(c,d.outShape,a.dtype);return[{dataId:h,shape:d.outShape,dtype:a.dtype},{dataId:m,shape:d.outShape,dtype:"int32"}]}};function iP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=k.parseAxisParam(s,r.shape),u=F.computeOutAndReduceShapes(r.shape,o)[1],l=k.sizeFromShape(u),d=[],p=n.makeTensorInfo([],"float32",new Float32Array([l]));d.push(p);let c=Kr({inputs:{x:r},backend:n,attrs:{dtype:"float32"}});d.push(c);let h=CA({inputs:{a:c,b:p},backend:n});d.push(h);let m=Id({inputs:{x:h},backend:n,attrs:{axis:s,keepDims:i}});return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var oP={kernelName:Bs,backendName:"cpu",kernelFunc:iP};function lP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;ve(r,"min");let o=k.parseAxisParam(s,r.shape),u=o,l=F.getAxesPermutation(u,r.shape.length),d=r;l!=null&&(d=ia({inputs:{x:r},backend:n,attrs:{perm:l}}),u=F.getInnerMostAxes(u.length,r.shape.length)),F.assertAxesAreInnerMostDims("min",u,d.shape.length);let[p,c]=F.computeOutAndReduceShapes(d.shape,u),h=k.sizeFromShape(c),m=k.makeZerosTypedArray(k.sizeFromShape(p),d.dtype),f=n.data.get(d.dataId).values;for(let y=0;y<m.length;++y){let g=y*h,x=f[g];for(let v=0;v<h;++v){let b=f[g+v];(Number.isNaN(b)||b<x)&&(x=b)}m[y]=x}l!=null&&n.disposeIntermediateTensorInfo(d);let A=n.makeTensorInfo(p,d.dtype,m);if(i){let y=F.expandShapeToKeepDim(p,o),g=ft({inputs:{x:A},backend:n,attrs:{shape:y}});return 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u=o?r:hv({inputs:{logits:r},backend:n,attrs:{dim:-1}}),l=u.shape[0],d=u.shape[1],p=n.data.get(u.dataId).values,c=[l,s],h=k.makeZerosTypedArray(k.sizeFromShape(c),"int32");for(let m=0;m<l;++m){let f=m*d,A=new Float32Array(d-1);A[0]=p[f];for(let x=1;x<A.length;++x)A[x]=A[x-1]+p[f+x];let y=mP.alea(i.toString()),g=m*s;for(let x=0;x<s;++x){let v=y();h[g+x]=A.length;for(let b=0;b<A.length;b++)if(v<A[b]){h[g+x]=b;break}}}return o||n.disposeIntermediateTensorInfo(u),n.makeTensorInfo(c,"int32",h)}var gP={kernelName:Ac,backendName:"cpu",kernelFunc:yP},xP=Ka.nonMaxSuppressionV3Impl;function bP(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=a;ve(r,"NonMaxSuppression");let l=n.data.get(r.dataId).values,d=n.data.get(s.dataId).values,{selectedIndices:p}=xP(l,d,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var vP={kernelName:qo,backendName:"cpu",kernelFunc:bP},wP=Ka.nonMaxSuppressionV4Impl;function kP(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=a;ve(r,"NonMaxSuppressionPadded");let d=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,{selectedIndices:c,validOutputs:h}=wP(d,p,i,o,u,l);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var IP={kernelName:Xo,backendName:"cpu",kernelFunc:kP},SP=Ka.nonMaxSuppressionV5Impl;function NP(e){let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=a;ve(r,"NonMaxSuppressionWithScore");let d=n.data.get(r.dataId).values,p=n.data.get(s.dataId).values,c=i,h=o,m=u,f=l,{selectedIndices:A,selectedScores:y}=SP(d,p,c,h,m,f);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var TP={kernelName:Ko,backendName:"cpu",kernelFunc:NP};function CP(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a;ve(r,"oneHot");let u=k.sizeFromShape(r.shape),l=new Float32Array(u*s);l.fill(o);let d=n.data.get(r.dataId).values;for(let p=0;p<u;++p)d[p]>=0&&d[p]<s&&(l[p*s+d[p]]=i);return n.makeTensorInfo([...r.shape,s],"int32",l)}var EP={kernelName:Gs,backendName:"cpu",kernelFunc:CP};function $h(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("zerosLike is not supported for string tensors");if(a.dtype==="complex64"){let r=$i({inputs:{input:a},backend:n}),s=$h({inputs:{x:r},backend:n}),i=Ll({inputs:{input:a},backend:n}),o=$h({inputs:{x:i},backend:n}),u=Hn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return MA({backend:n,attrs:{shape:a.shape,value:0,dtype:a.dtype}})}var RP={kernelName:hl,backendName:"cpu",kernelFunc:$h};function fv(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported for string tensors");if(a.dtype==="complex64"){let r=$i({inputs:{input:a},backend:n}),s=fv({inputs:{x:r},backend:n}),i=Ll({inputs:{input:a},backend:n}),o=$h({inputs:{x:i},backend:n}),u=Hn({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return MA({backend:n,attrs:{shape:a.shape,value:1,dtype:a.dtype}})}var MP={kernelName:Zo,backendName:"cpu",kernelFunc:fv};function mv(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return Fh({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(d=>{let 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t.makeTensorInfo([o.length],s,o)}var PP={kernelName:Vu,backendName:"cpu",kernelFunc:zP},LP=rt(Qo,e=>1/e),WP={kernelName:Qo,backendName:"cpu",kernelFunc:LP};function BP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;ve(r,"resizeBilinear");let u=k.computeStrides(r.shape),[l,d]=o,[p,c,h,m]=r.shape,f=n.data.get(r.dataId).values,A=new Float32Array(k.sizeFromShape([p,l,d,m])),y=[s&&l>1?c-1:c,s&&d>1?h-1:h],g=[s&&l>1?l-1:l,s&&d>1?d-1:d],x=0,v=y[0]/g[0],b=y[1]/g[1];for(let w=0;w<p;w++)for(let N=0;N<l;N++){let C;i?C=v*(N+.5)-.5:C=v*N;let E=Math.max(0,Math.floor(C)),z=C-E,$=Math.min(c-1,Math.ceil(C)),S=w*u[0]+E*u[1],O=w*u[0]+$*u[1];for(let _=0;_<d;_++){let W;i?W=b*(_+.5)-.5:W=b*_;let G=Math.max(0,Math.floor(W)),H=W-G,J=Math.min(h-1,Math.ceil(W)),K=S+G*u[2],ne=O+G*u[2],Q=S+J*u[2],se=O+J*u[2];for(let Z=0;Z<m;Z++){let le=f[K+Z],oe=f[ne+Z],ge=f[Q+Z],he=f[se+Z],Ne=le+(ge-le)*H,Te=oe+(he-oe)*H,De=Ne+(Te-Ne)*z;A[x++]=De}}}return n.makeTensorInfo([p,l,d,m],"float32",A)}var VP={kernelName:Ys,backendName:"cpu",kernelFunc:BP};function jP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;ve([s,r],"resizeBilinearGrad");let o=k.computeStrides(r.shape),[u,l,d,p]=r.shape,[,c,h]=s.shape,m=new Float32Array(u*l*d*p),f=[i&&c>1?l-1:l,i&&h>1?d-1:d],A=[i&&c>1?c-1:c,i&&h>1?h-1:h],y=f[0]/A[0],g=f[1]/A[1],x=n.data.get(s.dataId).values,v=0;for(let b=0;b<u;b++){let w=b*o[0];for(let N=0;N<c;N++){let C=N*y,E=Math.floor(C),z=Math.min(Math.ceil(C),l-1),$=w+E*o[1],S=w+z*o[1],O=C-E,_=1-O;for(let W=0;W<h;W++){let G=W*g,H=Math.floor(G),J=Math.min(Math.ceil(G),d-1),K=G-H,ne=1-K,Q=$+H*o[2],se=$+J*o[2],Z=S+H*o[2],le=S+J*o[2],oe=_*ne,ge=_*K,he=O*ne,Ne=O*K;for(let Te=0;Te<p;Te++){let De=x[v++];m[Q+Te]+=De*oe,m[se+Te]+=De*ge,m[Z+Te]+=De*he,m[le+Te]+=De*Ne}}}}return n.makeTensorInfo([u,d,l,p],"float32",m)}var UP={kernelName:xc,backendName:"cpu",kernelFunc:jP};function HP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a;ve(r,"resizeNearestNeighbor");let u=k.computeStrides(r.shape),[l,d]=o,[p,c,h,m]=r.shape,f=n.data.get(r.dataId).values,A=new Float32Array(p*l*d*m),y=[s&&l>1?c-1:c,s&&d>1?h-1:h],g=[s&&l>1?l-1:l,s&&d>1?d-1:d],x=y[0]/g[0],v=y[1]/g[1],b=0;for(let w=0;w<p;w++){let N=w*u[0];for(let C=0;C<l;C++){let E=i?x*(C+.5):x*C,z=Math.min(c-1,s?Math.round(E):Math.floor(E));i&&(z=Math.max(0,z));let $=N+z*u[1];for(let S=0;S<d;S++){let O=i?v*(S+.5):v*S,_=Math.min(h-1,s?Math.round(O):Math.floor(O));i&&(_=Math.max(0,_));let W=$+_*u[2];for(let G=0;G<m;G++){let H=f[W+G];A[b++]=H}}}}return n.makeTensorInfo([p,l,d,m],r.dtype,A)}var GP={kernelName:ju,backendName:"cpu",kernelFunc:HP};function qP(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a;ve([s,r],"resizeNearestNeighborGrad");let o=k.computeStrides(r.shape),u=k.computeStrides(s.shape),[l,d,p,c]=r.shape,[,h,m]=s.shape,f=new Float32Array(l*d*p*c),A=n.data.get(s.dataId).values,y=[i&&h>1?d-1:d,i&&m>1?p-1:p],g=[i&&h>1?h-1:h,i&&m>1?m-1:m],x=y[0]/g[0],v=y[1]/g[1],b=1/x,w=1/v,N=Math.ceil(b)*2+2,C=Math.ceil(w)*2+2;for(let E=0;E<l;E++){let z=E*o[0];for(let $=0;$<d;$++){let S=z+$*o[1],O=Math.floor($*b),_=Math.floor(O-N/2);for(let W=0;W<p;W++){let G=S+W*o[2],H=Math.floor(W*w),J=Math.floor(H-C/2);for(let K=0;K<c;K++){let ne=0;for(let Q=0;Q<N;Q++){let se=Q+_;if(se<0||se>=h)continue;let Z=z+se*u[1],le=se*x,oe=Math.min(d-1,i?Math.round(le):Math.floor(le));if($===oe)for(let ge=0;ge<C;ge++){let he=ge+J;if(he<0||he>=m)continue;let Ne=Z+he*u[2],Te=he*v,De=Math.min(p-1,i?Math.round(Te):Math.floor(Te));W===De&&(ne+=A[Ne+K])}}f[G+K]=ne}}}}return n.makeTensorInfo(r.shape,r.dtype,f)}var XP={kernelName:gc,backendName:"cpu",kernelFunc:qP};function KP(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a;ve(r,"reverse");let i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Za({inputs:{x:r},backend:n});let u=new Lt(r.shape,r.dtype),l=n.bufferSync(r);for(let d=0;d<u.size;d++){let p=u.indexToLoc(d),c=p.slice();o.forEach(h=>c[h]=r.shape[h]-1-c[h]),u.set(l.get(...c),...p)}return n.makeTensorInfo(u.shape,u.dtype,u.values)}var ZP={kernelName:Qs,backendName:"cpu",kernelFunc:KP},YP={kernelName:fl,backendName:"cpu",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,u=k.getTypedArrayFromDType(a.dtype,k.sizeFromShape(a.shape)),[l,d,p,c]=a.shape,[h,m]=F.getImageCenter(i,d,p),f=255,A=Math.sin(r),y=Math.cos(r),g=o.data.get(a.dataId).values;for(let x=0;x<l;x++){let v=x*p*d*c;for(let b=0;b<d;b++){let w=b*(p*c);for(let N=0;N<p;N++){let C=N*c;for(let E=0;E<c;E++){let z=[l,b,N,E],$=z[2],S=z[1],O=($-h)*y-(S-m)*A,_=($-h)*A+(S-m)*y;O=Math.round(O+h),_=Math.round(_+m);let W=s;if(typeof s!="number"&&(E===3?W=f:W=s[E]),O>=0&&O<p&&_>=0&&_<d){let H=_*(p*c),J=O*c,K=v+H+J+E;W=g[K]}let G=v+w+C+E;u[G]=W}}}}return{dataId:o.write(u,a.shape,a.dtype),shape:a.shape,dtype:a.dtype}}},JP=rt(ei,e=>{let t=Math.floor(e);return e-t<.5?Math.floor(e):e-t>.5?Math.ceil(e):t%2==0?t:t+1}),QP={kernelName:ei,backendName:"cpu",kernelFunc:JP};function yv(e,t,n,a,r,s,i,o,u,l){let d=[a/r,r],p=e.values,c=t.values;if(a===0)return Be(n,t.dtype);let h=Be(d,t.dtype);h.values.fill(u);for(let m=0;m<s;m++){let f=[],A=0;for(let y=0;y<i;y++){let g=p[m*i+y];f.push(g),A+=g*o[y]}if(A<0||A>=a/r)throw new Error(`Invalid indices: ${f} does not index into ${n}`);for(let y=0;y<r;y++)l?h.values[A*r+y]+=c[m*r+y]:h.values[A*r+y]=t.rank===0?c[0]:c[m*r+y]}return h}function eL(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:u,sliceSize:l,strides:d,outputSize:p}=F.calculateShapes(s,r,i),c=!0,h=n.bufferSync(r),m=n.bufferSync(s),f=yv(h,m,i,p,l,u,o,d,0,c);return n.makeTensorInfo(i,f.dtype,f.values)}var tL={kernelName:tl,backendName:"cpu",kernelFunc:eL};function nL(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t;ve([a,r,s],"select");let i=a.shape.length,o=n.data.get(a.dataId).values,u=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,d=fa(r.dtype,s.dtype),p=k.makeZerosTypedArray(k.sizeFromShape(r.shape),d),c=0,h=i===0||i>1||r.shape.length===1?1:k.sizeFromShape(r.shape.slice(1));for(let m=0;m<o.length;m++)for(let f=0;f<h;f++)o[m]===1?p[c++]=u[m]:p[c++]=l[m];return n.makeTensorInfo(r.shape,d,p)}var aL={kernelName:nl,backendName:"cpu",kernelFunc:nL},rL=F.SELU_SCALEALPHA,sL=F.SELU_SCALE,iL=rt(al,e=>e>=0?sL*e:rL*(Math.exp(e)-1)),oL={kernelName:al,backendName:"cpu",kernelFunc:iL},lL=rt(il,e=>e<0?-1:e>0?1:0),uL={kernelName:il,backendName:"cpu",kernelFunc:lL},dL=rt(ni,e=>Math.sin(e)),pL={kernelName:ni,backendName:"cpu",kernelFunc:dL},cL=rt(sl,e=>Math.sinh(e)),hL={kernelName:sl,backendName:"cpu",kernelFunc:cL},fL=11920928955078125e-23,gv=Math.log(fL)+2,mL=rt(ol,e=>{let t=e>-gv,n=e<gv,a=Math.exp(e),r;return n?r=a:t?r=e:r=Math.log(1+a),r}),AL={kernelName:ol,backendName:"cpu",kernelFunc:mL};function yL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;ve([r],"spaceToBatchND");let o=k.sizeFromShape(s),u=[[0,0]];u.push(...i);for(let A=1+s.length;A<r.shape.length;++A)u.push([0,0]);let l=Av.kernelFunc({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),d=F.getReshaped(l.shape,s,o,!1),p=F.getPermuted(d.length,s.length,!1),c=F.getReshapedPermuted(l.shape,s,o,!1),h=ft({inputs:{x:l},backend:n,attrs:{shape:d}}),m=ia({inputs:{x:h},backend:n,attrs:{perm:p}}),f=ft({inputs:{x:m},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),f}var gL={kernelName:Uu,backendName:"cpu",kernelFunc:yL};function xL(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
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|
${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
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|
${i.shape}`);let o=n.data.get(a.dataId).values,u=n.data.get(r.dataId).values,l=n.data.get(s.dataId).values,d=n.data.get(i.dataId).values[0],[p,c,h,m,f]=V7(o,a.shape,a.dtype,u,r.dtype,l,d);return[n.makeTensorInfo(c,a.dtype,p),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(A=>Number(A)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var bL={kernelName:bc,backendName:"cpu",kernelFunc:xL};function vL(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape
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|
${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape
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|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.data.get(r.dataId).values),o=n.data.get(a.dataId).values,u=Array.from(n.data.get(s.dataId).values),[l,d,p]=j7(o,a.shape,a.dtype,i,u);return[n.makeTensorInfo(d,a.dtype,l),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var wL={kernelName:vc,backendName:"cpu",kernelFunc:vL};function kL(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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|
${s.shape}`);let i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,[l,d]=IA(i,a.shape,a.dtype,o,u,!0);return n.makeTensorInfo(d,a.dtype,l)}var IL={kernelName:wc,backendName:"cpu",kernelFunc:kL};function SL(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
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|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
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|
${s.shape}`);let i=n.data.get(a.dataId).values,o=n.data.get(r.dataId).values,u=n.data.get(s.dataId).values,[l,d]=IA(i,a.shape,a.dtype,o,u);return n.makeTensorInfo(d,a.dtype,l)}var NL={kernelName:kc,backendName:"cpu",kernelFunc:SL};function TL(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:u,numUpdates:l,sliceSize:d,strides:p,outputSize:c}=F.calculateShapes(s,r,o),h=!1,m=n.bufferSync(r),f=n.bufferSync(s),A=n.data.get(i.dataId).values[0],y=yv(m,f,o,c,d,l,u,p,A,h);return n.makeTensorInfo(o,y.dtype,y.values)}var CL={kernelName:Ic,backendName:"cpu",kernelFunc:TL};function EL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],u=F.prepareSplitSize(r,s,o),l=new Array(r.shape.length).fill(0),d=r.shape.slice();return u.map(p=>{let c=[...d];c[o]=p;let h=Di({inputs:{x:r},backend:n,attrs:{begin:l,size:c}});return l[o]+=p,h})}var RL={kernelName:ll,backendName:"cpu",kernelFunc:EL},ML=rt(ri,e=>Math.sqrt(e)),FL={kernelName:ri,backendName:"cpu",kernelFunc:ML},$L={kernelName:Hu,backendName:"cpu",kernelFunc:({inputs:e,backend:t})=>{let{x:n}=e,a=t;ve(n,"square");let r=a.data.get(n.dataId).values,s=new Float32Array(r.length);for(let i=0;i<r.length;++i){let o=r[i];s[i]=o*o}return{dataId:a.write(s,n.shape,n.dtype),shape:n.shape,dtype:n.dtype}}},DL=rt(zr,(e,t)=>{let n=t;return isNaN(e)?NaN:e>0?1:n.alpha}),_L={kernelName:zr,backendName:"cpu",kernelFunc:DL};function OL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a;ve(r,"stridedSlice");let{nonStrided:h,$begin:m,$strides:f,size:A,newShape:y,outShape:g}=hn.sliceInfo(r.shape,s,i,o,u,l,d,p,c),x=ft({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(h){let w=Di({inputs:{x},backend:n,attrs:{begin:m,size:A}});v=ft({inputs:{x:w},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(w)}else if(g.some(w=>w===0))v=n.makeTensorInfo(g,r.dtype,[]);else{let w=n.bufferSync(x),N=H7(g,w,f,m);v=n.makeTensorInfo(N.shape,N.dtype,N.values)}let b=ft({inputs:{x:v},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),b}var zL={kernelName:ul,backendName:"cpu",kernelFunc:OL};function PL(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=a,{data:d,dataSplits:p}=t,c=n.data.get(d.dataId).values,h=n.data.get(p.dataId).values,[m,f]=G7(c,h,r,s,i,o,u,l);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(p.shape,"int32",f)]}var LL={kernelName:Sc,backendName:"cpu",kernelFunc:PL};function WL(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of 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XL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;ve(r,"tile");let i=Z7(n.bufferSync(r),s);return n.makeTensorInfo(i.shape,i.dtype,i.values)}var KL={kernelName:Or,backendName:"cpu",kernelFunc:XL};function ZL(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a;ve(r,"topk");let o=n.data.get(r.dataId).values,[u,l]=Y7(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(u.shape,u.dtype,u.values),n.makeTensorInfo(l.shape,l.dtype,l.values)]}var YL={kernelName:dl,backendName:"cpu",kernelFunc:ZL};function JL(e){let{inputs:t,attrs:n,backend:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=n,[d,p,c,h]=r.shape,[m,f]=l!=null?l:[p,c],A=[d,m,f,h],y=k.computeStrides(r.shape),g=y[0],x=y[1],v=y[2],b=k.getTypedArrayFromDType(r.dtype,k.sizeFromShape(A));b.fill(u);let w=a.data.get(r.dataId).values,N=a.data.get(s.dataId).values;for(let C=0;C<d;++C){let E=s.shape[0]===1?N:N.subarray(C*8,C*8+8);for(let z=0;z<m;++z)for(let $=0;$<f;++$)for(let S=0;S<h;++S){let 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e!==2?!1:Ya(e).fenceSync!=null}function Vl(e,t){Array.isArray(e)||(e=[e]),e.forEach(n=>{n!=null&&k.assert(n.dtype!=="complex64",()=>`${t} does not support complex64 tensors in the WebGL backend.`)})}var Me=te();Me.registerFlag("HAS_WEBGL",()=>Me.getNumber("WEBGL_VERSION")>0);Me.registerFlag("WEBGL_VERSION",()=>OA(2)?2:OA(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",()=>Me.getBool("WEBGL_PACK"));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",()=>Lv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_MAX_TEXTURES_IN_SHADER",()=>Wv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",()=>{let e=Me.getNumber("WEBGL_VERSION");return e===0?0:Bv(e)});Me.registerFlag("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",()=>Me.getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!nd.isMobile());Me.registerFlag("WEBGL_RENDER_FLOAT32_CAPABLE",()=>Vv(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",()=>jv(Me.getNumber("WEBGL_VERSION")));Me.registerFlag("WEBGL_FENCE_API_ENABLED",()=>Uv(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,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_DELETE_TEXTURE_THRESHOLD must be -1 (indicating never delete) or at least 0, but got ${e}.`)});Me.registerFlag("WEBGL_FLUSH_THRESHOLD",()=>nd.isMobile()&&Me.getBool("IS_CHROME")?1:-1,e=>{if(e<0&&e!==-1)throw new Error(`WEBGL_FLUSH_THRESHOLD must be -1 (indicating never manual flush) or at least 0, but got ${e}.`)});Me.registerFlag("CPU_HANDOFF_SIZE_THRESHOLD",()=>128);function yn(){let e,t,n,a,r,s,i,o,u,l;return te().getNumber("WEBGL_VERSION")===2?(e="#version 300 es",t="in",n="out",a="in",r="texture",s="outputColor",i="out vec4 outputColor;",o=`
|
|
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)
|
|
`,u="",l=`
|
|
#define round(value) newRound(value)
|
|
int newRound(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 newRound(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`):(e="",t="attribute",n="varying",a="varying",r="texture2D",s="gl_FragColor",i="",o=`
|
|
#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));
|
|
}
|
|
`,u=`
|
|
uniform float INFINITY;
|
|
|
|
bool isinf(float val) {
|
|
return abs(val) == INFINITY;
|
|
}
|
|
bvec4 isinf(vec4 val) {
|
|
return equal(abs(val), vec4(INFINITY));
|
|
}
|
|
`,l=`
|
|
int round(float value) {
|
|
return int(floor(value + 0.5));
|
|
}
|
|
|
|
ivec4 round(vec4 value) {
|
|
return ivec4(floor(value + vec4(0.5)));
|
|
}
|
|
`),{version:e,attribute:t,varyingVs:n,varyingFs:a,texture2D:r,output:s,defineOutput:i,defineSpecialNaN:o,defineSpecialInf:u,defineRound:l}}function Pi(e,t,n="index"){let a=k.computeStrides(t);return a.map((r,s)=>{let i=`int ${e[s]} = ${n} / ${r}`,o=s===a.length-1?`int ${e[s+1]} = ${n} - ${e[s]} * ${r}`:`index -= ${e[s]} * ${r}`;return`${i}; ${o};`}).join("")}function PA(e){let t=k.computeStrides(e).map(n=>n.toString());return`
|
|
int getFlatIndex(ivec3 coords) {
|
|
return coords.x * ${t[0]} + coords.y * ${t[1]} + coords.z;
|
|
}
|
|
`}var Hv=`
|
|
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;
|
|
}
|
|
`,CW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outPackingScheme=Nd.DENSE;let t=Cd(e),n=yn();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${Pi(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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);
|
|
}
|
|
|
|
${n.output} = result;
|
|
}
|
|
`}},EW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outPackingScheme=Nd.DENSE;let t=Cd(e),n=yn();this.outputShape=e,this.userCode=`
|
|
ivec3 outCoordsFromFlatIndex(int index) {
|
|
${Pi(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${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));
|
|
}
|
|
|
|
${n.output} = result;
|
|
}
|
|
`}},RW=class{constructor(e){this.variableNames=["A"],this.outTexUsage=oa.DOWNLOAD;let t=yn();this.outputShape=e,this.userCode=`
|
|
${Hv}
|
|
|
|
void main() {
|
|
float x = getAAtOutCoords();
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},MW=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outTexUsage=oa.DOWNLOAD;let t=yn();this.outputShape=e,this.userCode=`
|
|
${Hv}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
float x = getChannel(getAAtOutCoords(), vec2(coords.y, coords.z));
|
|
${t.output} = encode_float(x);
|
|
}
|
|
`}},FW=class{constructor(e,t,n=!1){this.variableNames=["A"];let a=yn(),[r,s]=t;this.outputShape=e;let i="result";n&&(i="floor(result * 255. + 0.5)"),this.userCode=`
|
|
${PA(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
int flatIndex = getFlatIndex(coords);
|
|
int offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
int r = flatIndex / ${s};
|
|
int c = imod(flatIndex, ${s});
|
|
vec2 uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
|
|
vec4 values = ${a.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];
|
|
}
|
|
|
|
${a.output} = vec4(${i}, 0., 0., 0.);
|
|
}
|
|
`}},$W=class{constructor(e,t,n=!1){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let a=yn(),[r,s]=t;this.outputShape=e;let i="",o="result";n&&(o="floor(result * 255. + 0.5)");for(let u=0;u<=1;u++)for(let l=0;l<=1;l++){let d=u*2+l;i+=`
|
|
localCoords = coords;
|
|
if(localCoords[2] + ${l} < ${e[2]}) {
|
|
localCoords[2] += ${l};
|
|
if(localCoords[1] + ${u} < ${e[1]}) {
|
|
localCoords[1] += ${u};
|
|
|
|
flatIndex = getFlatIndex(localCoords);
|
|
offset = imod(flatIndex, 4);
|
|
|
|
flatIndex = idiv(flatIndex, 4, 1.);
|
|
|
|
r = flatIndex / ${s};
|
|
c = imod(flatIndex, ${s});
|
|
uv = (vec2(c, r) + halfCR) / vec2(${s}.0, ${r}.0);
|
|
values = ${a.texture2D}(A, uv);
|
|
|
|
if(offset == 0) {
|
|
result[${d}] = values[0];
|
|
} else if(offset == 1) {
|
|
result[${d}] = values[1];
|
|
} else if(offset == 2) {
|
|
result[${d}] = values[2];
|
|
} else {
|
|
result[${d}] = values[3];
|
|
}
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
${PA(e)}
|
|
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
int flatIndex, r, c, offset;
|
|
ivec3 localCoords;
|
|
vec2 uv;
|
|
vec4 values;
|
|
|
|
${i}
|
|
|
|
${a.output} = ${o};
|
|
}
|
|
`}},Gv={};Fe(Gv,{bindVertexProgramAttributeStreams:()=>tw,createBufferFromOutputTexture:()=>rw,createFloat16MatrixTexture:()=>Yv,createFloat16PackedMatrixTexture:()=>ew,createFloat32MatrixTexture:()=>Zv,createIndexBuffer:()=>Kv,createPackedMatrixTexture:()=>Qv,createUnsignedBytesMatrixTexture:()=>Jv,createVertexBuffer:()=>Xv,createVertexShader:()=>qv,downloadByteEncodedFloatMatrixFromOutputTexture:()=>iw,downloadFloat32MatrixFromBuffer:()=>sw,downloadMatrixFromPackedOutputTexture:()=>lw,downloadPackedMatrixFromBuffer:()=>ow,getInternalFormatForFloat16MatrixTexture:()=>WA,getInternalFormatForFloat16PackedMatrixTexture:()=>jA,getInternalFormatForFloat32MatrixTexture:()=>LA,getInternalFormatForPackedMatrixTexture:()=>VA,getInternalFormatForUnsignedBytesMatrixTexture:()=>BA,uploadDenseMatrixToTexture:()=>nw,uploadPixelDataToTexture:()=>aw});function qv(e){let t=yn(),n=`${t.version}
|
|
precision highp float;
|
|
${t.attribute} vec3 clipSpacePos;
|
|
${t.attribute} vec2 uv;
|
|
${t.varyingVs} vec2 resultUV;
|
|
|
|
void main() {
|
|
gl_Position = vec4(clipSpacePos, 1);
|
|
resultUV = uv;
|
|
}`;return kv(e,n)}function Xv(e){let t=new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]);return Tv(e,t)}function Kv(e){let t=new Uint16Array([0,1,2,2,1,3]);return Cv(e,t)}function Fd(e,t,n,a,r,s){Rv(t,n);let i=Ev(e),o=e.TEXTURE_2D;return xe(e,()=>e.bindTexture(o,i)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MIN_FILTER,e.NEAREST)),xe(e,()=>e.texParameteri(o,e.TEXTURE_MAG_FILTER,e.NEAREST)),xe(e,()=>e.texImage2D(o,0,a,t,n,0,r,s,null)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null)),i}function LA(e){return e.internalFormatFloat}function Zv(e,t,n,a){let[r,s]=Td(t,n);return Fd(e,r,s,LA(a),a.textureFormatFloat,e.FLOAT)}function WA(e){return e.internalFormatHalfFloat}function Yv(e,t,n,a){let[r,s]=Td(t,n);return Fd(e,r,s,WA(a),a.textureFormatFloat,a.textureTypeHalfFloat)}function BA(e){return e.downloadTextureFormat}function Jv(e,t,n,a){let[r,s]=Td(t,n);return Fd(e,r,s,BA(a),e.RGBA,e.UNSIGNED_BYTE)}function VA(e){return e.internalFormatPackedFloat}function Qv(e,t,n,a){let[r,s]=Bl(t,n);return Fd(e,r,s,VA(a),e.RGBA,e.FLOAT)}function jA(e){return e.internalFormatPackedHalfFloat}function ew(e,t,n,a){let[r,s]=Bl(t,n);return Fd(e,r,s,jA(a),e.RGBA,a.textureTypeHalfFloat)}function tw(e,t,n){let a=0,r=3*4,s=3*4+2*4;return xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,n)),DA(e,t,"clipSpacePos",n,3,s,a)&&DA(e,t,"uv",n,2,s,r)}function nw(e,t,n,a,r,s){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t));let i,o,u;r instanceof Uint8Array?(i=new Uint8Array(n*a*4),o=e.UNSIGNED_BYTE,u=e.RGBA):(i=new Float32Array(n*a*4),o=e.FLOAT,u=s.internalFormatPackedFloat),i.set(r),xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,u,n,a,0,e.RGBA,o,i)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function aw(e,t,n){xe(e,()=>e.bindTexture(e.TEXTURE_2D,t)),n.data instanceof Uint8Array?xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,n.width,n.height,0,e.RGBA,e.UNSIGNED_BYTE,n.data)):xe(e,()=>e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)),xe(e,()=>e.bindTexture(e.TEXTURE_2D,null))}function rw(e,t,n,a){let r=e.createBuffer();xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,r));let s=4*4*t*n;return xe(e,()=>e.bufferData(e.PIXEL_PACK_BUFFER,s,e.STREAM_READ)),xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,0)),xe(e,()=>e.bindBuffer(e.PIXEL_PACK_BUFFER,null)),r}function sw(e,t,n){let a=e,r=new Float32Array(n);return a.bindBuffer(a.PIXEL_PACK_BUFFER,t),a.getBufferSubData(a.PIXEL_PACK_BUFFER,0,r),a.bindBuffer(a.PIXEL_PACK_BUFFER,null),r}function iw(e,t,n,a){let[r,s]=Td(t,n),i=4,o=new Uint8Array(mW(t*n,i));return xe(e,()=>e.readPixels(0,0,r,s,a.downloadTextureFormat,e.UNSIGNED_BYTE,o)),new Float32Array(o.buffer)}function ow(e,t,n,a,r,s,i,o){let u=e,l=new Float32Array(AW(s,i));return u.bindBuffer(u.PIXEL_PACK_BUFFER,t),u.getBufferSubData(u.PIXEL_PACK_BUFFER,0,l),u.bindBuffer(u.PIXEL_PACK_BUFFER,null),l}function lw(e,t,n){let a=new Float32Array(t*n*4);return xe(e,()=>e.readPixels(0,0,n,t,e.RGBA,e.FLOAT,a)),a}var Bh=class{constructor(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[];let t=te().getNumber("WEBGL_VERSION");e!=null?(this.gl=e,Dh(t,e)):this.gl=Ya(t);let n="WEBGL_color_buffer_float",a="EXT_color_buffer_half_float";if(te().getNumber("WEBGL_VERSION")===1){let r="OES_texture_float",s="OES_texture_half_float";if(this.textureFloatExtension=Ed(this.gl,r),la(this.gl,s))this.textureHalfFloatExtension=Ed(this.gl,s);else if(te().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support half float textures, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.");if(this.colorBufferFloatExtension=this.gl.getExtension(n),la(this.gl,a))this.colorBufferHalfFloatExtension=Ed(this.gl,a);else if(te().get("WEBGL_FORCE_F16_TEXTURES"))throw new Error("GL context does not support color renderable half floats, yet the environment flag WEBGL_FORCE_F16_TEXTURES is set to true.")}else if(n="EXT_color_buffer_float",la(this.gl,n))this.colorBufferFloatExtension=this.gl.getExtension(n);else if(la(this.gl,a))this.colorBufferHalfFloatExtension=this.gl.getExtension(a);else throw new Error("GL context does not support color renderable floats");this.vertexBuffer=Xv(this.gl),this.indexBuffer=Kv(this.gl),this.framebuffer=Mv(this.gl),this.textureConfig=$A(this.gl,this.textureHalfFloatExtension)}get debug(){return te().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;xe(e,()=>e.finish()),xe(e,()=>e.bindFramebuffer(e.FRAMEBUFFER,null)),xe(e,()=>e.deleteFramebuffer(this.framebuffer)),xe(e,()=>e.bindBuffer(e.ARRAY_BUFFER,null)),xe(e,()=>e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,null)),xe(e,()=>e.deleteBuffer(this.indexBuffer)),this.disposed=!0}createFloat32MatrixTexture(e,t){return this.throwIfDisposed(),Zv(this.gl,e,t,this.textureConfig)}createFloat16MatrixTexture(e,t){return this.throwIfDisposed(),Yv(this.gl,e,t,this.textureConfig)}createUnsignedBytesMatrixTexture(e,t){return this.throwIfDisposed(),Jv(this.gl,e,t,this.textureConfig)}uploadPixelDataToTexture(e,t){this.throwIfDisposed(),aw(this.gl,e,t)}uploadDenseMatrixToTexture(e,t,n,a){this.throwIfDisposed(),nw(this.gl,e,t,n,a,this.textureConfig)}createFloat16PackedMatrixTexture(e,t){return this.throwIfDisposed(),ew(this.gl,e,t,this.textureConfig)}createPackedMatrixTexture(e,t){return this.throwIfDisposed(),Qv(this.gl,e,t,this.textureConfig)}deleteMatrixTexture(e){this.throwIfDisposed(),this.outputTexture===e&&(_A(this.gl,this.framebuffer),this.outputTexture=null),xe(this.gl,()=>this.gl.deleteTexture(e))}downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n){return this.downloadMatrixDriver(e,()=>iw(this.gl,t,n,this.textureConfig))}downloadPackedMatrixFromBuffer(e,t,n,a,r,s){return ow(this.gl,e,t,n,a,r,s,this.textureConfig)}downloadFloat32MatrixFromBuffer(e,t){return sw(this.gl,e,t)}createBufferFromTexture(e,t,n){this.bindTextureToFrameBuffer(e);let a=rw(this.gl,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),a}createAndWaitForFence(){let e=this.createFence(this.gl);return this.pollFence(e)}createFence(e){let t,n;if(te().getBool("WEBGL_FENCE_API_ENABLED")){let a=e,r=a.fenceSync(a.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=()=>{let s=a.clientWaitSync(r,0,0);return s===a.ALREADY_SIGNALED||s===a.CONDITION_SATISFIED},t=r}else te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=()=>this.isQueryAvailable(t,te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))):n=()=>!0;return{query:t,isFencePassed:n}}downloadMatrixFromPackedTexture(e,t,n){return this.downloadMatrixDriver(e,()=>lw(this.gl,t,n))}createProgram(e){this.throwIfDisposed();let t=this.gl,n=Iv(t,e);this.vertexShader==null&&(this.vertexShader=qv(t));let a=Sv(t);return xe(t,()=>t.attachShader(a,this.vertexShader)),xe(t,()=>t.attachShader(a,n)),Nv(t,a),this.debug&&_h(t,a),this.vertexAttrsAreBound||(this.setProgram(a),this.vertexAttrsAreBound=tw(t,this.program,this.vertexBuffer)),a}deleteProgram(e){this.throwIfDisposed(),e===this.program&&(this.program=null),e!=null&&xe(this.gl,()=>this.gl.deleteProgram(e))}setProgram(e){this.throwIfDisposed(),this.program=e,this.program!=null&&this.debug&&_h(this.gl,this.program),xe(this.gl,()=>this.gl.useProgram(e))}getUniformLocation(e,t,n=!0){return this.throwIfDisposed(),n?$v(this.gl,e,t):Dv(this.gl,e,t)}getAttributeLocation(e,t){return this.throwIfDisposed(),xe(this.gl,()=>this.gl.getAttribLocation(e,t))}getUniformLocationNoThrow(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)}setInputMatrixTexture(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),_v(this.gl,e,t,n)}setOutputMatrixTexture(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)}setOutputPackedMatrixTexture(e,t,n){this.throwIfDisposed();let[a,r]=Bl(t,n);this.setOutputMatrixTextureDriver(e,a,r)}setOutputMatrixWriteRegion(e,t,n,a){this.setOutputMatrixWriteRegionDriver(n,e,a,t)}setOutputPackedMatrixWriteRegion(e,t,n,a){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")}debugValidate(){this.program!=null&&_h(this.gl,this.program),Rd(this.gl)}executeProgram(){this.throwIfDisposed(),this.throwIfNoProgram();let e=this.gl;this.debug&&this.debugValidate(),xe(e,()=>e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0))}blockUntilAllProgramsCompleted(){this.throwIfDisposed(),xe(this.gl,()=>this.gl.finish())}getQueryTimerExtension(){return this.disjointQueryTimerExtension==null&&(this.disjointQueryTimerExtension=Ed(this.gl,te().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(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.createQuery();return n.beginQuery(a.TIME_ELAPSED_EXT,r),r}let e=this.getQueryTimerExtensionWebGL1(),t=e.createQueryEXT();return e.beginQueryEXT(e.TIME_ELAPSED_EXT,t),t}endQuery(){if(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")===2){let t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);return}let e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}async waitForQueryAndGetTime(e){return await k.repeatedTry(()=>this.disposed||this.isQueryAvailable(e,te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))),this.getQueryTime(e,te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}getQueryTime(e,t){if(t===0)return null;if(t===2){let n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}else{let n=this.getQueryTimerExtensionWebGL1();return n.getQueryObjectEXT(e,n.QUERY_RESULT_EXT)/1e6}}isQueryAvailable(e,t){if(t===0)return!0;if(t===2){let n=this.gl,a=this.getQueryTimerExtensionWebGL2(),r=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(a.GPU_DISJOINT_EXT)),r&&!this.disjoint}else{let n=this.getQueryTimerExtensionWebGL1(),a=n.getQueryObjectEXT(e,n.QUERY_RESULT_AVAILABLE_EXT);return this.disjoint==null&&(this.disjoint=this.gl.getParameter(n.GPU_DISJOINT_EXT)),a&&!this.disjoint}}pollFence(e){return new Promise(t=>{this.addItemToPoll(()=>e.isFencePassed(),()=>t())})}pollItems(){let e=DW(this.itemsToPoll.map(t=>t.isDoneFn));for(let t=0;t<=e;++t){let{resolveFn:n}=this.itemsToPoll[t];n()}this.itemsToPoll=this.itemsToPoll.slice(e+1)}addItemToPoll(e,t){this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),!(this.itemsToPoll.length>1)&&k.repeatedTry(()=>(this.pollItems(),this.itemsToPoll.length===0))}bindTextureToFrameBuffer(e){this.throwIfDisposed(),Oh(this.gl,e,this.framebuffer),this.debug&&Rd(this.gl)}unbindTextureToFrameBuffer(){this.outputTexture!=null?(Oh(this.gl,this.outputTexture,this.framebuffer),this.debug&&Rd(this.gl)):_A(this.gl,this.framebuffer)}downloadMatrixDriver(e,t){this.bindTextureToFrameBuffer(e);let n=t();return this.unbindTextureToFrameBuffer(),n}setOutputMatrixTextureDriver(e,t,n){this.throwIfDisposed();let a=this.gl;Oh(a,e,this.framebuffer),this.debug&&Rd(a),this.outputTexture=e,xe(a,()=>a.viewport(0,0,t,n)),xe(a,()=>a.scissor(0,0,t,n))}setOutputMatrixWriteRegionDriver(e,t,n,a){this.throwIfDisposed(),xe(this.gl,()=>this.gl.scissor(e,t,n,a))}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 DW(e){let t=0;for(;t<e.length&&e[t]();++t);return t-1}var{getBroadcastDims:uw}=F;function _W(e,t,n,a){let r=[];e.forEach(h=>{let m=k.sizeFromShape(h.shapeInfo.logicalShape);h.shapeInfo.isUniform?r.push(`uniform float ${h.name}${m>1?`[${m}]`:""};`):(r.push(`uniform sampler2D ${h.name};`),r.push(`uniform int offset${h.name};`))});let s=r.join(`
|
|
`),i=e.map(h=>OW(h,t,a)).join(`
|
|
`),o=t.texShape,u=yn(),l=LW(u),d,p,c=VW(u);return t.isPacked?(d=zW(t.logicalShape,o),p=BW(u)):(d=PW(t.logicalShape,o),p=WW(u)),a&&(c+=GW),[c,l,p,s,d,i,n].join(`
|
|
`)}function jl(e){let t=e.shapeInfo.logicalShape;switch(t.length){case 0:return rB(e);case 1:return iB(e);case 2:return lB(e);case 3:return dB(e);case 4:return cB(e);case 5:return hB(e);case 6:return fB(e);default:throw new Error(`${t.length}-D input sampling is not yet supported`)}}function dw(e){switch(e.shapeInfo.logicalShape.length){case 0:return aB(e);case 1:return sB(e);case 2:return oB(e);case 3:return uB(e);default:return pB(e)}}function OW(e,t,n=!1){let a="";n?a+=dw(e):a+=jl(e);let r=e.shapeInfo.logicalShape,s=t.logicalShape;return r.length<=s.length&&(n?a+=mB(e,t):a+=AB(e,t)),a}function zW(e,t){switch(e.length){case 0:return pw();case 1:return qW(e,t);case 2:return tB(e,t);case 3:return KW(e,t);default:return YW(e,t)}}function PW(e,t){switch(e.length){case 0:return pw();case 1:return XW(e,t);case 2:return nB(e,t);case 3:return ZW(e,t);case 4:return JW(e,t);case 5:return QW(e,t);case 6:return eB(e,t);default:throw new Error(`${e.length}-D output sampling is not yet supported`)}}function LW(e){return`
|
|
float sampleTexture(sampler2D textureSampler, vec2 uv) {
|
|
return ${e.texture2D}(textureSampler, uv).r;
|
|
}
|
|
`}function WW(e){return`
|
|
void setOutput(float val) {
|
|
${e.output} = vec4(val, 0, 0, 0);
|
|
}
|
|
`}function BW(e){return`
|
|
void setOutput(vec4 val) {
|
|
${e.output} = val;
|
|
}
|
|
`}function VW(e){return`${e.version}
|
|
precision highp float;
|
|
precision highp int;
|
|
precision highp sampler2D;
|
|
${e.varyingFs} vec2 resultUV;
|
|
${e.defineOutput}
|
|
const vec2 halfCR = vec2(0.5, 0.5);
|
|
|
|
struct ivec5
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
};
|
|
|
|
struct ivec6
|
|
{
|
|
int x;
|
|
int y;
|
|
int z;
|
|
int w;
|
|
int u;
|
|
int v;
|
|
};
|
|
|
|
uniform float NAN;
|
|
${e.defineSpecialNaN}
|
|
${e.defineSpecialInf}
|
|
${e.defineRound}
|
|
|
|
int imod(int x, int y) {
|
|
return x - y * (x / y);
|
|
}
|
|
|
|
int idiv(int a, int b, float sign) {
|
|
int res = a / b;
|
|
int mod = imod(a, b);
|
|
if (sign < 0. && mod != 0) {
|
|
res -= 1;
|
|
}
|
|
return res;
|
|
}
|
|
|
|
//Based on the work of Dave Hoskins
|
|
//https://www.shadertoy.com/view/4djSRW
|
|
#define HASHSCALE1 443.8975
|
|
float random(float seed){
|
|
vec2 p = resultUV * seed;
|
|
vec3 p3 = fract(vec3(p.xyx) * HASHSCALE1);
|
|
p3 += dot(p3, p3.yzx + 19.19);
|
|
return fract((p3.x + p3.y) * p3.z);
|
|
}
|
|
|
|
${jW}
|
|
${UW}
|
|
${HW}
|
|
`}var jW=`
|
|
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);
|
|
}
|
|
`,UW=`
|
|
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);
|
|
}
|
|
`,HW=`
|
|
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);
|
|
}
|
|
`,GW=`
|
|
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 pw(){return`
|
|
int getOutputCoords() {
|
|
return 0;
|
|
}
|
|
`}function qW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return n[0]===1?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.x * ${n[1]}.0);
|
|
}
|
|
`:n[1]===1?`
|
|
int getOutputCoords() {
|
|
return 2 * int(resultUV.y * ${n[0]}.0);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
return 2 * (resTexRC.x * ${n[1]} + resTexRC.y);
|
|
}
|
|
`}function XW(e,t){return t[0]===1?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.x * ${t[1]}.0);
|
|
}
|
|
`:t[1]===1?`
|
|
int getOutputCoords() {
|
|
return int(resultUV.y * ${t[0]}.0);
|
|
}
|
|
`:`
|
|
int getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
return resTexRC.x * ${t[1]} + resTexRC.y;
|
|
}
|
|
`}function KW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[2]/2),r=a*Math.ceil(e[1]/2);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
|
|
int b = index / ${r};
|
|
index -= b * ${r};
|
|
|
|
int r = 2 * (index / ${a});
|
|
int c = imod(index, ${a}) * 2;
|
|
|
|
return ivec3(b, r, c);
|
|
}
|
|
`}function ZW(e,t){let n=Pi(["r","c","d"],e);return`
|
|
ivec3 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}function YW(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],a=Math.ceil(e[e.length-1]/2),r=a*Math.ceil(e[e.length-2]/2),s=r,i="",o="b, r, c";for(let u=2;u<e.length-1;u++)s*=e[e.length-u-1],i=`
|
|
int b${u} = index / ${s};
|
|
index -= b${u} * ${s};
|
|
`+i,o=`b${u}, `+o;return`
|
|
ivec${e.length} getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
|
|
${i}
|
|
|
|
int b = index / ${r};
|
|
index -= b * ${r};
|
|
|
|
int r = 2 * (index / ${a});
|
|
int c = imod(index, ${a}) * 2;
|
|
|
|
return ivec${e.length}(${o});
|
|
}
|
|
`}function JW(e,t){let n=Pi(["r","c","d","d2"],e);return`
|
|
ivec4 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
${n}
|
|
return ivec4(r, c, d, d2);
|
|
}
|
|
`}function QW(e,t){let n=Pi(["r","c","d","d2","d3"],e);return`
|
|
ivec5 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx * vec2(${t[0]},
|
|
${t[1]}));
|
|
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec5 outShape = ivec5(r, c, d, d2, d3);
|
|
return outShape;
|
|
}
|
|
`}function eB(e,t){let n=Pi(["r","c","d","d2","d3","d4"],e);return`
|
|
ivec6 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
|
|
${n}
|
|
|
|
ivec6 result = ivec6(r, c, d, d2, d3, d4);
|
|
return result;
|
|
}
|
|
`}function tB(e,t){let n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(k.arraysEqual(e,t))return`
|
|
ivec2 getOutputCoords() {
|
|
return 2 * ivec2(resultUV.yx * vec2(${n[0]}, ${n[1]}));
|
|
}
|
|
`;let a=Math.ceil(e[1]/2);return`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${n[0]}, ${n[1]}));
|
|
|
|
int index = resTexRC.x * ${n[1]} + resTexRC.y;
|
|
int r = 2 * (index / ${a});
|
|
int c = imod(index, ${a}) * 2;
|
|
|
|
return ivec2(r, c);
|
|
}
|
|
`}function nB(e,t){return k.arraysEqual(e,t)?`
|
|
ivec2 getOutputCoords() {
|
|
return ivec2(resultUV.yx * vec2(${t[0]}, ${t[1]}));
|
|
}
|
|
`:e[1]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(index, 0);
|
|
}
|
|
`:e[0]===1?`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
return ivec2(0, index);
|
|
}
|
|
`:`
|
|
ivec2 getOutputCoords() {
|
|
ivec2 resTexRC = ivec2(resultUV.yx *
|
|
vec2(${t[0]}, ${t[1]}));
|
|
int index = resTexRC.x * ${t[1]} + resTexRC.y;
|
|
int r = index / ${e[1]};
|
|
int c = index - r * ${e[1]};
|
|
return ivec2(r, c);
|
|
}
|
|
`}function Li(e){return`offset${e}`}function aB(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=yn();return`
|
|
vec4 ${n}() {
|
|
return ${a.texture2D}(${t}, halfCR);
|
|
}
|
|
`}function rB(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`float ${n}() {return ${t};}`;let[a,r]=e.shapeInfo.texShape;if(a===1&&r===1)return`
|
|
float ${n}() {
|
|
return sampleTexture(${t}, halfCR);
|
|
}
|
|
`;let[s,i]=e.shapeInfo.texShape,o=Li(t);return`
|
|
float ${n}() {
|
|
vec2 uv = uvFromFlat(${s}, ${i}, ${o});
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function sB(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),a=e.shapeInfo.texShape,r=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],s=yn();return`
|
|
vec4 ${n}(int index) {
|
|
vec2 uv = packedUVfrom1D(
|
|
${r[0]}, ${r[1]}, index);
|
|
return ${s.texture2D}(${t}, uv);
|
|
}
|
|
`}function iB(e){let t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);if(e.shapeInfo.isUniform)return`
|
|
float ${n}(int index) {
|
|
${Ul(e)}
|
|
}
|
|
`;let a=e.shapeInfo.texShape,r=a[0],s=a[1];if(s===1&&r===1)return`
|
|
float ${n}(int index) {
|
|
return sampleTexture(${t}, halfCR);
|
|
}
|
|
`;let i=Li(t);return s===1?`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2(0.5, (float(index + ${i}) + 0.5) / ${r}.0);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`:r===1?`
|
|
float ${n}(int index) {
|
|
vec2 uv = vec2((float(index + ${i}) + 0.5) / ${s}.0, 0.5);
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`:`
|
|
float ${n}(int index) {
|
|
vec2 uv = uvFromFlat(${r}, ${s}, index + ${i});
|
|
return sampleTexture(${t}, uv);
|
|
}
|
|
`}function oB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=r[0],i=r[1],o=yn();if(r!=null&&k.arraysEqual(t,r))return`
|
|
vec4 ${a}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${i}.0, ${s}.0);
|
|
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`;let u=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],l=Math.ceil(t[1]/2);return`
|
|
vec4 ${a}(int row, int col) {
|
|
vec2 uv = packedUVfrom2D(${l}, ${u[0]}, ${u[1]}, row, col);
|
|
return ${o.texture2D}(${n}, uv);
|
|
}
|
|
`}function lB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape;if(r!=null&&k.arraysEqual(t,r)){let p=r[0],c=r[1];return`
|
|
float ${a}(int row, int col) {
|
|
vec2 uv = (vec2(col, row) + halfCR) / vec2(${c}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}let{newShape:s,keptDims:i}=k.squeezeShape(t),o=s;if(o.length<t.length){let p=Hl(e,o),c=["row","col"];return`
|
|
${jl(p)}
|
|
float ${a}(int row, int col) {
|
|
return ${a}(${Gl(c,i)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col) {
|
|
int index = round(dot(vec2(row, col), vec2(${t[1]}, 1)));
|
|
${Ul(e)}
|
|
}
|
|
`;let u=r[0],l=r[1],d=Li(n);return l===1?`
|
|
float ${a}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${t[1]}, 1, 1));
|
|
vec2 uv = vec2(0.5, (index + 0.5) / ${u}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:u===1?`
|
|
float ${a}(int row, int col) {
|
|
float index = dot(vec3(row, col, ${d}), vec3(${t[1]}, 1, 1));
|
|
vec2 uv = vec2((index + 0.5) / ${l}.0, 0.5);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`:`
|
|
float ${a}(int row, int col) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${t[1]} + col + ${d};
|
|
vec2 uv = uvFromFlat(${u}, ${l}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function uB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=e.shapeInfo.texShape,s=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];if(t[0]===1){let p=t.slice(1),c=[1,2],h=Hl(e,p),m=["b","row","col"];return`
|
|
${dw(h)}
|
|
vec4 ${a}(int b, int row, int col) {
|
|
return ${a}(${Gl(m,c)});
|
|
}
|
|
`}let i=s[0],o=s[1],u=Math.ceil(t[2]/2),l=u*Math.ceil(t[1]/2),d=yn();return`
|
|
vec4 ${a}(int b, int row, int col) {
|
|
vec2 uv = packedUVfrom3D(
|
|
${i}, ${o}, ${l}, ${u}, b, row, col);
|
|
return ${d.texture2D}(${n}, uv);
|
|
}
|
|
`}function dB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[1]*t[2],s=t[2],{newShape:i,keptDims:o}=k.squeezeShape(t),u=i;if(u.length<t.length){let m=Hl(e,u),f=["row","col","depth"];return`
|
|
${jl(m)}
|
|
float ${a}(int row, int col, int depth) {
|
|
return ${a}(${Gl(f,o)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth) {
|
|
int index = round(dot(vec3(row, col, depth),
|
|
vec3(${r}, ${s}, 1)));
|
|
${Ul(e)}
|
|
}
|
|
`;let l=e.shapeInfo.texShape,d=l[0],p=l[1],c=e.shapeInfo.flatOffset;if(p===r&&c==null)return`
|
|
float ${a}(int row, int col, int depth) {
|
|
float texR = float(row);
|
|
float texC = dot(vec2(col, depth), vec2(${s}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(p===s&&c==null)return`
|
|
float ${a}(int row, int col, int depth) {
|
|
float texR = dot(vec2(row, col), vec2(${t[1]}, 1));
|
|
float texC = float(depth);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${p}.0, ${d}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let h=Li(n);return`
|
|
float ${a}(int row, int col, int depth) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${r} + col * ${s} + depth + ${h};
|
|
vec2 uv = uvFromFlat(${d}, ${p}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function pB(e){let t=e.shapeInfo.logicalShape,n=t.length,a=e.name,r="get"+a.charAt(0).toUpperCase()+a.slice(1),s=e.shapeInfo.texShape,i=[Math.ceil(s[0]/2),Math.ceil(s[1]/2)],o=i[0],u=i[1],l=Math.ceil(t[n-1]/2),d=l*Math.ceil(t[n-2]/2),p="int b, int row, int col",c=`b * ${d} + (row / 2) * ${l} + (col / 2)`;for(let m=2;m<n-1;m++)p=`int b${m}, `+p,d*=t[n-m-1],c=`b${m} * ${d} + `+c;let h=yn();return`
|
|
vec4 ${r}(${p}) {
|
|
int index = ${c};
|
|
int texR = index / ${u};
|
|
int texC = index - texR * ${u};
|
|
vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${u}, ${o});
|
|
return ${h.texture2D}(${a}, uv);
|
|
}
|
|
`}function cB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[3],s=t[2]*r,i=t[1]*s,{newShape:o,keptDims:u}=k.squeezeShape(t);if(o.length<t.length){let m=Hl(e,o),f=["row","col","depth","depth2"];return`
|
|
${jl(m)}
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
return ${a}(${Gl(f,u)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
int index = round(dot(vec4(row, col, depth, depth2),
|
|
vec4(${i}, ${s}, ${r}, 1)));
|
|
${Ul(e)}
|
|
}
|
|
`;let l=e.shapeInfo.flatOffset,d=e.shapeInfo.texShape,p=d[0],c=d[1];if(c===i&&l==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
float texR = float(row);
|
|
float texC =
|
|
dot(vec3(col, depth, depth2),
|
|
vec3(${s}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${c}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(c===r&&l==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
float texR = dot(vec3(row, col, depth),
|
|
vec3(${t[1]*t[2]}, ${t[2]}, 1));
|
|
float texC = float(depth2);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${c}.0, ${p}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let h=Li(n);return`
|
|
float ${a}(int row, int col, int depth, int depth2) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${i} + col * ${s} +
|
|
depth * ${r} + depth2;
|
|
vec2 uv = uvFromFlat(${p}, ${c}, index + ${h});
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function hB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),r=t[4],s=t[3]*r,i=t[2]*s,o=t[1]*i,{newShape:u,keptDims:l}=k.squeezeShape(t);if(u.length<t.length){let f=Hl(e,u),A=["row","col","depth","depth2","depth3"];return`
|
|
${jl(f)}
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
return ${a}(${Gl(A,l)});
|
|
}
|
|
`}if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
float index = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${o}, ${i}, ${s}, ${r})) +
|
|
depth3;
|
|
${Ul(e)}
|
|
}
|
|
`;let d=e.shapeInfo.flatOffset,p=e.shapeInfo.texShape,c=p[0],h=p[1];if(h===o&&d==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${i}, ${s}, ${r}, 1));
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${c}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(h===r&&d==null)return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
float texR = dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]},
|
|
${t[2]*t[3]}, ${t[3]}, 1));
|
|
int texC = depth3;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${h}.0, ${c}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let m=Li(n);return`
|
|
float ${a}(int row, int col, int depth, int depth2, int depth3) {
|
|
// Explicitly use integer operations as dot() only works on floats.
|
|
int index = row * ${o} + col * ${i} + depth * ${s} +
|
|
depth2 * ${r} + depth3 + ${m};
|
|
vec2 uv = uvFromFlat(${c}, ${h}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function fB(e){let t=e.shapeInfo.logicalShape,n=e.name,a="get"+n.charAt(0).toUpperCase()+n.slice(1),{newShape:r,keptDims:s}=k.squeezeShape(t);if(r.length<t.length){let A=Hl(e,r),y=["row","col","depth","depth2","depth3","depth4"];return`
|
|
${jl(A)}
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
return ${a}(${Gl(y,s)});
|
|
}
|
|
`}let i=t[5],o=t[4]*i,u=t[3]*o,l=t[2]*u,d=t[1]*l;if(e.shapeInfo.isUniform)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int index = round(dot(
|
|
vec4(row, col, depth, depth2),
|
|
vec4(${d}, ${l}, ${u}, ${o})) +
|
|
dot(
|
|
vec2(depth3, depth4),
|
|
vec2(${i}, 1)));
|
|
${Ul(e)}
|
|
}
|
|
`;let p=e.shapeInfo.flatOffset,c=e.shapeInfo.texShape,h=c[0],m=c[1];if(m===d&&p==null)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
int texR = row;
|
|
float texC = dot(vec4(col, depth, depth2, depth3),
|
|
vec4(${l}, ${u}, ${o}, ${i})) +
|
|
float(depth4);
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${m}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;if(m===i&&p==null)return`
|
|
float ${a}(int row, int col, int depth,
|
|
int depth2, int depth3, int depth4) {
|
|
float texR = dot(vec4(row, col, depth, depth2),
|
|
vec4(${t[1]*t[2]*t[3]*t[4]},
|
|
${t[2]*t[3]*t[4]},
|
|
${t[3]*t[4]},
|
|
${t[4]})) + float(depth3);
|
|
int texC = depth4;
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${m}.0, ${h}.0);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`;let f=Li(n);return`
|
|
float ${a}(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 * ${d} + col * ${l} + depth * ${u} +
|
|
depth2 * ${o} + depth3 * ${i} + depth4 + ${f};
|
|
vec2 uv = uvFromFlat(${h}, ${m}, index);
|
|
return sampleTexture(${n}, uv);
|
|
}
|
|
`}function Ul(e){let t=e.name,n=k.sizeFromShape(e.shapeInfo.logicalShape);return n<2?`return ${t};`:`
|
|
for (int i = 0; i < ${n}; i++) {
|
|
if (i == index) {
|
|
return ${t}[i];
|
|
}
|
|
}
|
|
`}function mB(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=e.shapeInfo.logicalShape.length,i=t.logicalShape.length,o=uw(e.shapeInfo.logicalShape,t.logicalShape),u=ut(i),l=i-s,d,p=["x","y","z","w","u","v"];s===0?d="":i<2&&o.length>=1?d="coords = 0;":d=o.map(A=>`coords.${p[A+l]} = 0;`).join(`
|
|
`);let c="";i<2&&s>0?c="coords":c=e.shapeInfo.logicalShape.map((A,y)=>`coords.${p[y+l]}`).join(", ");let h="return outputValue;",m=k.sizeFromShape(e.shapeInfo.logicalShape)===1,f=k.sizeFromShape(t.logicalShape)===1;if(s===1&&!m&&!f)h=`
|
|
return vec4(outputValue.xy, outputValue.xy);
|
|
`;else if(m&&!f)i===1?h=`
|
|
return vec4(outputValue.x, outputValue.x, 0., 0.);
|
|
`:h=`
|
|
return vec4(outputValue.x);
|
|
`;else if(o.length){let A=s-2,y=s-1;o.indexOf(A)>-1&&o.indexOf(y)>-1?h="return vec4(outputValue.x);":o.indexOf(A)>-1?h="return vec4(outputValue.x, outputValue.y, outputValue.x, outputValue.y);":o.indexOf(y)>-1&&(h="return vec4(outputValue.xx, outputValue.zz);")}return`
|
|
vec4 ${r}() {
|
|
${u} coords = getOutputCoords();
|
|
${d}
|
|
vec4 outputValue = get${a}(${c});
|
|
${h}
|
|
}
|
|
`}function AB(e,t){let n=e.name,a=n.charAt(0).toUpperCase()+n.slice(1),r="get"+a+"AtOutCoords",s=t.texShape,i=e.shapeInfo.texShape,o=e.shapeInfo.logicalShape.length,u=t.logicalShape.length;if(!e.shapeInfo.isUniform&&o===u&&e.shapeInfo.flatOffset==null&&k.arraysEqual(i,s))return`
|
|
float ${r}() {
|
|
return sampleTexture(${n}, resultUV);
|
|
}
|
|
`;let l=ut(u),d=uw(e.shapeInfo.logicalShape,t.logicalShape),p=u-o,c,h=["x","y","z","w","u","v"];o===0?c="":u<2&&d.length>=1?c="coords = 0;":c=d.map(f=>`coords.${h[f+p]} = 0;`).join(`
|
|
`);let m="";return u<2&&o>0?m="coords":m=e.shapeInfo.logicalShape.map((f,A)=>`coords.${h[A+p]}`).join(", "),`
|
|
float ${r}() {
|
|
${l} coords = getOutputCoords();
|
|
${c}
|
|
return get${a}(${m});
|
|
}
|
|
`}function ut(e){if(e<=1)return"int";if(e===2)return"ivec2";if(e===3)return"ivec3";if(e===4)return"ivec4";if(e===5)return"ivec5";if(e===6)return"ivec6";throw Error(`GPU for rank ${e} is not yet supported`)}function Hl(e,t){let n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function Gl(e,t){return t.map(n=>e[n]).join(", ")}function yB(e,t,n,a){let r=t.userCode,s=n.map((h,m)=>{let f={logicalShape:h.shape,texShape:h.isUniform?null:h.texData.texShape,isUniform:h.isUniform,isPacked:h.isUniform?!1:h.texData.isPacked,flatOffset:null};return h.texData!=null&&h.texData.slice!=null&&h.texData.slice.flatOffset>0&&(f.flatOffset=h.texData.slice.flatOffset),{name:t.variableNames[m],shapeInfo:f}}),i=s.map(h=>h.shapeInfo),o={logicalShape:a.shape,texShape:a.texData.texShape,isUniform:!1,isPacked:a.texData.isPacked,flatOffset:null},u=_W(s,o,r,t.packedInputs),l=e.createProgram(u),d=null,p=e.getUniformLocation(l,"NAN",!1);te().getNumber("WEBGL_VERSION")===1&&(d=e.getUniformLocation(l,"INFINITY",!1));let c={};for(let h=0;h<t.variableNames.length;h++){let m=t.variableNames[h],f=!1;c[m]=e.getUniformLocation(l,m,f),c[`offset${m}`]=e.getUniformLocation(l,`offset${m}`,f)}return{program:t,source:u,webGLProgram:l,uniformLocations:c,inShapeInfos:i,outShapeInfo:o,infLoc:d,nanLoc:p}}function cw(e,t){if(e.length!==t.length)throw Error(`Binary was compiled with ${e.length} inputs, but was executed with ${t.length} inputs`);e.forEach((n,a)=>{let r=n.logicalShape,s=t[a],i=s.shape;if(!k.arraysEqual(r,i))throw Error(`Binary was compiled with different shapes than the current args. Shapes ${r} and ${i} must match`);if(n.isUniform&&s.isUniform)return;let o=n.texShape,u=s.isUniform?null:s.texData.texShape;if(!k.arraysEqual(o,u))throw Error(`Binary was compiled with different texture shapes than the current args. Shape ${o} and ${u} must match`)})}function gB(e,t,n,a,r){cw(t.inShapeInfos,n),cw([t.outShapeInfo],[a]);let s=a.texData.texture,i=a.texData.texShape;a.texData.isPacked?e.setOutputPackedMatrixTexture(s,i[0],i[1]):e.setOutputMatrixTexture(s,i[0],i[1]),e.setProgram(t.webGLProgram),te().getNumber("WEBGL_VERSION")===1&&t.infLoc!==null&&e.gl.uniform1f(t.infLoc,Infinity),t.nanLoc!==null&&e.gl.uniform1f(t.nanLoc,NaN),n.forEach((o,u)=>{let l=t.program.variableNames[u],d=t.uniformLocations[l],p=t.uniformLocations[`offset${l}`];if(d!=null){if(o.isUniform){if(k.sizeFromShape(o.shape)<2)e.gl.uniform1f(d,o.uniformValues[0]);else{let c=o.uniformValues;c instanceof Float32Array||(c=new Float32Array(c)),e.gl.uniform1fv(d,c)}return}o.texData.slice!=null&&p!=null&&e.gl.uniform1i(p,o.texData.slice.flatOffset),e.setInputMatrixTexture(o.texData.texture,d,u)}}),r!=null&&r(e,t.webGLProgram),e.executeProgram()}function xB(e,t,n){let a="";t.concat(n).forEach(i=>{let o=i.texData!=null&&i.texData.slice!=null&&i.texData.slice.flatOffset>0,u=i.isUniform?"uniform":i.texData.texShape;a+=`${i.shape}_${u}_${o}`});let r=e.userCode,s=e.constructor.name;return s+="_"+a+"_"+r,s}var{addImpl:bB,bincountImpl:hw,bincountReduceImpl:vB,ceilImpl:wB,concatImpl:kB,equalImpl:IB,expImpl:SB,expm1Impl:NB,floorImpl:TB,gatherNdImpl:CB,gatherV2Impl:EB,greaterImpl:RB,greaterEqualImpl:MB,lessImpl:FB,lessEqualImpl:$B,linSpaceImpl:DB,logImpl:_B,maxImpl:OB,maximumImpl:zB,minimumImpl:PB,multiplyImpl:LB,negImpl:WB,notEqualImpl:BB,prodImpl:VB,rangeImpl:jB,rsqrtImpl:UB,simpleAbsImpl:fw,sliceImpl:HB,sparseFillEmptyRowsImpl:GB,sparseReshapeImpl:qB,sparseSegmentReductionImpl:mw,stridedSliceImpl:XB,stringNGramsImpl:KB,stringSplitImpl:ZB,stringToHashBucketFastImpl:YB,subImpl:JB,tileImpl:QB,topKImpl:eV,transposeImpl:UA,uniqueImpl:tV}=yA;function Aw(e,t){return["x","y","z","w","u","v"].slice(0,t).map(n=>`${e}.${n}`)}function gn(e,t){return t===1?[e]:Aw(e,t)}function nV(e,t){if(e===1)return"rc";let n="";for(let a=0;a<e;a++)n+=t[a],a<e-1&&(n+=",");return n}var aV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0,this.outputShape=e;let t=e.length;if(t===0)this.userCode=`
|
|
void main() {
|
|
setOutput(vec4(getA(), 0., 0., 0.));
|
|
}
|
|
`;else{let n=gn("rc",t),a=ut(t),r=sV(t,e,n),s=iV(t,e[e.length-1],e[e.length-2],n),i=oV(e,n);this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
|
|
if(${r}) {
|
|
setOutput(vec4(0));
|
|
} else {
|
|
${s}
|
|
|
|
setOutput(vec4(${i}));
|
|
}
|
|
}
|
|
`}}};function rV(e,t){let n=[];for(let a=0;a<=1;a++)for(let r=0;r<=1;r++){let s=`${a===0?"r":"rp1"}, ${r===0?"c":"cp1"}`;for(let i=2;i<e;i++)s=`${t[t.length-1-i]},`+s;n.push(s)}return n}function sV(e,t,n){if(e===1)return`rc > ${t[0]}`;let a="";for(let r=e-2;r<e;r++)a+=`${n[r]} >= ${t[r]}`,r<e-1&&(a+="||");return a}function iV(e,t,n,a){if(e===1)return"";let r=a.slice(-2);return`
|
|
int r = ${r[0]};
|
|
int c = ${r[1]};
|
|
int rp1 = r + 1;
|
|
int cp1 = c + 1;
|
|
|
|
bool cEdge = cp1 >= ${t};
|
|
bool rEdge = rp1 >= ${n};
|
|
`}function oV(e,t){let n=e.length,a=rV(n,t);return n===1?`getA(rc),
|
|
rc + 1 >= ${e[0]} ? 0. : getA(rc + 1),
|
|
0, 0`:`getA(${a[0]}),
|
|
cEdge ? 0. : getA(${a[1]}),
|
|
rEdge ? 0. : getA(${a[2]}),
|
|
rEdge || cEdge ? 0. : getA(${a[3]})`}var yw=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let n="";for(let a=0;a<4;a++){let r="thisRC = rc;";a%2==1&&(r+="thisRC.z += 1;"),a>1&&(r+="thisRC.y += 1;"),n+=`
|
|
${r}
|
|
${a>0?"if(thisRC.y < rows && thisRC.z < cols){":""}
|
|
int flatIndex = getFlatIndex(thisRC);
|
|
|
|
ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);
|
|
vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));
|
|
|
|
result[${a}] =
|
|
getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);
|
|
${a>0?"}":""}
|
|
`}this.userCode=`
|
|
${lV(t)}
|
|
${PA(e)}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
ivec3 thisRC;
|
|
int rows = ${e[1]};
|
|
int cols = ${e[2]};
|
|
|
|
${n}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function lV(e){return`
|
|
ivec3 inputCoordsFromReshapedOutCoords(int index) {
|
|
${Pi(["r","c","d"],e)}
|
|
return ivec3(r, c, d);
|
|
}
|
|
`}var uV=class{constructor(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this._numBytesAllocated=0,this._numBytesFree=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}acquireTexture(e,t,n){let a=xw(t,n),r=bw(e,a,n);r in this.freeTextures||(this.freeTextures[r]=[]),r in this.usedTextures||(this.usedTextures[r]=[]);let s=gw(e,a,this.gpgpu.gl,this.gpgpu.textureConfig,n);if(this.freeTextures[r].length>0){this.numFreeTextures--,this.numUsedTextures++,this._numBytesFree-=s,this.log();let o=this.freeTextures[r].shift();return this.usedTextures[r].push(o),o}let i;return a===an.PACKED_2X2_FLOAT32?i=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):a===an.PACKED_2X2_FLOAT16?i=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):a===an.UNPACKED_FLOAT32?i=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):a===an.UNPACKED_FLOAT16?i=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):a===an.PACKED_4X1_UNSIGNED_BYTE&&(i=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[r].push(i),this.numUsedTextures++,this._numBytesAllocated+=s,this.log(),i}releaseTexture(e,t,n,a){if(this.freeTextures==null)return;let r=xw(n,a),s=bw(t,r,a);s in this.freeTextures||(this.freeTextures[s]=[]);let i=gw(t,r,this.gpgpu.gl,this.gpgpu.textureConfig,a),o=te().get("WEBGL_DELETE_TEXTURE_THRESHOLD");o!==-1&&this._numBytesAllocated>o?(this.gpgpu.deleteMatrixTexture(e),this._numBytesAllocated-=i):(this.freeTextures[s].push(e),this.numFreeTextures++,this._numBytesFree+=i),this.numUsedTextures--;let u=this.usedTextures[s],l=u.indexOf(e);if(l<0)throw new Error("Cannot release a texture that was never provided by this texture manager");u.splice(l,1),this.log()}log(){if(!this.logEnabled)return;let e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",`${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 dV(e,t){let n=e;if(t===n.R32F)return 4;if(t===n.R16F)return 2;if(t===n.RGBA32F||t===e.RGBA)return 16;if(t===n.RGBA16F)return 8;throw new Error(`Unknown internal format ${t}`)}function gw(e,t,n,a,r){let s=pV(t,a),i;if(r){let[u,l]=Bl(e[0],e[1]);i=u*l}else{let[u,l]=Td(e[0],e[1]);i=u*l}let o=dV(n,s);return i*o}function pV(e,t){switch(e){case an.PACKED_2X2_FLOAT32:return VA(t);case an.PACKED_2X2_FLOAT16:return jA(t);case an.UNPACKED_FLOAT32:return LA(t);case an.UNPACKED_FLOAT16:return WA(t);case an.PACKED_4X1_UNSIGNED_BYTE:return BA(t);default:throw new Error(`Unknown physical texture type ${e}`)}}function cV(e){return te().getBool("WEBGL_RENDER_FLOAT32_ENABLED")?e?an.PACKED_2X2_FLOAT32:an.UNPACKED_FLOAT32:e?an.PACKED_2X2_FLOAT16:an.UNPACKED_FLOAT16}function xw(e,t){if(e===oa.UPLOAD)return an.PACKED_2X2_FLOAT32;if(e===oa.RENDER||e==null)return cV(t);if(e===oa.DOWNLOAD||e===oa.PIXELS)return an.PACKED_4X1_UNSIGNED_BYTE;throw new Error(`Unknown logical texture type ${e}`)}function bw(e,t,n){return`${e[0]}_${e[1]}_${t}_${n}`}var Zr=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);
|
|
}
|
|
`}},Na="if (isnan(x)) return x;",hV="return x;",vw="return abs(x);",fV="return (x >= 0.0) ? x : (exp(x) - 1.0);",mV=Na+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,AV=Na+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,Vh="return x;",yV="return 1.0 / (1.0 + exp(-1.0 * x));",gV="return x;",xV=`
|
|
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;
|
|
`,bV=`
|
|
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;
|
|
`,vV=`
|
|
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;
|
|
`,wV="return 1.0 / (1.0 + exp(-1.0 * x));",ql=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.userCode=`
|
|
vec4 unaryOperation(vec4 x) {
|
|
${t}
|
|
}
|
|
|
|
void main() {
|
|
vec4 x = getAAtOutCoords();
|
|
vec4 y = unaryOperation(x);
|
|
|
|
setOutput(y);
|
|
}
|
|
`}},kV=class{constructor(e){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!1,this.outputShape=e;let t=e.length,n=gn("rc",t),a=ut(t),r=nV(t,n),s=n.slice(-2),i=t<=1?"rc":`vec2(${s.join(",")})`;this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
vec4 packedInput = getA(${r});
|
|
|
|
setOutput(getChannel(packedInput, ${i}));
|
|
}
|
|
`}},IV=Ka.whereImpl,SV=1e-7,NV=1e-4,HA={};function TV(e){return e in HA||(HA[e]={}),HA[e]}var CV=te().getNumber("CPU_HANDOFF_SIZE_THRESHOLD"),EV=600;function RV(){return te().global.screen==null?1024:te().global.screen.height*te().global.screen.width*window.devicePixelRatio*EV/1024/1024}var Xl=class extends Cu{constructor(e){super();if(this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.dataRefCount=new WeakMap,this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.lastGlFlushTime=0,this.warnedAboutMemory=!1,this.pendingDeletes=0,this.disposed=!1,!te().getBool("HAS_WEBGL"))throw new Error("WebGL is not supported on this device");if(e==null){let t=Ya(te().getNumber("WEBGL_VERSION"));this.binaryCache=TV(te().getNumber("WEBGL_VERSION")),this.gpgpu=new Bh(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 uV(this.gpgpu),this.numMBBeforeWarning=RV(),this.texData=new jp(this,hr())}nextDataId(){return Xl.nextDataId++}numDataIds(){return this.texData.numDataIds()-this.pendingDeletes}write(e,t,n){if((te().getBool("WEBGL_CHECK_NUMERICAL_PROBLEMS")||te().getBool("DEBUG"))&&this.checkNumericalProblems(e),n==="complex64"&&e!=null)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");let a={id:this.nextDataId()};return this.texData.set(a,{shape:t,dtype:n,values:e,usage:oa.UPLOAD,refCount:1}),a}refCount(e){return this.texData.has(e)?this.texData.get(e).refCount:0}incRef(e){let t=this.texData.get(e);t.refCount++}decRef(e){if(this.texData.has(e)){let t=this.texData.get(e);t.refCount--}}move(e,t,n,a,r){if(te().getBool("DEBUG")&&this.checkNumericalProblems(t),a==="complex64")throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");this.texData.set(e,{shape:n,dtype:a,values:t,usage:oa.UPLOAD,refCount:r})}disposeIntermediateTensorInfo(e){this.disposeData(e.dataId)}readSync(e){let t=this.texData.get(e),{values:n,dtype:a,complexTensorInfos:r,slice:s,shape:i,isPacked:o}=t;if(s!=null){let p;o?p=new ql(i,Vh):p=new Zr(i,Vh);let c=this.runWebGLProgram(p,[{dataId:e,shape:i,dtype:a}],a),h=this.readSync(c.dataId);return this.disposeIntermediateTensorInfo(c),h}if(n!=null)return this.convertAndCacheOnCPU(e);if(a==="string")return n;let u=this.activeTimers!=null,l;u&&(l=k.now());let d;if(a==="complex64"){let p=this.readSync(r.real.dataId),c=this.readSync(r.imag.dataId);d=F.mergeRealAndImagArrays(p,c)}else d=this.getValuesFromTexture(e);return u&&(this.downloadWaitMs+=k.now()-l),this.convertAndCacheOnCPU(e,d)}async read(e){if(this.pendingRead.has(e)){let h=this.pendingRead.get(e);return new Promise(m=>h.push(m))}let t=this.texData.get(e),{values:n,shape:a,slice:r,dtype:s,complexTensorInfos:i,isPacked:o}=t;if(r!=null){let h;o?h=new ql(a,Vh):h=new Zr(a,Vh);let m=this.runWebGLProgram(h,[{dataId:e,shape:a,dtype:s}],s),f=this.read(m.dataId);return this.disposeIntermediateTensorInfo(m),f}if(n!=null)return this.convertAndCacheOnCPU(e);if(!te().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&te().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,l;if(s!=="complex64"&&te().get("WEBGL_BUFFER_SUPPORTED")){l=this.decode(e);let h=this.texData.get(l.dataId);u=this.gpgpu.createBufferFromTexture(h.texture,...Cd(a))}this.pendingRead.set(e,[]),s!=="complex64"&&await this.gpgpu.createAndWaitForFence();let d;if(s==="complex64"){let h=await Promise.all([this.read(i.real.dataId),this.read(i.imag.dataId)]),m=h[0],f=h[1];d=F.mergeRealAndImagArrays(m,f)}else if(u==null)d=this.getValuesFromTexture(e);else{let h=k.sizeFromShape(a);d=this.gpgpu.downloadFloat32MatrixFromBuffer(u,h)}l!=null&&this.disposeIntermediateTensorInfo(l);let p=this.convertAndCacheOnCPU(e,d),c=this.pendingRead.get(e);return this.pendingRead.delete(e),c.forEach(h=>h(p)),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)&&hr().removeDataId(e,this),this.pendingDeletes--),p}bufferSync(e){let t=this.readSync(e.dataId),n=t;if(e.dtype==="string")try{n=t.map(a=>k.decodeString(a))}catch(a){throw new Error("Failed to decode encoded string bytes into utf-8")}return Be(e.shape,e.dtype,n)}checkNumericalProblems(e){if(e!=null)for(let t=0;t<e.length;t++){let n=e[t];if(!vv(n))throw te().getBool("WEBGL_RENDER_FLOAT32_CAPABLE")?Error(`The value ${n} cannot be represented with your current settings. Consider enabling float32 rendering: 'tf.env().set('WEBGL_RENDER_FLOAT32_ENABLED', true);'`):Error(`The value ${n} cannot be represented on this device.`)}}getValuesFromTexture(e){let{shape:t,dtype:n,isPacked:a}=this.texData.get(e),r=k.sizeFromShape(t);if(te().getBool("WEBGL_DOWNLOAD_FLOAT_ENABLED")){let p=this.decode(e),c=this.texData.get(p.dataId),h=this.gpgpu.downloadMatrixFromPackedTexture(c.texture,...Cd(t)).subarray(0,r);return this.disposeIntermediateTensorInfo(p),h}let s=te().getBool("WEBGL_PACK")&&a===!0,i=s?zh(t):t,o=s?new MW(i):new RW(i),u=this.runWebGLProgram(o,[{shape:i,dtype:n,dataId:e}],"float32"),l=this.texData.get(u.dataId),d=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(l.texture,l.texShape[0],l.texShape[1]).subarray(0,r);return this.disposeIntermediateTensorInfo(u),d}timerAvailable(){return te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0}async time(e){let t=this.activeTimers,n=[],a=!1;this.programTimersStack==null?(this.programTimersStack=n,a=!0):this.activeTimers.push(n),this.activeTimers=n,e();let r=k.flatten(this.activeTimers.map(o=>o.query)).filter(o=>o!=null),s=k.flatten(this.activeTimers.map(o=>o.name)).filter(o=>o!=null);this.activeTimers=t,a&&(this.programTimersStack=null);let i={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:null,wallMs:null};if(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0){let o=await Promise.all(r);i.kernelMs=k.sum(o),i.getExtraProfileInfo=()=>o.map((u,l)=>({name:s[l],ms:u})).map(u=>`${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 te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?this.gpgpu.beginQuery():{startMs:k.now(),endMs:null}}endTimer(e){return te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0?(this.gpgpu.endQuery(),e):(e.endMs=k.now(),e)}async getQueryTime(e){if(te().getNumber("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE")>0)return this.gpgpu.waitForQueryAndGetTime(e);let t=e;return t.endMs-t.startMs}disposeData(e,t=!1){if(this.pendingDisposal.has(e))return!1;if(!this.texData.has(e))return!0;if(t?this.texData.get(e).refCount=0:this.texData.get(e).refCount--,!t&&this.texData.get(e).refCount>0)return!1;if(this.pendingRead.has(e))return this.pendingDisposal.add(e),this.pendingDeletes++,!1;this.releaseGPUData(e);let{complexTensorInfos:n}=this.texData.get(e);return n!=null&&(this.disposeData(n.real.dataId,t),this.disposeData(n.imag.dataId,t)),this.texData.delete(e),!0}releaseGPUData(e){let{texture:t,dtype:n,texShape:a,usage:r,isPacked:s,slice:i}=this.texData.get(e),o=i&&i.origDataId||e,u=this.dataRefCount.get(o);u>1?this.dataRefCount.set(o,u-1):(this.dataRefCount.delete(o),t!=null&&(this.numBytesInGPU-=this.computeBytes(a,n),this.textureManager.releaseTexture(t,a,r,s)));let l=this.texData.get(e);l.texture=null,l.texShape=null,l.isPacked=!1,l.slice=null}getTexture(e){return this.uploadToGPU(e),this.texData.get(e).texture}getDataInfo(e){return this.texData.get(e)}shouldExecuteOnCPU(e,t=CV){return te().getBool("WEBGL_CPU_FORWARD")&&e.every(n=>this.texData.get(n.dataId).texture==null&&k.sizeFromShape(n.shape)<t)}getGPGPUContext(){return this.gpgpu}where(e){F.warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");let t=e.dataSync();return IV(e.shape,t)}packedUnaryOp(e,t,n){let a=new ql(e.shape,t),r=this.compileAndRun(a,[e],n);return hr().makeTensorFromDataId(r.dataId,r.shape,r.dtype)}abs(e){if(this.shouldExecuteOnCPU([e])&&e.dtype!=="complex64"){let a=fw(this.texData.get(e.dataId).values);return this.makeOutput(e.shape,e.dtype,a)}if(te().getBool("WEBGL_PACK_UNARY_OPERATIONS"))return this.packedUnaryOp(e,vw,e.dtype);let t=new Zr(e.shape,vw),n=this.compileAndRun(t,[e]);return hr().makeTensorFromDataId(n.dataId,n.shape,n.dtype)}makeTensorInfo(e,t,n){let a;if(t==="string"&&n!=null&&n.length>0&&k.isString(n[0])){let r=n.map(s=>k.encodeString(s));a=this.write(r,e,t)}else a=this.write(n,e,t);return this.texData.get(a).usage=null,{dataId:a,shape:e,dtype:t}}makeOutput(e,t,n){let{dataId:a}=this.makeTensorInfo(e,t,n);return hr().makeTensorFromDataId(a,e,t,this)}unpackTensor(e){let t=new kV(e.shape);return this.runWebGLProgram(t,[e],e.dtype)}packTensor(e){let t=new aV(e.shape),n=!0;return this.runWebGLProgram(t,[e],e.dtype,null,n)}packedReshape(e,t){let n=[Oi(e.shape),...zi(e.shape)],a={dtype:e.dtype,shape:n,dataId:e.dataId},r=[Oi(t),...zi(t)],s=new yw(r,n),i=!0,o=this.runWebGLProgram(s,[a],e.dtype,null,i);return{dataId:o.dataId,shape:t,dtype:o.dtype}}decode(e){let t=this.texData.get(e),{isPacked:n,shape:a,dtype:r}=t,s=zh(a),i;n?i=new EW(s):i=new CW(s);let o=!0,u=this.runWebGLProgram(i,[{shape:s,dtype:r,dataId:e}],r,null,o);return{dtype:r,shape:a,dataId:u.dataId}}runWebGLProgram(e,t,n,a,r=!1){let s=this.makeTensorInfo(e.outputShape,n),i=this.texData.get(s.dataId);if(e.packedOutput&&(i.isPacked=!0),e.outPackingScheme===Nd.DENSE){let f=Cd(e.outputShape);i.texShape=f.map(A=>A*2)}if(e.outTexUsage!=null&&(i.usage=e.outTexUsage),k.sizeFromShape(s.shape)===0)return i.values=k.getTypedArrayFromDType(s.dtype,0),s;let o=[],u=t.map(f=>{if(f.dtype==="complex64")throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");let A=this.texData.get(f.dataId);if(A.texture==null){if(!e.packedInputs&&k.sizeFromShape(f.shape)<=te().getNumber("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:f.shape,texData:null,isUniform:!0,uniformValues:A.values};e.packedInputs&&(A.isPacked=!0,A.shape=f.shape)}else if(!!A.isPacked!=!!e.packedInputs)f=A.isPacked?this.unpackTensor(f):this.packTensor(f),o.push(f),A=this.texData.get(f.dataId);else if(A.isPacked&&!Md(A.shape,f.shape)){let y=f,g=f.shape;f.shape=A.shape,f=this.packedReshape(f,g),o.push(f),A=this.texData.get(f.dataId),y.shape=g}return this.uploadToGPU(f.dataId),{shape:f.shape,texData:A,isUniform:!1}});this.uploadToGPU(s.dataId);let l={shape:s.shape,texData:i,isUniform:!1},d=xB(e,u,l),p=this.getAndSaveBinary(d,()=>yB(this.gpgpu,e,u,l)),c=this.activeTimers!=null,h;c&&(h=this.startTimer()),gB(this.gpgpu,p,u,l,a),o.forEach(f=>this.disposeIntermediateTensorInfo(f)),c&&(h=this.endTimer(h),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(h)}));let m=te().get("WEBGL_FLUSH_THRESHOLD");if(m>0){let f=k.now();f-this.lastGlFlushTime>m&&(this.gpgpu.gl.flush(),this.lastGlFlushTime=f)}if(!te().getBool("WEBGL_LAZILY_UNPACK")&&i.isPacked&&r===!1){let f=this.unpackTensor(s);return this.disposeIntermediateTensorInfo(s),f}return s}compileAndRun(e,t,n,a,r=!1){return n=n||t[0].dtype,this.runWebGLProgram(e,t,n,a,r)}getAndSaveBinary(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]}getTextureManager(){return this.textureManager}dispose(){this.disposed||(te().getBool("IS_TEST")||Object.keys(this.binaryCache).forEach(e=>{this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram),delete this.binaryCache[e]}),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(!te().get("WEBGL_RENDER_FLOAT32_ENABLED")){let e=te().getBool("DEBUG");te().set("DEBUG",!1);let t=this.abs(we(1e-8)).dataSync()[0];if(te().set("DEBUG",e),t>0)return 32}return 16})),this.floatPrecisionValue}epsilon(){return this.floatPrecision()===32?SV:NV}uploadToGPU(e){let t=this.texData.get(e),{shape:n,dtype:a,values:r,texture:s,usage:i,isPacked:o}=t;if(s!=null)return;let u=this.activeTimers!=null,l;u&&(l=k.now());let d=t.texShape;if(d==null&&(d=Pv(n,o),t.texShape=d),r!=null){let p=zh(n),c,h=d[1],m=d[0],f=r instanceof Uint8Array;o?([h,m]=Bl(d[0],d[1]),c=new $W(p,[m,h],f)):c=new FW(p,[m,h],f);let A=this.makeTensorInfo([m,h],a);f?this.texData.get(A.dataId).usage=oa.PIXELS:this.texData.get(A.dataId).usage=oa.UPLOAD,this.gpgpu.uploadDenseMatrixToTexture(this.getTexture(A.dataId),h,m,r);let y=!0,g=this.runWebGLProgram(c,[A],a,null,y),x=this.texData.get(g.dataId);t.texture=x.texture,t.texShape=x.texShape,t.isPacked=x.isPacked,t.usage=x.usage,this.disposeIntermediateTensorInfo(A),this.texData.delete(g.dataId),t.values=null,u&&(this.uploadWaitMs+=k.now()-l)}else{let p=this.acquireTexture(d,i,a,o);t.texture=p}}convertAndCacheOnCPU(e,t){let n=this.texData.get(e),{dtype:a}=n;return this.releaseGPUData(e),t!=null&&(n.values=MV(t,a)),n.values}acquireTexture(e,t,n,a){if(this.numBytesInGPU+=this.computeBytes(e,n),!this.warnedAboutMemory&&this.numBytesInGPU>this.numMBBeforeWarning*1024*1024){let r=(this.numBytesInGPU/1024/1024).toFixed(2);this.warnedAboutMemory=!0,console.warn(`High memory usage in GPU: ${r} MB, most likely due to a memory leak`)}return this.textureManager.acquireTexture(e,t,a)}computeBytes(e,t){return e[0]*e[1]*k.bytesPerElement(t)}};Xl.nextDataId=0;function MV(e,t){if(t==="float32"||t==="complex64")return e;if(t==="int32"||t==="bool"){let n=t==="int32"?new Int32Array(e.length):new Uint8Array(e.length);for(let a=0;a<n.length;++a)n[a]=Math.round(e[a]);return n}else throw new Error(`Unknown dtype ${t}`)}var ww="3.7.0";function kw(){te().set("WEBGL_FORCE_F16_TEXTURES",!0)}nd.isBrowser()&&kl("webgl",()=>new Xl,2);var FV={forceHalfFloat:kw},Iw=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,Kl=class{constructor(e,t,n){this.variableNames=["A","B"],this.outputShape=F.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOperation(float a, float b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float a = getAAtOutCoords();
|
|
float b = getBAtOutCoords();
|
|
setOutput(binaryOperation(a, b));
|
|
}
|
|
`}},jh=`
|
|
result.r = isNaN.r > 0. ? NAN : result.r;
|
|
result.g = isNaN.g > 0. ? NAN : result.g;
|
|
result.b = isNaN.b > 0. ? NAN : result.b;
|
|
result.a = isNaN.a > 0. ? NAN : result.a;
|
|
`,$d=class{constructor(e,t,n,a=!1){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.packedInputs=!0,this.packedOutput=!0,this.outputShape=F.assertAndGetBroadcastShape(t,n);let r=this.outputShape.length,s="";if(a)if(r===0||k.sizeFromShape(this.outputShape)===1)s=`
|
|
result.y = 0.;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else if(s=`
|
|
${ut(r)} coords = getOutputCoords();
|
|
`,r===1)s+=`
|
|
result.y = (coords + 1) >= ${this.outputShape[0]} ? 0. : result.y;
|
|
result.z = 0.;
|
|
result.w = 0.;
|
|
`;else{let i=gn("coords",r);s+=`
|
|
bool nextRowOutOfBounds =
|
|
(${i[r-2]} + 1) >= ${this.outputShape[r-2]};
|
|
bool nextColOutOfBounds =
|
|
(${i[r-1]} + 1) >= ${this.outputShape[r-1]};
|
|
result.y = nextColOutOfBounds ? 0. : result.y;
|
|
result.z = nextRowOutOfBounds ? 0. : result.z;
|
|
result.w = nextColOutOfBounds || nextRowOutOfBounds ? 0. : result.w;
|
|
`}this.userCode=`
|
|
vec4 binaryOperation(vec4 a, vec4 b) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
vec4 a = getAAtOutCoords();
|
|
vec4 b = getBAtOutCoords();
|
|
|
|
vec4 result = binaryOperation(a, b);
|
|
${s}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}};function Gn(e){let{inputs:t,backend:n}=e,{x:a}=t;return n.incRef(a.dataId),{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}var $V={kernelName:_s,backendName:"webgl",kernelFunc:Gn};function Yr(e){let{inputs:t,backend:n}=e,{real:a,imag:r}=t,s=n.makeTensorInfo(a.shape,"complex64"),i=n.texData.get(s.dataId),o=Gn({inputs:{x:a},backend:n}),u=Gn({inputs:{x:r},backend:n});return i.complexTensorInfos={real:o,imag:u},s}var DV={kernelName:Zp,backendName:"webgl",kernelFunc:Yr},Sw="return (a < 0.) ? b * a : a;",Nw=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function _V(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{alpha:s}=a,i=n.makeTensorInfo([],"float32",k.createScalarValue(s,"float32")),o=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new $d(Nw,r.shape,i.shape):new Kl(Sw,r.shape,i.shape),u=n.runWebGLProgram(o,[r,i],r.dtype);return n.disposeIntermediateTensorInfo(i),u}var OV={kernelName:Os,backendName:"webgl",kernelFunc:_V},Tw="return (a < 0.) ? b * a : a;",Cw=`
|
|
vec4 aLessThanZero = vec4(lessThan(a, vec4(0.)));
|
|
return (aLessThanZero * (b * a)) + ((vec4(1.0) - aLessThanZero) * a);
|
|
`;function zV(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new $d(Cw,a.shape,r.shape):new Kl(Tw,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)}var PV={kernelName:Ks,backendName:"webgl",kernelFunc:zV},Ew="if (isnan(x)) return x;",LV=`
|
|
if (isnan(a)) return a;
|
|
if (isnan(b)) return b;
|
|
`,WV=`
|
|
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 Ke({opSnippet:e,packedOpSnippet:t,cpuKernelImpl:n,dtype:a}){return({inputs:r,backend:s})=>{let{x:i}=r,o=s,u=a||i.dtype;if(o.shouldExecuteOnCPU([i])&&n!=null){let p=o.texData.get(i.dataId),c=n(p.values,u);return o.makeTensorInfo(i.shape,u,c)}let l=te().getBool("WEBGL_PACK_UNARY_OPERATIONS")&&t!=null,d;return l?d=new ql(i.shape,t):d=new Zr(i.shape,e),o.runWebGLProgram(d,[i],u)}}function rn({opSnippet:e,packedOpSnippet:t,checkOutOfBounds:n=!1,supportsComplex:a=!1,cpuKernelImpl:r,dtype:s}){return({inputs:i,backend:o})=>{let{a:u,b:l}=i,d=o;if(a&&u.dtype==="complex64"){let m=d.texData.get(u.dataId),f=d.texData.get(l.dataId),[A,y]=[[m.complexTensorInfos.real,f.complexTensorInfos.real],[m.complexTensorInfos.imag,f.complexTensorInfos.imag]].map(x=>{let[v,b]=x,w={dataId:v.dataId,dtype:v.dtype,shape:u.shape},N={dataId:b.dataId,dtype:b.dtype,shape:l.shape},C=new Kl(e,u.shape,l.shape);return d.runWebGLProgram(C,[w,N],fa(v.dtype,b.dtype))}),g=Yr({inputs:{real:A,imag:y},backend:d});return d.disposeIntermediateTensorInfo(A),d.disposeIntermediateTensorInfo(y),g}let p=s||fa(u.dtype,l.dtype);if((u.dtype==="string"||l.dtype==="string"||d.shouldExecuteOnCPU([u,l]))&&r!=null){let m=d.texData.get(u.dataId).values,f=d.texData.get(l.dataId).values,A=u.dtype==="string"?F.fromUint8ToStringArray(m):m,y=u.dtype==="string"?F.fromUint8ToStringArray(f):f,[g,x]=r(u.shape,l.shape,A,y,p),v=d.makeTensorInfo(x,p),b=d.texData.get(v.dataId);return b.values=g,v}let c=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")&&t!=null,h;return c?h=new $d(t,u.shape,l.shape,n):h=new Kl(e,u.shape,l.shape),d.runWebGLProgram(h,[u,l],p)}}function Uh(e,t=!1){if(e==="linear")return t?gV:hV;if(e==="relu")return t?bV:mV;if(e==="elu")return t?xV:fV;if(e==="relu6")return t?vV:AV;if(e==="prelu")return t?Cw:Tw;if(e==="leakyrelu")return t?Nw:Sw;if(e==="sigmoid")return t?wV:yV;throw new Error(`Activation ${e} has not been implemented for the WebGL backend.`)}var Rw=class{constructor(e,t,n,a=!1,r=!1,s=!1,i=null,o=!1,u=!1){this.variableNames=["matrixA","matrixB"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=n;let l=a?e[1]:e[2],d=Math.ceil(l/2),p=a?"i * 2, rc.y":"rc.y, i * 2",c=r?"rc.z, i * 2":"i * 2, rc.z",h=a?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],m=r?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"],f="",A="";i&&(o?f=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${i}
|
|
}`:u?f=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${i}
|
|
}`:f=`vec4 activation(vec4 x) {
|
|
${i}
|
|
}`,A="result = activation(result);");let y=s?"result += getBiasAtOutCoords();":"";s&&this.variableNames.push("bias"),o&&this.variableNames.push("preluActivationWeights"),u&&this.variableNames.push("leakyreluAlpha");let g="rc.x",x="rc.x";e[0]<t[0]?g=`int(min(float(rc.x), ${e[0]-1}.))`:t[0]<e[0]&&(x=`int(min(float(rc.x), ${t[0]-1}.))`),this.userCode=`
|
|
${f}
|
|
|
|
const float sharedDimension = ${d}.0;
|
|
|
|
vec4 dot2x2ARowBCol(ivec3 rc) {
|
|
vec4 result = vec4(0);
|
|
for (int i = 0; i < ${d}; i++) {
|
|
int batchA = ${g};
|
|
int batchB = ${x};
|
|
vec4 a = getMatrixA(batchA, ${p});
|
|
vec4 b = getMatrixB(batchB, ${c});
|
|
|
|
// These swizzled products need to be separately added.
|
|
// See: https://github.com/tensorflow/tfjs/issues/1735
|
|
result += (${h[0]} * ${m[0]});
|
|
result += (${h[1]} * ${m[1]});
|
|
}
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec3 rc = getOutputCoords();
|
|
vec4 result = dot2x2ARowBCol(rc);
|
|
|
|
${y}
|
|
|
|
${A}
|
|
|
|
setOutput(result);
|
|
}
|
|
`}},Mw={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},Fw=class{constructor(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.outputShape=F.assertAndGetBroadcastShape(t,n),this.userCode=`
|
|
float binaryOpComplex(
|
|
float areal, float aimag, float breal, float bimag) {
|
|
${e}
|
|
}
|
|
|
|
void main() {
|
|
float areal = getARealAtOutCoords();
|
|
float aimag = getAImagAtOutCoords();
|
|
float breal = getBRealAtOutCoords();
|
|
float bimag = getBImagAtOutCoords();
|
|
setOutput(binaryOpComplex(areal, aimag, breal, bimag));
|
|
}
|
|
`}},$w="return a * b;";function GA(e){let{inputs:t,backend:n}=e,{a,b:r}=t,s=F.upcastType(a.dtype,r.dtype);if(a.dtype==="complex64"){let o=n.texData.get(a.dataId),u=n.texData.get(r.dataId),l=new Fw(Mw.REAL,a.shape,r.shape),d=new Fw(Mw.IMAG,a.shape,r.shape),p=[{dataId:o.complexTensorInfos.real.dataId,dtype:o.complexTensorInfos.real.dtype,shape:a.shape},{dataId:o.complexTensorInfos.imag.dataId,dtype:o.complexTensorInfos.imag.dtype,shape:a.shape},{dataId:u.complexTensorInfos.real.dataId,dtype:u.complexTensorInfos.real.dtype,shape:r.shape},{dataId:u.complexTensorInfos.imag.dataId,dtype:u.complexTensorInfos.imag.dtype,shape:r.shape}],c=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=Yr({inputs:{real:c,imag:h},backend:n});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}if(n.shouldExecuteOnCPU([a,r])){let o=n.texData.get(a.dataId),u=n.texData.get(r.dataId),[l,d]=LB(a.shape,r.shape,o.values,u.values,s),p=n.makeTensorInfo(d,s),c=n.texData.get(p.dataId);return c.values=l,p}let i;return te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?i=new $d($w,a.shape,r.shape):i=new Kl($w,a.shape,r.shape),n.runWebGLProgram(i,[a,r],s)}var BV={kernelName:Hs,backendName:"webgl",kernelFunc:GA};function VV(e,t,n){let a=[Oi(e.shape),...zi(e.shape)],r={dtype:e.dtype,shape:a,dataId:e.dataId},s=[Oi(t),...zi(t)],i=new yw(s,a),o=!0,u=n.runWebGLProgram(i,[r],e.dtype,null,o);return{dataId:u.dataId,shape:t,dtype:u.dtype}}function ye(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{shape:s}=a,i=n,o=k.sizeFromShape(r.shape),u=k.inferFromImplicitShape(s,o),l=k.sizeFromShape(u);k.assert(o===l,()=>`The new shape (${u}) has ${l} elements and the old shape (${r.shape}) has ${o} elements. The new shape and old shape must have the same number of elements.`);let d=i.texData.get(r.dataId);return d.isPacked&&!Md(r.shape,u)&&!(d.texture!==null&&Md(d.shape,u))?VV(r,u,i):(i.incRef(r.dataId),{dataId:r.dataId,shape:u,dtype:r.dtype})}var jV={kernelName:el,backendName:"webgl",kernelFunc:ye},Dw=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i=Math.floor(n/4)*4,o=n%4,u="sumValue += dot(values, ones);";if(t!=null){let d=1/t;u=`sumValue += dot(values * ${k.isInt(d)?d.toPrecision(2):d}, ones);`}let l="";r%n>0&&(l=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return 0.0;
|
|
}
|
|
`),this.userCode=`
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${l}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${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 + ${i};
|
|
if (${o===1}) {
|
|
vec4 values = vec4(getValue(batch, inIdx), 0.0, 0.0, 0.0);
|
|
|
|
${u}
|
|
} else if (${o===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1), 0.0, 0.0);
|
|
|
|
${u}
|
|
} else if (${o===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2), 0.0);
|
|
|
|
${u}
|
|
}
|
|
setOutput(sumValue);
|
|
}
|
|
`}},UV=class{constructor(e,t){this.variableNames=["x"];let{windowSize:n,batchSize:a,inSize:r,outSize:s}=e;this.outputShape=[a,s];let i="0.0",o="";t==="prod"?i="1.0":t==="min"?(i="1.0 / 1e-20",o="min"):t==="max"&&(i="-1.0 / 1e-20",o="max");let u=`${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 l=Math.floor(n/4)*4,d=n%4,p=`
|
|
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 = ${o}(values, minMaxValue);
|
|
if (${t==="min"} || ${t==="max"}) {
|
|
minMaxValue = ${o}(values, minMaxValue);
|
|
bvec4 isNaN = isnan(values);
|
|
if (isNaN.r || isNaN.g || isNaN.b || isNaN.a) {
|
|
minMaxValue = vec4(NAN);
|
|
}
|
|
}
|
|
}
|
|
`,c="vec4";t==="all"?(i="1.0",p=`
|
|
bool reducedAllValue = all(values);
|
|
float floatedReducedAllValue = float(reducedAllValue);
|
|
allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);
|
|
`,c="bvec4"):t==="any"&&(i="0.0",p=`
|
|
bool reducedAnyValue = any(values);
|
|
float floatedReducedAnyValue = float(reducedAnyValue);
|
|
anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);
|
|
`,c="bvec4");let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
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) {
|
|
${h}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${n};
|
|
|
|
vec4 minMaxValue = vec4(${i});
|
|
float prodValue = 1.0;
|
|
float sumValue = 0.0;
|
|
float allValue = 1.0;
|
|
float anyValue = 0.0;
|
|
|
|
for (int i = 0; i < ${l}; i += 4) {
|
|
int inIdx = inOffset + i;
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
getValue(batch, inIdx + 3)
|
|
);
|
|
|
|
${p}
|
|
}
|
|
|
|
int inIdx = inOffset + ${l};
|
|
if (${d===1}) {
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${p}
|
|
} else if (${d===2}) {
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${p}
|
|
} else if (${d===3}) {
|
|
${c} values = ${c}(
|
|
getValue(batch, inIdx),
|
|
getValue(batch, inIdx + 1),
|
|
getValue(batch, inIdx + 2),
|
|
initializationValue
|
|
);
|
|
|
|
${p}
|
|
}
|
|
setOutput(${u});
|
|
}
|
|
`}};function HV(e){let t=[];for(;t.length===0||t[t.length-1].outSize!==1;){let n=t.length?t[t.length-1].outSize:e[1],a=F.computeOptimalWindowSize(n);t.push({inSize:n,windowSize:a,outSize:Math.ceil(n/a)})}return t}function Wi(e,t,n,a){let r=HV(e.shape),s=e;for(let i=0;i<r.length;i++){let{inSize:o,windowSize:u,outSize:l}=r[i],d,p;n==="mean"?d=i===0?new Dw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},o):new Dw({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l}):d=new UV({windowSize:u,inSize:o,batchSize:e.shape[0],outSize:l},n),p=s,s=a.runWebGLProgram(d,[s],t),p.dataId!==e.dataId&&a.disposeIntermediateTensorInfo(p)}return s}var GV=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[t[s]];this.outputShape=n,this.rank=n.length;let a=ut(this.rank),r=qV(t);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function qV(e){let t=e.length;if(t>6)throw Error(`Transpose for rank ${t} is not yet supported`);let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],a=new Array(t);for(let r=0;r<e.length;r++)a[e[r]]=n[r];return a.join()}var XV=class{constructor(e,t){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0;let n=new Array(e.length);for(let l=0;l<n.length;l++)n[l]=e[t[l]];if(this.outputShape=n,this.rank=n.length,this.rank>6)throw Error(`Packed transpose for rank ${this.rank} is not yet supported.`);let a=ut(this.rank),r=Aw("rc",this.rank),s=new Array(this.rank);for(let l=0;l<t.length;l++)s[t[l]]=r[l];let i=`vec2(${s.slice(-2).join()})`,o=`++${r[this.rank-1]} < ${n[this.rank-1]}`,u=`getChannel(getA(${s.join()}), ${i})`;this.userCode=`
|
|
void main() {
|
|
${a} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result[0] = ${u};
|
|
if(${o}) {
|
|
result[1] = ${u};
|
|
}
|
|
--${r[this.rank-1]};
|
|
if(++${r[this.rank-2]} < ${n[this.rank-2]}) {
|
|
result[2] = ${u};
|
|
if(${o}) {
|
|
result[3] = ${u};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Hh(e,t,n){let a=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new XV(e.shape,t):new GV(e.shape,t);return n.runWebGLProgram(a,[e],e.dtype)}function KV(e,t,n,a){let r=t,s=e.shape.length,i=k.parseAxisParam(r,e.shape),o=i,u=F.getAxesPermutation(o,s),l=u!=null,d=e;l&&(d=Hh(e,u,a),o=F.getInnerMostAxes(o.length,s)),F.assertAxesAreInnerMostDims("sum",o,s);let[p,c]=F.computeOutAndReduceShapes(d.shape,o),h=p;n&&(h=F.expandShapeToKeepDim(p,i));let m=k.sizeFromShape(c),f=k.sizeFromShape(e.shape)/m,A=ye({inputs:{x:d},attrs:{shape:[f,m]},backend:a}),y=_c(e.dtype),g=Wi(A,y,"sum",a),x=ye({inputs:{x:g},attrs:{shape:h},backend:a});return a.disposeIntermediateTensorInfo(A),a.disposeIntermediateTensorInfo(g),l&&a.disposeIntermediateTensorInfo(d),x}function Gh(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a;return KV(r,s,i,n)}var ZV={kernelName:si,backendName:"webgl",kernelFunc:Gh};function xn(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{perm:s}=a,i=n,o=r.shape.length,u=new Array(o);for(let d=0;d<u.length;d++)u[d]=r.shape[s[d]];let l;if(i.shouldExecuteOnCPU([r])){let d=i.texData.get(r.dataId).values,p=UA(d,r.shape,r.dtype,s,u);l=i.makeTensorInfo(u,r.dtype);let c=i.texData.get(l.dataId);c.values=p}else l=Hh(r,s,i);return l}var YV={kernelName:pi,backendName:"webgl",kernelFunc:xn},_w=1e3;function qh({a:e,b:t,transposeA:n,transposeB:a,backend:r,bias:s=null,preluActivationWeights:i=null,leakyreluAlpha:o=0,activation:u=null}){let l=e.shape.length,d=t.shape.length,p=n?e.shape[l-2]:e.shape[l-1],c=a?t.shape[d-1]:t.shape[d-2],h=n?e.shape[l-1]:e.shape[l-2],m=a?t.shape[d-2]:t.shape[d-1],f=e.shape.slice(0,-2),A=t.shape.slice(0,-2),y=k.sizeFromShape(f),g=k.sizeFromShape(A),x=y===g||y===1||g===1;k.assert(l>=2&&d>=2&&x,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${f}) and (${A}).`);let v=(y>g?e.shape.slice(0,-2):t.shape.slice(0,-2)).concat([h,m]);k.assert(p===c,()=>`Error in matMul: inner shapes (${p}) and (${c}) of Tensors with shapes ${e.shape} and ${t.shape} and transposeA=${n} and transposeB=${a} must match.`);let b=n?[y,p,h]:[y,h,p],w=a?[g,m,c]:[g,c,m],N=ye({inputs:{x:e},backend:r,attrs:{shape:b}}),C=ye({inputs:{x:t},backend:r,attrs:{shape:w}}),E=[N,C],z=Math.max(y,g),$=n?N.shape[1]:N.shape[2],S=s!=null,O=i!=null,_=u==="leakyrelu",W=u!=null?Uh(u,!0):null,G=S||O||_||W!=null,H;if((h===1||m===1)&&$>_w&&G===!1){let K=N,ne=C;n&&(K=xn({inputs:{x:N},backend:r,attrs:{perm:[0,2,1]}}),E.push(K)),a&&(ne=xn({inputs:{x:C},backend:r,attrs:{perm:[0,2,1]}}),E.push(ne));let Q=m!==1,se=m===1,Z=K;Q&&(Z=ye({inputs:{x:K},backend:r,attrs:{shape:[z,$,1]}}),E.push(Z));let le=m===1?2:1,oe=ne;se&&(oe=ye({inputs:{x:ne},backend:r,attrs:{shape:[z,1,$]}}),E.push(oe));let ge=GA({inputs:{a:Z,b:oe},backend:r});H=Gh({inputs:{x:ge},backend:r,attrs:{axis:le,keepDims:!0}}),E.push(ge)}else{let K=fa(e.dtype,t.dtype),ne=new Rw(b,w,[z,h,m],n,a,S,W,O,_),Q=[N,C];if(s!=null&&Q.push(s),O&&Q.push(i),_){let se=r.makeTensorInfo([],"float32",k.createScalarValue(o,"float32"));Q.push(se),E.push(se)}H=r.runWebGLProgram(ne,Q,K)}let J=ye({inputs:{x:H},backend:r,attrs:{shape:v}});E.push(H);for(let K of E)r.disposeIntermediateTensorInfo(K);return J}function JV(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t,{transposeA:u,transposeB:l,activation:d,leakyreluAlpha:p}=a;return qh({a:r,b:s,transposeA:u,transposeB:l,backend:n,bias:i,preluActivationWeights:o,leakyreluAlpha:p,activation:d})}var QV={kernelName:ci,backendName:"webgl",kernelFunc:JV},Ow="return abs(x);";function ej(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])&&a.dtype!=="complex64"){let s=n.texData.get(a.dataId),i=fw(s.values);return n.makeTensorInfo(a.shape,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ql(a.shape,Ow):r=new Zr(a.shape,Ow),n.runWebGLProgram(r,[a],a.dtype)}var tj={kernelName:fo,backendName:"webgl",kernelFunc:ej},nj=Na+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return acos(x);
|
|
`,aj=Ke({opSnippet:nj}),rj={kernelName:mo,backendName:"webgl",kernelFunc:aj},sj=Na+`
|
|
if (x < 1.0) return NAN;
|
|
return log(x + sqrt(x * x - 1.0));`,ij=Ke({opSnippet:sj}),oj={kernelName:Ao,backendName:"webgl",kernelFunc:ij},zw="return a + b;",lj=rn({opSnippet:zw,packedOpSnippet:zw,supportsComplex:!0,cpuKernelImpl:bB}),uj={kernelName:Dr,backendName:"webgl",kernelFunc:lj},dj=class{constructor(e,t){this.outputShape=[],this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`float v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
float result = ${a};
|
|
setOutput(result);
|
|
}
|
|
`}},pj=class{constructor(e,t){this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.variableNames=t.map((r,s)=>`T${s}`);let n=[];this.variableNames.forEach(r=>{n.push(`vec4 v${r} = get${r}AtOutCoords();`)});let a=this.variableNames.map(r=>`v${r}`).join(" + ");this.userCode=`
|
|
void main() {
|
|
${n.join(`
|
|
`)}
|
|
|
|
vec4 result = ${a};
|
|
setOutput(result);
|
|
}
|
|
`}};function Xh(e){let{inputs:t,backend:n}=e,a=t;if(a.length===1)return Gn({inputs:{x:a[0]},backend:n});if(a.length>te().get("WEBGL_MAX_TEXTURES_IN_SHADER")){let o=Math.floor(a.length/2),u=Xh({inputs:a.slice(0,o),backend:n}),l=Xh({inputs:a.slice(o),backend:n});return Xh({inputs:[u,l],backend:n})}let r=a.map(o=>o.dtype).reduce((o,u)=>fa(o,u)),s=a.map(o=>o.shape),i=te().getBool("WEBGL_PACK")?new pj(a[0].shape,s):new dj(a[0].shape,s);return n.runWebGLProgram(i,a,r)}var cj={kernelName:gs,backendName:"webgl",kernelFunc:Xh};function hj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=u,d=F.getAxesPermutation(l,o),p=r;d!=null&&(p=xn({inputs:{x:r},backend:n,attrs:{perm:d}}),l=F.getInnerMostAxes(l.length,o)),F.assertAxesAreInnerMostDims("all",l,o);let[c,h]=F.computeOutAndReduceShapes(p.shape,l),m=k.sizeFromShape(h),f=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),A=Wi(f,f.dtype,"all",n),y;if(i){let g=F.expandShapeToKeepDim(c,u);y=ye({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ye({inputs:{x:A},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),d!=null&&n.disposeIntermediateTensorInfo(p),y}var fj={kernelName:yo,backendName:"webgl",kernelFunc:hj};function mj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=u,d=F.getAxesPermutation(l,o),p=r;d!=null&&(p=xn({inputs:{x:r},backend:n,attrs:{perm:d}}),l=F.getInnerMostAxes(l.length,o)),F.assertAxesAreInnerMostDims("any",l,o);let[c,h]=F.computeOutAndReduceShapes(p.shape,l),m=k.sizeFromShape(h),f=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),A=Wi(f,f.dtype,"any",n),y;if(i){let g=F.expandShapeToKeepDim(c,u);y=ye({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ye({inputs:{x:A},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),d!=null&&n.disposeIntermediateTensorInfo(p),y}var Aj={kernelName:go,backendName:"webgl",kernelFunc:mj},yj=class{constructor(e,t,n){this.variableNames=["A"];let{windowSize:a,batchSize:r,outSize:s}=e;n||this.variableNames.push("bestIndicesA"),this.outputShape=[r,s];let i=t==="max"?">":"<",o=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = outIdx * ${a};
|
|
|
|
int bestIndex = inOffset;
|
|
float bestValue = getA(batch, bestIndex);
|
|
|
|
for (int i = 0; i < ${a}; i++) {
|
|
int inIdx = ${o};
|
|
float candidate = getA(batch, inIdx);
|
|
if (candidate ${i} bestValue) {
|
|
bestValue = candidate;
|
|
bestIndex = inIdx;
|
|
}
|
|
}
|
|
setOutput(float(bestIndex));
|
|
}
|
|
`}},gj=class{constructor(e,t,n,a){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,k.assert(e.length>2,()=>`Packed arg${n.charAt(0).toUpperCase()+n.slice(1)} supports only inputs with rank above 2.`);let r=e[e.length-1],s=Math.ceil(r/t);this.outputShape=e.slice(0,-1),s>1&&this.outputShape.push(s),a||this.variableNames.push("bestIndicesA");let i=this.outputShape,o=i.length,u=ut(o),l=gn("coords",o),d,p;if(s===1){p=o+1;let N=ut(p);d=`
|
|
${N} sourceLocR = ${N}(${l.join()}, 0);
|
|
++${l[o-1]};
|
|
${N} sourceLocG = ${N}(${l.join()}, 0);
|
|
++${l[o-2]};
|
|
${N} sourceLocA = ${N}(${l.join()}, 0);
|
|
--${l[o-1]};
|
|
${N} sourceLocB = ${N}(${l.join()}, 0);
|
|
--${l[o-2]};`}else p=o,d=`
|
|
${u} sourceLocR = coords;
|
|
++${l[o-1]};
|
|
${u} sourceLocG = coords;
|
|
++${l[o-2]};
|
|
${u} sourceLocA = coords;
|
|
--${l[o-1]};
|
|
${u} sourceLocB = coords;
|
|
--${l[o-2]};`;let c=["x","y","z","w","u","v"].slice(0,p),h="."+c[p-1],m=c.map(N=>"int "+N),f=gn("sourceLocR",p-1).concat("inIdx.r"),A=gn("sourceLocG",p-1).concat("inIdx.g"),y=gn("sourceLocB",p-1).concat("inIdx.b"),g=gn("sourceLocA",p-1).concat("inIdx.a"),x=n==="max"?"greaterThan":"lessThan",v=a?"":`
|
|
inIdx = round(vec4(getBestIndicesAChannel(${f.join()}),
|
|
getBestIndicesAChannel(${A.join()}),
|
|
getBestIndicesAChannel(${y.join()}),
|
|
getBestIndicesAChannel(${g.join()})));`,b=`vec4(
|
|
getAChannel(${f.join()}),
|
|
hasNextCol ? getAChannel(${A.join()}) : 0.,
|
|
hasNextRow ? getAChannel(${y.join()}) : 0.,
|
|
hasNextRow && hasNextCol ? getAChannel(${g.join()}) : 0.)`,w=a?"":`
|
|
float getBestIndicesAChannel(${m.join()}) {
|
|
return getChannel(getBestIndicesA(${c.join()}),
|
|
vec2(${c.slice(-2).join()}));
|
|
}`;this.userCode=`
|
|
float getAChannel(${m.join()}) {
|
|
return getChannel(getA(${c.join()}),
|
|
vec2(${c.slice(-2).join()}));
|
|
}
|
|
${w}
|
|
void main() {
|
|
${u} coords = getOutputCoords();
|
|
bool hasNextCol = ${l[o-1]} < ${i[o-1]-1};
|
|
bool hasNextRow = ${l[o-2]} < ${i[o-2]-1};
|
|
${d}
|
|
ivec4 srcIdx = ivec4(sourceLocR${h}, sourceLocG${h},
|
|
sourceLocB${h}, sourceLocA${h}) * ${t};
|
|
ivec4 inIdx = srcIdx;
|
|
vec4 bestIndex = vec4(inIdx);
|
|
vec4 bestValue = ${b};
|
|
|
|
for (int i = 0; i < ${t}; i++) {
|
|
inIdx = srcIdx;
|
|
${v}
|
|
vec4 candidate = ${b};
|
|
bvec4 nan = isnan(candidate);
|
|
bvec4 replace = bvec4(
|
|
vec4(${x}(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 Pw(e,t,n,a=null){let r=t.shape[0],s=t.shape[1];a!=null&&(r=a.shape[0],s=a.shape[1]);let i=F.computeOptimalWindowSize(s),o={windowSize:i,inSize:s,batchSize:r,outSize:Math.ceil(s/i)},u=new yj(o,n,a==null),l=[t];a!=null&&l.push(a);let d=e.runWebGLProgram(u,l,"int32");if(d.shape[1]===1)return d;let p=Pw(e,t,n,d);return e.disposeIntermediateTensorInfo(d),p}function Lw(e,t,n,a=null){let r=a!=null?a.shape:t.shape,s=r[r.length-1],i=F.computeOptimalWindowSize(s),o=new gj(r,i,n,a==null),u=a==null?[t]:[t,a],l=e.runWebGLProgram(o,u,"int32");if(l.shape.length===t.shape.length){let d=Lw(e,t,n,l);return e.disposeIntermediateTensorInfo(l),d}return l}function Ww(e,t,n,a){let r=[n];if(F.assertAxesAreInnerMostDims("arg"+a.charAt(0).toUpperCase()+a.slice(1),r,t.shape.length),!te().getBool("WEBGL_PACK_REDUCE")||t.shape.length<=2){let s=[],[i,o]=F.computeOutAndReduceShapes(t.shape,r),u=k.sizeFromShape(o),l=ye({inputs:{x:t},backend:e,attrs:{shape:[-1,u]}});s.push(l);let d=Pw(e,l,a);s.push(d);let p=ye({inputs:{x:d},backend:e,attrs:{shape:i}});return s.forEach(c=>e.disposeIntermediateTensorInfo(c)),p}return Lw(e,t,a)}function xj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=xn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=F.getInnerMostAxes(i.length,u.shape.length)),F.assertAxesAreInnerMostDims("argMax",[i[0]],u.shape.length);let d=Ww(n,u,i[0],"max");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var bj={kernelName:xs,backendName:"webgl",kernelFunc:xj};function vj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s}=a,i=k.parseAxisParam(s,r.shape),o=F.getAxesPermutation(i,r.shape.length),u=r,l=[];o!=null&&(u=xn({inputs:{x:r},backend:n,attrs:{perm:o}}),l.push(u),i=F.getInnerMostAxes(i.length,u.shape.length)),F.assertAxesAreInnerMostDims("argMin",[i[0]],u.shape.length);let d=Ww(n,u,i[0],"min");return l.forEach(p=>n.disposeIntermediateTensorInfo(p)),d}var wj={kernelName:Mu,backendName:"webgl",kernelFunc:vj},kj=Na+`
|
|
if (abs(x) > 1.) {
|
|
return NAN;
|
|
}
|
|
return asin(x);
|
|
`,Ij=Ke({opSnippet:kj}),Sj={kernelName:xo,backendName:"webgl",kernelFunc:Ij},Nj=Na+"return log(x + sqrt(x * x + 1.0));",Tj=Ke({opSnippet:Nj}),Cj={kernelName:bo,backendName:"webgl",kernelFunc:Tj},Ej=Na+`
|
|
return atan(x);
|
|
`,Rj=Ke({opSnippet:Ej}),Mj={kernelName:vo,backendName:"webgl",kernelFunc:Rj},Fj=LV+`
|
|
return atan(a, b);
|
|
`,$j=`
|
|
vec4 result = atan(a, b);
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+WV+`
|
|
return result;
|
|
`,Dj=rn({opSnippet:Fj,packedOpSnippet:$j}),_j={kernelName:ko,backendName:"webgl",kernelFunc:Dj},Oj=Na+`
|
|
if ((x < -1.0) || (x > 1.0)) return NAN;
|
|
return (log(1.0 + x) - log(1.0 - x)) / 2.0;`,zj=Ke({opSnippet:Oj}),Pj={kernelName:wo,backendName:"webgl",kernelFunc:zj},Dd=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideHeight,o=e.strideWidth,u=e.dilationHeight,l=e.dilationWidth,d=e.effectiveFilterHeight,p=e.effectiveFilterWidth,c=e.padInfo.top,h=e.padInfo.left;this.outputShape=e.outShape;let m=t==="avg",f=`((batch * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + d`,A=`(xR * ${e.inWidth} + xC) * ${e.inChannels} + d`,y="0.0";if(m||(y="-1.0 / 1e-20"),n){let N=">=";this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${c}, ${h});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
float avgValue = 0.0;
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${p};
|
|
wC += ${l}) {
|
|
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 ${N} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${a?r?f:A:`wR * ${p} + wC`};
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let g="max",x=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(x="avgValue / count");let v=Math.floor(s/4)*4,b=s%4,w=`
|
|
if (${m}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${g}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec2 strides = ivec2(${i}, ${o});
|
|
const ivec2 pads = ivec2(${c}, ${h});
|
|
const float initializationValue = ${y};
|
|
const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);
|
|
|
|
float count = 0.0;
|
|
|
|
float getValue(int batch, int xR, int xC, int d) {
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xR, xC, d);
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// max/min x(?, ?, d) to get y(yR, yC, d).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${y});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wR = 0; wR < ${d};
|
|
wR += ${u}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${v}; wC += 4) {
|
|
int xC = xCCorner + wC * ${l};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${l}, d),
|
|
getValue(batch, xR, xC + 2 * ${l}, d),
|
|
getValue(batch, xR, xC + 3 * ${l}, d)
|
|
);
|
|
|
|
${w}
|
|
}
|
|
|
|
int xC = xCCorner + ${v};
|
|
if (${b===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${w}
|
|
} else if (${b===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${l}, d),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${w}
|
|
} else if (${b===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xR, xC, d),
|
|
getValue(batch, xR, xC + ${l}, d),
|
|
getValue(batch, xR, xC + 2 * ${l}, d),
|
|
initializationValue
|
|
);
|
|
|
|
${w}
|
|
}
|
|
}
|
|
setOutput(${x});
|
|
}
|
|
`}},qA=class{constructor(e,t,n,a=!1,r=!1){if(this.variableNames=["x"],t==="avg"&&n)throw new Error("Cannot compute positions for average pool.");let s=e.filterWidth,i=e.strideDepth,o=e.strideHeight,u=e.strideWidth,l=e.dilationDepth,d=e.dilationHeight,p=e.dilationWidth,c=e.effectiveFilterDepth,h=e.effectiveFilterHeight,m=e.effectiveFilterWidth,f=e.padInfo.front,A=e.padInfo.top,y=e.padInfo.left;this.outputShape=e.outShape;let g=t==="avg",x="0.0";if(g||(x="-1.0 / 1e-20"),n){let E=">=";this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${u});
|
|
const ivec3 pads = ivec3(${f}, ${A}, ${y});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, ch) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
float minMaxValue = 0.0;
|
|
float minMaxValueFound = 0.0;
|
|
int minMaxPosition = 0;
|
|
|
|
for (int wD = 0; wD < ${c};
|
|
wD += ${l}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${d}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${m};
|
|
wC += ${p}) {
|
|
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 ${E} currMinMaxValue) {
|
|
minMaxValue = value;
|
|
minMaxValueFound = 1.0;
|
|
minMaxPosition = ${a?r?`(((batch * ${e.inDepth} + xD) * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`((xD * ${e.inHeight} + xR) * ${e.inWidth} + xC) * ${e.inChannels} + ch`:`wD * ${h} * ${m} +
|
|
wR * ${m} + wC`};
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(float(minMaxPosition));
|
|
}
|
|
`;return}let v="max",b=`${t}(${t}(${t}(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])`;t==="avg"&&(b="avgValue / count");let w=Math.floor(s/4)*4,N=s%4,C=`
|
|
if (${g}) {
|
|
avgValue += dot(values, ones);
|
|
} else {
|
|
minMaxValue = ${v}(values, minMaxValue);
|
|
}
|
|
`;this.userCode=`
|
|
const ivec3 strides =
|
|
ivec3(${i}, ${o}, ${u});
|
|
const ivec3 pads = ivec3(${f}, ${A}, ${y});
|
|
const float initializationValue = ${x};
|
|
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 >= ${e.inWidth}) {
|
|
return initializationValue;
|
|
}
|
|
count += 1.0;
|
|
return getX(batch, xD, xR, xC, ch);
|
|
}
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int ch = coords.u;
|
|
|
|
ivec3 xCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;
|
|
int xDCorner = xCorner.x;
|
|
int xRCorner = xCorner.y;
|
|
int xCCorner = xCorner.z;
|
|
|
|
// max/min x(?, ?, ?, d) to get y(yD, yR, yC, ch).
|
|
// ? = to be determined
|
|
vec4 minMaxValue = vec4(${x});
|
|
float avgValue = 0.0;
|
|
count = 0.0;
|
|
|
|
for (int wD = 0; wD < ${c};
|
|
wD += ${l}) {
|
|
int xD = xDCorner + wD;
|
|
|
|
if (xD < 0 || xD >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${h};
|
|
wR += ${d}) {
|
|
int xR = xRCorner + wR;
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${w}; wC += 4) {
|
|
int xC = xCCorner + wC * ${p};
|
|
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${p}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
|
|
getValue(batch, xD, xR, xC + 3 * ${p}, ch)
|
|
);
|
|
|
|
${C}
|
|
}
|
|
|
|
int xC = xCCorner + ${w};
|
|
if (${N===1}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
initializationValue,
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${N===2}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${p}, ch),
|
|
initializationValue,
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
} else if (${N===3}) {
|
|
vec4 values = vec4(
|
|
getValue(batch, xD, xR, xC, ch),
|
|
getValue(batch, xD, xR, xC + ${p}, ch),
|
|
getValue(batch, xD, xR, xC + 2 * ${p}, ch),
|
|
initializationValue
|
|
);
|
|
|
|
${C}
|
|
}
|
|
}
|
|
setOutput(${b});
|
|
}
|
|
}
|
|
`}};function Lj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Vl(r,"avgPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:u}=a,l=1;k.assert(F.eitherStridesOrDilationsAreOne(i,l),()=>`Error in avgPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let d=F.computePool2DInfo(r.shape,s,i,l,o,u);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return Gn({inputs:{x:r},backend:n});let p=new Dd(d,"avg",!1);return n.runWebGLProgram(p,[r],"float32")}var Wj={kernelName:bs,backendName:"webgl",kernelFunc:Lj};function Bj(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dimRoundingMode:u,dataFormat:l}=a,d=[1,1,1],p=F.computePool3DInfo(r.shape,s,i,d,o,u,l),c=new qA(p,"avg",!1);return n.runWebGLProgram(c,[r],"float32")}var Vj={kernelName:Fu,backendName:"webgl",kernelFunc:Bj},jj=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=o-1-e.padInfo.top,d=u-1-e.padInfo.left,p=1/(t*n);this.userCode=`
|
|
const ivec2 pads = ivec2(${l}, ${d});
|
|
const float avgMultiplier = float(${p});
|
|
|
|
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 < ${o};
|
|
wR += ${s}) {
|
|
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 < ${u};
|
|
wC+= ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
|
|
dotProd += dyValue * avgMultiplier;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Uj=class{constructor(e){this.variableNames=["dy"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,d=e.effectiveFilterDepth,p=e.effectiveFilterHeight,c=e.effectiveFilterWidth,h=d-1-e.padInfo.front,m=p-1-e.padInfo.top,f=c-1-e.padInfo.left,A=1/(t*n*a);this.userCode=`
|
|
const ivec3 pads = ivec3(${h}, ${m}, ${f});
|
|
const float avgMultiplier = float(${A});
|
|
|
|
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 < ${d};
|
|
wD += ${o}) {
|
|
float dyD = float(dyDCorner + wD) / ${r}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${p};
|
|
wR += ${u}) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
for (int wC = 0; wC < ${c};
|
|
wC += ${l}) {
|
|
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 Hj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:u,pad:l,dimRoundingMode:d}=a,p=[1,1,1],c=F.computePool3DInfo(i.shape,o,u,p,l,d),h=new Uj(c);return n.runWebGLProgram(h,[r],i.dtype)}var Gj={kernelName:Xp,backendName:"webgl",kernelFunc:Hj};function qj(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s;Vl([r,s],"avgPoolGrad");let{filterSize:o,strides:u,pad:l}=a,d=F.computePool2DInfo(i.shape,o,u,1,l),p=new jj(d);return n.runWebGLProgram(p,[r],i.dtype)}var Xj={kernelName:qp,backendName:"webgl",kernelFunc:qj};function Kj(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;return qh({a:r,b:s,transposeA:i,transposeB:o,backend:n})}var Zj={kernelName:vs,backendName:"webgl",kernelFunc:Kj},Yj=class{constructor(e,t,n,a,r,s){this.outputShape=[],this.variableNames=["x","mean","variance"],F.assertAndGetBroadcastShape(e,t),F.assertAndGetBroadcastShape(e,n);let i="0.0";a!=null&&(F.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="1.0";r!=null&&(F.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
float x = getXAtOutCoords();
|
|
float mean = getMeanAtOutCoords();
|
|
float variance = getVarianceAtOutCoords();
|
|
float offset = ${i};
|
|
float scale = ${o};
|
|
float inv = scale * inversesqrt(variance + float(${s}));
|
|
setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));
|
|
}
|
|
`}},Jj=class{constructor(e,t,n,a,r,s){this.packedInputs=!0,this.packedOutput=!0,this.variableNames=["x","mean","variance"],F.assertAndGetBroadcastShape(e,t),F.assertAndGetBroadcastShape(e,n);let i="vec4(0.0)";a!=null&&(F.assertAndGetBroadcastShape(e,a),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");let o="vec4(1.0)";r!=null&&(F.assertAndGetBroadcastShape(e,r),this.variableNames.push("scale"),o="getScaleAtOutCoords()"),this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
vec4 offset = ${i};
|
|
vec4 scale = ${o};
|
|
|
|
vec4 x = getXAtOutCoords();
|
|
vec4 mean = getMeanAtOutCoords();
|
|
vec4 variance = getVarianceAtOutCoords();
|
|
|
|
vec4 inv = scale * inversesqrt(variance + vec4(${s}));
|
|
|
|
setOutput((x - mean) * inv + offset);
|
|
}
|
|
`}},Qj=({inputs:e,backend:t,attrs:n})=>{let{x:a,mean:r,variance:s,offset:i,scale:o}=e;k.assert(r.shape.length===s.shape.length,()=>"Batch normalization gradient requires mean and variance to have equal ranks."),k.assert(i==null||r.shape.length===i.shape.length,()=>"Batch normalization gradient requires mean and offset to have equal ranks."),k.assert(o==null||r.shape.length===o.shape.length,()=>"Batch normalization gradient requires mean and scale to have equal ranks.");let{varianceEpsilon:u}=n;u==null&&(u=.001);let l=[a,r,s],d=null;i!=null&&(d=i.shape,l.push(i));let p=null;o!=null&&(p=o.shape,l.push(o));let c=te().getBool("WEBGL_PACK_NORMALIZATION")?new Jj(a.shape,r.shape,s.shape,d,p,u):new Yj(a.shape,r.shape,s.shape,d,p,u);return t.runWebGLProgram(c,l,l[0].dtype)},eU={kernelName:$s,backendName:"webgl",kernelFunc:Qj},tU=class{constructor(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=`uniform int start[${this.rank}];`,a=nU(this.rank),r,s=e.map((i,o)=>`sourceLoc.${XA[o]} = start[${o}] + coords.${XA[o]};`);r=`
|
|
${t} sourceLoc;
|
|
${t} coords = getOutputCoords();
|
|
${s.join(`
|
|
`)}
|
|
`,this.userCode=`
|
|
${n}
|
|
void main() {
|
|
${r}
|
|
setOutput(getSource(${a}));
|
|
}
|
|
`}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,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}},XA=["x","y","z","w","u","v"];function nU(e){if(e===1)return"sourceLoc";if(e<=6)return XA.slice(0,e).map(t=>"sourceLoc."+t).join(",");throw Error(`Slicing for rank ${e} is not yet supported`)}var aU=class{constructor(e){this.variableNames=["source"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e,this.rank=e.length;let t=ut(this.rank),n=gn("coords",this.rank),a=gn("sourceLoc",this.rank),r=this.rank===1?"sourceLoc":`vec2(${a.slice(-2).join()})`,s=`getChannel(getSource(${a.join()}), ${r})`,i=`
|
|
result.x = ${s};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${a[this.rank-1]};
|
|
result.y = ${s};
|
|
--${a[this.rank-1]};
|
|
}
|
|
`,o=this.rank===1?"":`
|
|
--${n[this.rank-1]};
|
|
if (++${n[this.rank-2]} < ${e[this.rank-2]}) {
|
|
++${a[this.rank-2]};
|
|
result.z = ${s};
|
|
if (++${n[this.rank-1]} < ${e[this.rank-1]}) {
|
|
++${a[this.rank-1]};
|
|
result.w = ${s};
|
|
}
|
|
}
|
|
`,u=this.rank<=4?`sourceLoc = coords +
|
|
${t}(${e.map((l,d)=>`start[${d}]`).join()});`:e.map((l,d)=>`${a[d]} = ${n[d]} + start[${d}];`).join(`
|
|
`);this.userCode=`
|
|
uniform int start[${this.rank}];
|
|
void main() {
|
|
${t} coords = getOutputCoords();
|
|
${t} sourceLoc;
|
|
${u}
|
|
vec4 result = vec4(0.);
|
|
${i}
|
|
${o}
|
|
setOutput(result);
|
|
}
|
|
`}getCustomSetupFunc(e){if(e.length!==this.rank)throw Error(`The rank (${this.rank}) of the program must match the length of start (${e.length})`);return(t,n)=>{this.startLoc==null&&(this.startLoc=t.getUniformLocationNoThrow(n,"start"),this.startLoc==null)||t.gl.uniform1iv(this.startLoc,e)}}};function rU(e,t,n,a){let r=a.texData.get(e.dataId),s=a.makeTensorInfo(n,e.dtype),i=a.texData.get(s.dataId);Object.assign(i,r),i.refCount=1,i.shape=n,i.dtype=e.dtype;let o=hn.computeFlatOffset(t,k.computeStrides(e.shape));r.slice&&(o+=r.slice.flatOffset),i.slice={flatOffset:o,origDataId:r.slice&&r.slice.origDataId||e.dataId};let u=a.dataRefCount.get(i.slice.origDataId)||1;return a.dataRefCount.set(i.slice.origDataId,u+1),s}function _d(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,size:i}=a,[o,u]=hn.parseSliceParams(r,s,i);if(hn.assertParamsValid(r,o,u),k.sizeFromShape(u)===0)return n.makeTensorInfo(u,r.dtype,[]);if(n.shouldExecuteOnCPU([r])||r.dtype==="string"){let p=n.texData.get(r.dataId),c=HB(p.values,o,u,r.shape,r.dtype);return n.makeTensorInfo(u,r.dtype,c)}let{isPacked:l}=n.texData.get(r.dataId),d=hn.isSliceContinous(r.shape,o,u);if(l||!d){let p=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new aU(u):new tU(u),c=p.getCustomSetupFunc(o);return n.runWebGLProgram(p,[r],r.dtype,c)}return n.uploadToGPU(r.dataId),rU(r,o,u,n)}var sU={kernelName:rl,backendName:"webgl",kernelFunc:_d},iU=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,crops:i}=a;k.assert(r.shape.length<=4,()=>"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((g,x)=>g*x),u=F.getReshaped(r.shape,s,o),l=F.getPermuted(u.length,s.length),d=F.getReshapedPermuted(r.shape,s,o),p=F.getSliceBeginCoords(i,s.length),c=F.getSliceSize(d,i,s.length),h=[],m=ye({inputs:{x:r},backend:n,attrs:{shape:u}}),f=xn({inputs:{x:m},backend:n,attrs:{perm:l}}),A=ye({inputs:{x:f},backend:n,attrs:{shape:d}}),y=_d({inputs:{x:A},backend:n,attrs:{begin:p,size:c}});return h.push(m),h.push(f),h.push(A),h.forEach(g=>n.disposeIntermediateTensorInfo(g)),y},oU={kernelName:$u,backendName:"webgl",kernelFunc:iU};function lU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i}=a,o=n.readSync(r.dataId),u=n.readSync(s.dataId),l=hw(o,u,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,l)}var uU={kernelName:Kp,backendName:"webgl",kernelFunc:lU},dU="return float(a != b);",Bw=rn({opSnippet:dU,cpuKernelImpl:BB,dtype:"bool"}),pU={kernelName:Go,backendName:"webgl",kernelFunc:Bw};function Od(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Gn({inputs:{x:r.complexTensorInfos.real},backend:n})}var cU={kernelName:yc,backendName:"webgl",kernelFunc:Od},hU="return float(int(x));";function fU(e,t){let n=new Zr(e.shape,hU),a=t.runWebGLProgram(n,[e],"int32");return{dataId:a.dataId,shape:a.shape,dtype:a.dtype}}function KA(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dtype:s}=a;if(s==="complex64"){if(r.dtype==="complex64")return Gn({inputs:{x:r},backend:n});let i=$t(r.shape),o=KA({inputs:{x:r},backend:n,attrs:{dtype:"float32"}}),u=Yr({inputs:{real:o,imag:i},backend:n});return i.dispose(),n.disposeIntermediateTensorInfo(o),u}if(r.dtype==="complex64"){let i=Od({inputs:{input:r},backend:n}),o=KA({inputs:{x:i},backend:n,attrs:{dtype:s}});return n.disposeIntermediateTensorInfo(i),o}if(!k.hasEncodingLoss(r.dtype,s)){let i=Gn({inputs:{x:r},backend:n});return{dataId:i.dataId,shape:i.shape,dtype:s}}if(s==="int32")return fU(r,n);if(s==="bool"){let i=n.makeTensorInfo([],"bool",k.getTypedArrayFromDType("bool",1)),o=Bw({inputs:{a:r,b:i},backend:n});return n.disposeIntermediateTensorInfo(i),o}throw new Error(`Error in Cast: failed to cast ${r.dtype} to ${s}`)}var mU={kernelName:ws,backendName:"webgl",kernelFunc:KA},Vw="return ceil(x);",AU=Ke({opSnippet:Vw,packedOpSnippet:Vw,cpuKernelImpl:wB}),yU={kernelName:ks,backendName:"webgl",kernelFunc:AU},gU=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(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}},xU=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(n,a)=>{this.minLoc==null&&(this.minLoc=n.getUniformLocationNoThrow(a,"minVal"),this.maxLoc=n.getUniformLocationNoThrow(a,"maxVal")),n.gl.uniform1f(this.minLoc,e),n.gl.uniform1f(this.maxLoc,t)}}};function bU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o;te().getBool("WEBGL_PACK_CLIP")?o=new xU(r.shape):o=new gU(r.shape);let u=o.getCustomSetupFunc(s,i);return n.runWebGLProgram(o,[r],r.dtype,u)}var vU={kernelName:_r,backendName:"webgl",kernelFunc:bU},wU=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 jw(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}}function kU(e){let{inputs:t,backend:n}=e,{x:a}=t,r=n.texData.get(a.dataId),s=new wU(a.shape),i=[jw(a,r.complexTensorInfos.real),jw(a,r.complexTensorInfos.imag)];return n.runWebGLProgram(s,i,i[0].dtype)}var IU={kernelName:Du,backendName:"webgl",kernelFunc:kU},SU=class{constructor(e){this.outputShape=[],this.outputShape=F.computeOutShape(e,1),this.variableNames=e.map((s,i)=>`T${i}`);let t=new Array(e.length-1);t[0]=e[0][1];for(let s=1;s<t.length;s++)t[s]=t[s-1]+e[s][1];let n=[`if (yC < ${t[0]}) setOutput(getT0(yR, yC));`];for(let s=1;s<t.length;s++){let i=t[s-1];n.push(`else if (yC < ${t[s]}) setOutput(getT${s}(yR, yC-${i}));`)}let a=t.length,r=t[t.length-1];n.push(`else setOutput(getT${a}(yR, yC-${r}));`),this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int yR = coords.x;
|
|
int yC = coords.y;
|
|
|
|
${n.join(`
|
|
`)}
|
|
}
|
|
`}},NU=class{constructor(e,t){this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[],this.outputShape=F.computeOutShape(e,t);let n=this.outputShape,a=n.length,r=ut(a),s=gn("coords",a),i=["x","y","z","w","u","v"].slice(0,a);this.variableNames=e.map((m,f)=>`T${f}`);let o=new Array(e.length-1);o[0]=e[0][t];for(let m=1;m<o.length;m++)o[m]=o[m-1]+e[m][t];let u=i[t],l=i.slice(-2),d=i.join(),p=`if (${u} < ${o[0]}) {
|
|
return getChannel(
|
|
getT0(${d}), vec2(${l.join()}));
|
|
}`;for(let m=1;m<o.length;m++){let f=o[m-1];p+=`
|
|
if (${u} < ${o[m]} && ${u} >= ${o[m-1]}) {
|
|
return getChannel(
|
|
getT${m}(${Kh(i,u,f)}),
|
|
vec2(${Kh(l,u,f)}));
|
|
}`}let c=o.length,h=o[o.length-1];p+=`
|
|
return getChannel(
|
|
getT${c}(${Kh(i,u,h)}),
|
|
vec2(${Kh(l,u,h)}));`,this.userCode=`
|
|
float getValue(${i.map(m=>"int "+m)}) {
|
|
${p}
|
|
}
|
|
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
vec4 result = vec4(getValue(${s}), 0., 0., 0.);
|
|
|
|
${s[a-1]} = ${s[a-1]} + 1;
|
|
if (${s[a-1]} < ${n[a-1]}) {
|
|
result.g = getValue(${s});
|
|
}
|
|
|
|
${s[a-2]} = ${s[a-2]} + 1;
|
|
if (${s[a-2]} < ${n[a-2]}) {
|
|
result.a = getValue(${s});
|
|
}
|
|
|
|
${s[a-1]} = ${s[a-1]} - 1;
|
|
if (${s[a-2]} < ${n[a-2]} &&
|
|
${s[a-1]} < ${n[a-1]}) {
|
|
result.b = getValue(${s});
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}};function Kh(e,t,n){let a=e.indexOf(t);return e.map((r,s)=>s===a?`${r} - ${n}`:r).join()}function Zh(e){let{inputs:t,backend:n}=e,{input:a}=t,r=n.texData.get(a.dataId);return Gn({inputs:{x:r.complexTensorInfos.imag},backend:n})}var TU={kernelName:dc,backendName:"webgl",kernelFunc:Zh};function Zl(e,t,n){let a=e[0].dtype;if(a==="complex64"){let d=e.map(f=>Od({inputs:{input:f},backend:n})),p=e.map(f=>Zh({inputs:{input:f},backend:n})),c=Zl(d,t,n),h=Zl(p,t,n),m=Yr({inputs:{real:c,imag:h},backend:n});return d.forEach(f=>n.disposeIntermediateTensorInfo(f)),p.forEach(f=>n.disposeIntermediateTensorInfo(f)),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}let r=n.shouldExecuteOnCPU(e);if(a==="string"&&(r=!0),r){let d=e.map(y=>{let g=k.sizeFromShape(y.shape.slice(t));return ye({inputs:{x:y},backend:n,attrs:{shape:[-1,g]}})}),p=d.map(y=>({vals:n.readSync(y.dataId),shape:y.shape})),c=F.computeOutShape(d.map(y=>y.shape),1),h=d[0].shape[0]===1,m=kB(p,c,a,h),f=F.computeOutShape(e.map(y=>y.shape),t),A=n.makeTensorInfo(f,a,m);return d.forEach(y=>n.disposeIntermediateTensorInfo(y)),A}if(e.length>te().getNumber("WEBGL_MAX_TEXTURES_IN_SHADER")){let d=Math.floor(e.length/2),p=Zl(e.slice(0,d),t,n),c=Zl(e.slice(d),t,n),h=Zl([p,c],t,n);return n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),h}if(te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")&&e[0].shape.length>1){let d=new NU(e.map(p=>p.shape),t);return n.runWebGLProgram(d,e,a)}let{tensors2D:s,outShape:i}=CU(e,t,n),o=new SU(s.map(d=>d.shape)),u=n.runWebGLProgram(o,s,a);s.forEach(d=>n.disposeIntermediateTensorInfo(d));let l=ye({inputs:{x:u},attrs:{shape:i},backend:n});return n.disposeIntermediateTensorInfo(u),l}function CU(e,t,n){let a=F.computeOutShape(e.map(r=>r.shape),t);return{tensors2D:e.map(r=>ye({inputs:{x:r},attrs:{shape:[-1,k.sizeFromShape(r.shape.slice(t))]},backend:n})),outShape:a}}function Uw(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a,s=k.parseAxisParam(r,t[0].shape)[0],i=F.computeOutShape(t.map(l=>l.shape),s);if(k.sizeFromShape(i)===0)return n.makeTensorInfo(i,t[0].dtype,[]);let o=t.filter(l=>k.sizeFromShape(l.shape)>0);if(o.length===1)return Gn({inputs:{x:o[0]},backend:n});let u=o.map(l=>l.shape);return F.assertParamsConsistent(u,s),Zl(o,s,n)}var EU={kernelName:Io,backendName:"webgl",kernelFunc:Uw},Hw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.padInfo.top,i=e.padInfo.left,o=e.strideHeight,u=e.strideWidth,l=e.dilationHeight,d=e.dilationWidth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4,f=e.dataFormat==="channelsLast",A=f?1:2,y=f?2:3,g=f?3:1,x="",v="";n&&(a?x=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?x=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:x=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,v="result = activation(result);");let b=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${x}
|
|
|
|
const ivec2 strides = ivec2(${o}, ${u});
|
|
const ivec2 pads = ivec2(${s}, ${i});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d2 = coords[${g}];
|
|
|
|
ivec2 xRCCorner =
|
|
ivec2(coords[${A}], coords[${y}]) * strides - pads;
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
// Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${p}; wR++) {
|
|
int xR = xRCorner + wR * ${l};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${c}; wC++) {
|
|
int xC = xCCorner + wC * ${d};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 wValues = vec4(
|
|
getW(wR, wC, d1, d2),
|
|
getW(wR, wC, d1 + 1, d2),
|
|
getW(wR, wC, d1 + 2, d2),
|
|
getW(wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
if (${f}) {
|
|
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 (${m===1}) {
|
|
|
|
if (${f}) {
|
|
dotProd +=
|
|
getX(batch, xR, xC, ${h}) *
|
|
getW(wR, wC, ${h}, d2);
|
|
} else {
|
|
dotProd +=
|
|
getX(batch, ${h}, xR, xC) *
|
|
getW(wR, wC, ${h}, d2);
|
|
}
|
|
|
|
} else if (${m===2}) {
|
|
vec2 wValues = vec2(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2)
|
|
);
|
|
|
|
if (${f}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec2 xValues = vec2(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
} else if (${m===3}) {
|
|
vec3 wValues = vec3(
|
|
getW(wR, wC, ${h}, d2),
|
|
getW(wR, wC, ${h} + 1, d2),
|
|
getW(wR, wC, ${h} + 2, d2)
|
|
);
|
|
|
|
if (${f}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xR, xC, ${h}),
|
|
getX(batch, xR, xC, ${h} + 1),
|
|
getX(batch, xR, xC, ${h} + 2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else {
|
|
vec3 xValues = vec3(
|
|
getX(batch, ${h}, xR, xC),
|
|
getX(batch, ${h} + 1, xR, xC),
|
|
getX(batch, ${h} + 2, xR, xC)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = dotProd;
|
|
${b}
|
|
${v}
|
|
setOutput(result);
|
|
}
|
|
`}},RU=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let t=e.padInfo.front,n=e.padInfo.top,a=e.padInfo.left,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,d=e.filterDepth,p=e.filterHeight,c=e.filterWidth,h=Math.floor(e.inChannels/4)*4,m=e.inChannels%4;this.userCode=`
|
|
const ivec3 strides = ivec3(${r}, ${s}, ${i});
|
|
const ivec3 pads = ivec3(${t}, ${n}, ${a});
|
|
|
|
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 < ${d}; wF++) {
|
|
int xF = xFCorner + wF * ${o};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wR = 0; wR < ${p}; wR++) {
|
|
int xR = xRCorner + wR * ${u};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${c}; wC++) {
|
|
int xC = xCCorner + wC * ${l};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int d1 = 0; d1 < ${h}; d1 += 4) {
|
|
vec4 xValues = vec4(
|
|
getX(batch, xF, xR, xC, d1),
|
|
getX(batch, xF, xR, xC, d1 + 1),
|
|
getX(batch, xF, xR, xC, d1 + 2),
|
|
getX(batch, xF, xR, xC, d1 + 3)
|
|
);
|
|
vec4 wValues = vec4(
|
|
getW(wF, wR, wC, d1, d2),
|
|
getW(wF, wR, wC, d1 + 1, d2),
|
|
getW(wF, wR, wC, d1 + 2, d2),
|
|
getW(wF, wR, wC, d1 + 3, d2)
|
|
);
|
|
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
|
|
if (${m===1}) {
|
|
dotProd +=
|
|
getX(batch, xF, xR, xC, ${h}) *
|
|
getW(wF, wR, wC, ${h}, d2);
|
|
} else if (${m===2}) {
|
|
vec2 xValues = vec2(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1)
|
|
);
|
|
vec2 wValues = vec2(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
} else if (${m===3}) {
|
|
vec3 xValues = vec3(
|
|
getX(batch, xF, xR, xC, ${h}),
|
|
getX(batch, xF, xR, xC, ${h} + 1),
|
|
getX(batch, xF, xR, xC, ${h} + 2)
|
|
);
|
|
vec3 wValues = vec3(
|
|
getW(wF, wR, wC, ${h}, d2),
|
|
getW(wF, wR, wC, ${h} + 1, d2),
|
|
getW(wF, wR, wC, ${h} + 2, d2)
|
|
);
|
|
dotProd += dot(xValues, wValues);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},MU=class{constructor(e,t,n){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e;let{filterWidth:a,inChannels:r,strideWidth:s,strideHeight:i,padInfo:o,outWidth:u,dilationWidth:l,dilationHeight:d,dataFormat:p}=n,{left:c,top:h}=o,m=r*a,f=yn(),A=p==="channelsLast",y=A?0:1,g=A?1:2,x="";for(let v=0;v<=1;v++)for(let b=0;b<=1;b++)x+=`
|
|
blockIndex = rc.y + ${b};
|
|
pos = rc.x + ${v};
|
|
|
|
if(blockIndex < ${e[1]} && pos < ${e[0]}) {
|
|
offsetY = int(blockIndex / (${u})) * ${i} - ${h};
|
|
d0 = offsetY + ${d} * (pos / ${m});
|
|
|
|
if(d0 < ${t[y]} && d0 >= 0) {
|
|
|
|
offsetX = int(mod(float(blockIndex), ${u}.) * ${s}. - ${c}.);
|
|
d1 = offsetX + ${l} * (int(mod(float(pos), ${m}.) / ${r}.));
|
|
|
|
if(d1 < ${t[g]} && d1 >= 0) {
|
|
|
|
ch = int(mod(float(pos), ${r}.));
|
|
|
|
if (${A}) {
|
|
innerDims = vec2(d1, ch);
|
|
result[${v*2+b}] = getChannel(
|
|
getA(d0, int(innerDims.x),
|
|
int(innerDims.y)), innerDims);
|
|
} else {
|
|
innerDims = vec2(d0, d1);
|
|
result[${v*2+b}] = 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;
|
|
|
|
${x}
|
|
|
|
${f.output} = result;
|
|
}
|
|
`}};function Gw({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let u=e.shape,l=a.texData.get(e.dataId),d=n.inChannels,p=u[0]*u[1]*u[2],c=n.outChannels,h=n.dataFormat==="channelsLast",m=!1,f=!1,A,y=[],g=(p===1||c===1)&&d>_w,x=u[2]%2!=0&&!!l.isPacked;if(g||!te().getBool("WEBGL_LAZILY_UNPACK")||!te().getBool("WEBGL_PACK_BINARY_OPERATIONS")||!x){let v=h?u[0]*u[1]*u[2]:u[0]*u[2]*u[3],b=ye({inputs:{x:e},backend:a,attrs:{shape:[1,v,n.inChannels]}}),w=ye({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}}),N=qh({a:b,b:w,transposeA:m,transposeB:f,backend:a,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i});A=ye({inputs:{x:N},backend:a,attrs:{shape:n.outShape}}),y.push(b),y.push(w),y.push(N)}else{let v=h?u[0]*u[1]*(u[2]+1):u[0]*u[2]*(u[3]+1),b={dataId:e.dataId,shape:[1,v,n.inChannels],dtype:e.dtype},w=l.shape;l.shape=l.shape.slice(),l.shape[l.shape.length-2]++,k.assert(Md(l.shape,b.shape),()=>`packed reshape ${l.shape} to ${b.shape} isn't free`);let N=ye({inputs:{x:t},backend:a,attrs:{shape:[1,n.inChannels,n.outChannels]}});y.push(N);let C=qh({a:b,b:N,backend:a,transposeA:m,transposeB:f,bias:r,activation:o,preluActivationWeights:s,leakyreluAlpha:i}),E=a.texData.get(C.dataId);k.assert(E.isPacked,()=>"batchMatMul result is expected to be packed"),l.shape=w,E.shape=n.outShape,A=Gn({inputs:{x:C},backend:a}),A.shape=n.outShape,y.push(C)}for(let v of y)a.disposeIntermediateTensorInfo(v);return A}function qw({x:e,filter:t,convInfo:n,backend:a,bias:r=null,preluActivationWeights:s=null,leakyreluAlpha:i=0,activation:o=null}){let{filterWidth:u,filterHeight:l,inChannels:d,outWidth:p,outHeight:c,dataFormat:h}=n,m=h==="channelsLast",f=u*l*d,A=c*p,y=[f,A],g=!0,x=!1,v=[],b=ye({inputs:{x:e},backend:a,attrs:{shape:e.shape.slice(1)}}),w=ye({inputs:{x:t},backend:a,attrs:{shape:[1,f,k.sizeFromShape(t.shape)/f]}});v.push(b),v.push(w);let N=new MU(y,b.shape,n),C=a.runWebGLProgram(N,[b],"float32"),E=ye({inputs:{x:C},backend:a,attrs:{shape:[1,y[0],y[1]]}});v.push(C),v.push(E);let z=r!=null,$=s!=null,S=o==="leakyrelu",O=o?Uh(o,!0):null,_=new Rw(E.shape,w.shape,[1,A,n.outChannels],g,x,z,O,$,S),W=[E,w];if(r&&W.push(r),$&&W.push(s),S){let K=a.makeTensorInfo([],"float32",k.createScalarValue(i,"float32"));W.push(K),v.push(K)}let G=a.runWebGLProgram(_,W,"float32"),H=m?[1,c,p,n.outChannels]:[1,n.outChannels,c,p],J=ye({inputs:{x:G},backend:a,attrs:{shape:H}});v.push(G);for(let K of v)a.disposeIntermediateTensorInfo(K);return J}function FU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dataFormat:u,dilations:l,dimRoundingMode:d}=a,p=F.convertConv2DDataFormat(u),c=F.computeConv2DInfo(r.shape,s.shape,i,l,o,d,!1,p),h;if(c.filterHeight===1&&c.filterWidth===1&&c.dilationHeight===1&&c.dilationWidth===1&&c.strideHeight===1&&c.strideWidth===1&&(c.padInfo.type==="SAME"||c.padInfo.type==="VALID"))h=Gw({x:r,filter:s,convInfo:c,backend:n});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)h=qw({x:r,filter:s,convInfo:c,backend:n});else{let f=new Hw(c);h=n.runWebGLProgram(f,[r,s],"float32")}let m=ye({inputs:{x:h},backend:n,attrs:{shape:c.outShape}});return n.disposeIntermediateTensorInfo(h),m}var $U={kernelName:Is,backendName:"webgl",kernelFunc:FU},DU=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=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} - ${a};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
if (${s}) {
|
|
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);
|
|
}
|
|
`}},_U=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=e.dataFormat==="channelsLast",i=t-1-e.padInfo.top,o=n-1-e.padInfo.left,u=s?1:2,l=s?2:3,d=s?3:1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int d1 = coords[${d}];
|
|
|
|
ivec2 dyCorner = ivec2(coords[${u}], coords[${l}]) - 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) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
for (int d2 = 0; d2 < ${e.outChannels}; d2++) {
|
|
|
|
if (${s}) {
|
|
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);
|
|
}
|
|
`}},OU=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.padInfo.front,s=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} - ${r};
|
|
|
|
if (xF < 0 || xF >= ${e.inDepth}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yR = 0; yR < ${e.outHeight}; yR++) {
|
|
int xR = wR + yR * ${n} - ${s};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${a} - ${i};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yF, yR, yC, d2);
|
|
float xValue = getX(b, xF, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},zU=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterDepth,n=e.filterHeight,a=e.filterWidth,r=e.strideDepth,s=e.strideHeight,i=e.strideWidth,o=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=a-1-e.padInfo.left;this.userCode=`
|
|
const ivec3 pads = ivec3(${o}, ${u}, ${l});
|
|
|
|
void main() {
|
|
ivec5 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
int d1 = coords.u;
|
|
|
|
|
|
ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;
|
|
int dyFCorner = dyCorner.x;
|
|
int dyRCorner = dyCorner.y;
|
|
int dyCCorner = dyCorner.z;
|
|
|
|
float dotProd = 0.0;
|
|
for (int wF = 0; wF < ${t}; wF++) {
|
|
float dyF = float(dyFCorner + wF) / ${r}.0;
|
|
|
|
if (dyF < 0.0 || dyF >= ${e.outDepth}.0 || fract(dyF) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyF = int(dyF);
|
|
|
|
int wFPerm = ${t} - 1 - wF;
|
|
|
|
for (int wR = 0; wR < ${n}; wR++) {
|
|
float dyR = float(dyRCorner + wR) / ${s}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 ||
|
|
fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${n} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${a}; 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 = ${a} - 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 PU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,dataFormat:u,dimRoundingMode:l,filterShape:d}=a,p=F.convertConv2DDataFormat(u),c=F.computeConv2DInfo(r.shape,d,i,1,o,l,!1,p),h=new DU(c);return n.runWebGLProgram(h,[r,s],"float32")}var LU={kernelName:Yp,backendName:"webgl",kernelFunc:PU};function WU(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{inputShape:i,strides:o,pad:u,dataFormat:l,dimRoundingMode:d}=a,p=F.convertConv2DDataFormat(l),c=F.computeConv2DInfo(i,s.shape,o,1,u,d,!1,p),h=new _U(c);return n.runWebGLProgram(h,[r,s],"float32")}var BU={kernelName:Ss,backendName:"webgl",kernelFunc:WU};function VU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:u}=a,l=F.computeConv3DInfo(r.shape,s.shape,i,u,o),d=new RU(l);return n.runWebGLProgram(d,[r,s],"float32")}var jU={kernelName:_u,backendName:"webgl",kernelFunc:VU};function UU(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,pad:o,filterShape:u}=a,l=F.computeConv3DInfo(r.shape,u,i,1,o),d=new OU(l);return n.runWebGLProgram(d,[r,s],"float32")}var HU={kernelName:Jp,backendName:"webgl",kernelFunc:UU};function GU(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{pad:i,strides:o,inputShape:u}=a,l=F.computeConv3DInfo(u,s.shape,o,1,i),d=new zU(l);return n.runWebGLProgram(d,[r,s],"float32")}var qU={kernelName:Qp,backendName:"webgl",kernelFunc:GU},XU=Ew+`
|
|
return cos(x);
|
|
`,KU=Ke({opSnippet:XU}),ZU={kernelName:Ns,backendName:"webgl",kernelFunc:KU},YU=`
|
|
float e2x = exp(-x);
|
|
return (e2x + 1.0 / e2x) / 2.0;
|
|
`,JU=Ke({opSnippet:YU}),QU={kernelName:So,backendName:"webgl",kernelFunc:JU},eH=class{constructor(e,t,n,a,r){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];let[s,i,o,u]=e,[l]=t,[d,p]=n;this.outputShape=[l,d,p,u];let c=a==="bilinear"?1:0,[h,m]=[`${i-1}.0`,`${o-1}.0`],[f,A,y]=d>1?[`${(i-1)/(d-1)}`,"(y2-y1) * height_ratio",`y1*${h} + float(y)*(height_scale)`]:["0.0","0.0",`0.5 * (y1+y2) * ${h}`],[g,x,v]=p>1?[`${(o-1)/(p-1)}`,"(x2-x1) * width_ratio",`x1*${m} + float(x)*(width_scale)`]:["0.0","0.0",`0.5 * (x1+x2) * ${m}`];this.userCode=`
|
|
const float height_ratio = float(${f});
|
|
const float width_ratio = float(${g});
|
|
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 >= ${s}) {
|
|
return;
|
|
}
|
|
|
|
float height_scale = ${A};
|
|
float width_scale = ${x};
|
|
|
|
float in_y = ${y};
|
|
if( in_y < 0.0 || in_y > ${h} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
float in_x = ${v};
|
|
if( in_x < 0.0 || in_x > ${m} ) {
|
|
setOutput(float(${r}));
|
|
return;
|
|
}
|
|
|
|
vec2 sourceFracIndexCR = vec2(in_x,in_y);
|
|
if(${c} == 1) {
|
|
// Compute the four integer indices.
|
|
ivec2 sourceFloorCR = ivec2(sourceFracIndexCR);
|
|
ivec2 sourceCeilCR = ivec2(ceil(sourceFracIndexCR));
|
|
|
|
float topLeft = getImage(b, sourceFloorCR.y, sourceFloorCR.x, d);
|
|
float bottomLeft = getImage(b, sourceCeilCR.y, sourceFloorCR.x, d);
|
|
float topRight = getImage(b, sourceFloorCR.y, sourceCeilCR.x, d);
|
|
float bottomRight = getImage(b, sourceCeilCR.y, sourceCeilCR.x, d);
|
|
|
|
vec2 fracCR = sourceFracIndexCR - vec2(sourceFloorCR);
|
|
|
|
float top = topLeft + (topRight - topLeft) * fracCR.x;
|
|
float bottom = bottomLeft + (bottomRight - bottomLeft) * fracCR.x;
|
|
float newValue = top + (bottom - top) * fracCR.y;
|
|
setOutput(newValue);
|
|
} else {
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestCR = ivec2(floor(
|
|
sourceFracIndexCR + vec2(0.5,0.5)));
|
|
float newValue = getImage(b, sourceNearestCR.y, sourceNearestCR.x, d);
|
|
setOutput(newValue);
|
|
}
|
|
}
|
|
`}},tH=e=>{let{inputs:t,backend:n,attrs:a}=e,{image:r,boxes:s,boxInd:i}=t,{cropSize:o,method:u,extrapolationValue:l}=a,d=new eH(r.shape,s.shape,o,u,l);return n.runWebGLProgram(d,[r,s,i],"float32")},nH={kernelName:No,backendName:"webgl",kernelFunc:tH},Xw=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=e;let a=e.length,r=t?"0.0":`getX(${Kw(a,"coords")})`,s=e[e.length-1],i="",o="";t?(i=n?`end != ${s-1}`:"end != 0",o=n?"end + 1":"end - 1"):(i=n?`end + pow2 < ${s}`:"end >= pow2",o=n?"end + pow2":"end - pow2"),this.userCode=`
|
|
uniform float index;
|
|
void main() {
|
|
${ut(a)} coords = getOutputCoords();
|
|
int end = ${Zw(a,"coords")};
|
|
float val = ${r};
|
|
int pow2 = int(pow(2.0, index));
|
|
if (${i}) {
|
|
int idx = ${o};
|
|
${Zw(a,"coords")} = idx;
|
|
val += getX(${Kw(a,"coords")});
|
|
}
|
|
setOutput(val);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.index==null&&(this.index=t.getUniformLocation(n,"index")),t.gl.uniform1f(this.index,e)}}};function Kw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.x, ${t}.y`;if(e===3)return`${t}.x, ${t}.y, ${t}.z`;if(e===4)return`${t}.x, ${t}.y, ${t}.z, ${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function Zw(e,t){if(e===1)return`${t}`;if(e===2)return`${t}.y`;if(e===3)return`${t}.z`;if(e===4)return`${t}.w`;throw Error(`Cumulative sum for rank ${e} is not yet supported`)}function aH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,exclusive:i,reverse:o}=a,u=r.shape.length,l=F.getAxesPermutation([s],u),d=r;l!=null&&(d=xn({inputs:{x:r},backend:n,attrs:{perm:l}}));let p=F.getInnerMostAxes(1,u)[0];if(p!==u-1)throw new Error(`WebGL cumsum shader expects an inner-most axis=${r.shape.length-1} but got axis=${s}`);let c=d.shape[p],h=Gn({inputs:{x:d},backend:n});for(let m=0;m<=Math.ceil(Math.log2(c))-1;m++){let f=new Xw(d.shape,!1,o),A=f.getCustomSetupFunc(m),y=h;h=n.runWebGLProgram(f,[h],h.dtype,A),n.disposeIntermediateTensorInfo(y)}if(i){let m=new Xw(d.shape,i,o),f=h;h=n.runWebGLProgram(m,[h],h.dtype),n.disposeIntermediateTensorInfo(f)}if(l!=null){let m=F.getUndoAxesPermutation(l),f=xn({inputs:{x:h},backend:n,attrs:{perm:m}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(d),f}return h}var rH={kernelName:Ts,backendName:"webgl",kernelFunc:aH};function sH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,weights:s}=t,{size:i,binaryOutput:o}=a;if(r.shape.length===1){let u=n.readSync(r.dataId),l=n.readSync(s.dataId),d=hw(u,l,s.dtype,s.shape,i);return n.makeTensorInfo([i],s.dtype,d)}else if(r.shape.length===2){let u=n.bufferSync(r),l=n.bufferSync(s),d=vB(u,l,i,o);return n.makeTensorInfo(d.shape,s.dtype,d.values)}throw new Error(`Error in denseBincount: input must be at most rank 2, but got rank${r.shape.length}.`)}var iH={kernelName:ec,backendName:"webgl",kernelFunc:sH},oH=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int h = ${this.getHeightCoordString()};
|
|
int w = ${this.getWidthCoordString()};
|
|
int d = ${this.getDepthCoordString()};
|
|
|
|
int in_h = h / ${t};
|
|
int offset_h = imod(h, ${t});
|
|
int in_w = w / ${t};
|
|
int offset_w = imod(w, ${t});
|
|
int offset_d = (offset_h * ${t} + offset_w) *
|
|
${this.getOutputDepthSize()};
|
|
int in_d = d + offset_d;
|
|
|
|
float result = ${this.getInputSamplingString()};
|
|
setOutput(result);
|
|
}
|
|
`}getHeightCoordString(){return this.dataFormat==="NHWC"?"coords[1]":"coords[2]"}getWidthCoordString(){return this.dataFormat==="NHWC"?"coords[2]":"coords[3]"}getDepthCoordString(){return this.dataFormat==="NHWC"?"coords[3]":"coords[1]"}getOutputDepthSize(){return this.dataFormat==="NHWC"?this.outputShape[3]:this.outputShape[1]}getInputSamplingString(){return this.dataFormat==="NHWC"?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"}};function lH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockSize:s,dataFormat:i}=a;k.assert(s>1,()=>`blockSize should be > 1 for depthToSpace, but was: ${s}`);let o=r.shape[0],u=i==="NHWC"?r.shape[1]:r.shape[2],l=i==="NHWC"?r.shape[2]:r.shape[3],d=i==="NHWC"?r.shape[3]:r.shape[1],p=u*s,c=l*s,h=d/(s*s),m=i==="NHWC"?[o,p,c,h]:[o,h,p,c],f=new oH(m,s,i);return n.runWebGLProgram(f,[r],r.dtype)}var uH={kernelName:To,backendName:"webgl",kernelFunc:lH},Yw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.outputShape=e.outShape;let s=e.inHeight,i=e.inWidth,o=e.padInfo.top,u=e.padInfo.left,l=e.strideHeight,d=e.strideWidth,p=e.dilationHeight,c=e.dilationWidth,h=e.filterHeight,m=e.filterWidth,f=e.outChannels/e.inChannels,A="",y="";n&&(a?A=`float activation(float a) {
|
|
float b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?A=`float activation(float a) {
|
|
float b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:A=`
|
|
float activation(float x) {
|
|
${n}
|
|
}
|
|
`,y="result = activation(result);");let g=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${A}
|
|
|
|
const ivec2 strides = ivec2(${l}, ${d});
|
|
const ivec2 pads = ivec2(${o}, ${u});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${f};
|
|
int q = d2 - d1 * ${f};
|
|
|
|
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 < ${h}; wR++) {
|
|
int xR = xRCorner + wR * ${p};
|
|
|
|
if (xR < 0 || xR >= ${s}) {
|
|
continue;
|
|
}
|
|
|
|
for (int wC = 0; wC < ${m}; wC++) {
|
|
int xC = xCCorner + wC * ${c};
|
|
|
|
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;
|
|
${g}
|
|
${y}
|
|
setOutput(result);
|
|
}
|
|
`}},Jw=class{constructor(e,t=!1,n=null,a=!1,r=!1){this.variableNames=["x","W"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=e.outShape;let s=e.outChannels/e.inChannels,i=e.inHeight,o=e.inWidth,u=e.padInfo.top,l=e.padInfo.left,d=e.strideHeight,p=e.strideWidth,c=e.dilationHeight,h=e.dilationWidth,m=e.filterHeight,f=e.filterWidth,A=f,y=`
|
|
int xR; int xC; int xCOffset;
|
|
vec4 wTexel; vec4 previous; vec4 final;`;for(let b=0;b<f;b++)y+=`
|
|
vec4 xTexelC${b*2};
|
|
int xTexelC${b*2}Ready;
|
|
vec4 xC${b};`;for(let b=0;b<m;b++){for(let w=0;w<f;w++)y+=`
|
|
xTexelC${w*2} = vec4(0.0);
|
|
xTexelC${w*2}Ready = 0;
|
|
xC${w} = vec4(0.0);`;y+=`
|
|
xR = xRCorner + ${b*c};
|
|
if (xR >=0 && xR < ${i}) {
|
|
`;for(let w=0;w<(A+1)/2;w++){let N=w*2,C=N*h;if(y+=`
|
|
xC = xCCorner + ${C};
|
|
`,p===1){if(N<f&&(l%2==1?(y+=`
|
|
xCOffset = xC + 1;
|
|
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${C}Ready == 0) {
|
|
xTexelC${C} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${C}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C}Ready = 1;
|
|
}
|
|
`,h===1&&C>0?y+=`
|
|
xC${N} = vec4(xTexelC${C-2}.zw, xTexelC${C}.xy);
|
|
`:y+=`
|
|
xCOffset = xC + 1 - 2;
|
|
|
|
if (xCOffset >= 0 && xCOffset < ${o}) {
|
|
previous = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
previous.zw = vec2(0.0);
|
|
}
|
|
|
|
xC${N} = vec4(previous.zw, xTexelC${C}.xy);
|
|
} else {
|
|
xC${N} = vec4(0.0, 0.0, xTexelC${C}.xy);
|
|
}
|
|
`):y+=`
|
|
if (xC >= 0 && xC < ${o} && xTexelC${C}Ready == 0) {
|
|
xTexelC${C} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= ${o}) {
|
|
xTexelC${C}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C}Ready = 1;
|
|
}
|
|
|
|
xC${N} = xTexelC${C};
|
|
`,C+1<f)){let E=l%2==0?k.nearestLargerEven(h):h;h%2==0&&l%2==1||h%2!=0&&l%2!=1?(y+=`
|
|
xCOffset = xC + ${l%2} + ${E};
|
|
|
|
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${C+2}Ready == 0) {
|
|
xTexelC${C+2} = getX(batch, xR, xCOffset, d1);
|
|
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${C+2}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C+2}Ready = 1;
|
|
}
|
|
`,h>1&&(y+=`
|
|
xCOffset -= 2;
|
|
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${C}Ready == 0) {
|
|
xTexelC${C} = getX(batch, xR, xCOffset, d1);
|
|
xTexelC${C}Ready = 1;
|
|
}
|
|
`),y+=`
|
|
xC${N+1} = vec4(xTexelC${C}.zw, xTexelC${C+2}.xy);
|
|
`):E===1?y+=`
|
|
xC${N+1} = xTexelC${C};
|
|
`:y+=`
|
|
xCOffset = xC + ${E};
|
|
|
|
if (xCOffset >= 0 && xCOffset < ${o} && xTexelC${C+2}Ready == 0) {
|
|
xTexelC${C+2} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${C+2}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C+2}Ready = 1;
|
|
}
|
|
|
|
xC${N+1} = xTexelC${C+2};
|
|
`}}else C<f&&(l%2==1?(y+=`
|
|
xCOffset = xC + 1 - ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${C}Ready == 0) {
|
|
xTexelC${C} = getX(batch, xR, xCOffset, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${C}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C}Ready = 1;
|
|
}
|
|
|
|
if(xC + 1 >= 0 && xC + 1 < ${o} && xTexelC${C+2}Ready == 0) {
|
|
xTexelC${C+2} = getX(batch, xR, xC + 1, d1);
|
|
// Need to manually clear unused channels in case
|
|
// we're reading from recycled texture.
|
|
if (xC + 2 >= ${o}) {
|
|
xTexelC${C+2}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C+2}Ready = 1;
|
|
}
|
|
|
|
xC${N} = vec4(xTexelC${C}.zw, xTexelC${C+2}.zw);
|
|
`,C+1<f&&(y+=`
|
|
final = vec4(0.0);
|
|
xCOffset = xC + 1 + ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${o}) {
|
|
final = getX(batch, xR, xCOffset, d1);
|
|
}
|
|
xC${N+1} = vec4(xTexelC${C+2}.xy, final.xy);
|
|
`)):(y+=`
|
|
if(xC >= 0 && xC < ${o} && xTexelC${C}Ready == 0) {
|
|
xTexelC${C} = getX(batch, xR, xC, d1);
|
|
if (xC + 1 >= ${o}) {
|
|
xTexelC${C}.zw = vec2(0.0);
|
|
}
|
|
xTexelC${C}Ready = 1;
|
|
}
|
|
|
|
xCOffset = xC + ${p};
|
|
if(xCOffset >= 0 && xCOffset < ${o} && xTexelC${C+2}Ready == 0) {
|
|
xTexelC${C+2} = getX(batch, xR, xCOffset, d1);
|
|
if (xCOffset + 1 >= ${o}) {
|
|
xTexelC${C+2}.zw = vec2(0.);
|
|
}
|
|
xTexelC${C+2}Ready = 1;
|
|
}
|
|
|
|
xC${N} = vec4(
|
|
xTexelC${C}.xy, xTexelC${C+2}.xy);
|
|
`,C+1<f&&(y+=`
|
|
xC${N+1} = vec4(xTexelC${C}.zw, xTexelC${C+2}.zw);
|
|
`)));N<f&&(y+=`
|
|
wTexel = getW(${b}, ${C}, d1, q);
|
|
dotProd += xC${N} * vec4(wTexel.xz, wTexel.xz);
|
|
`,C+1<f&&(y+=`
|
|
wTexel = getW(${b}, ${C+1}, d1, q);
|
|
dotProd += xC${N+1} * vec4(wTexel.xz, wTexel.xz);
|
|
`))}y+=`
|
|
}
|
|
`}let g="",x="";n&&(a?g=`vec4 activation(vec4 a) {
|
|
vec4 b = getPreluActivationWeightsAtOutCoords();
|
|
${n}
|
|
}`:r?g=`vec4 activation(vec4 a) {
|
|
vec4 b = getLeakyreluAlphaAtOutCoords();
|
|
${n}
|
|
}`:g=`vec4 activation(vec4 x) {
|
|
${n}
|
|
}`,x="result = activation(result);");let v=t?"result += getBiasAtOutCoords();":"";t&&this.variableNames.push("bias"),a&&this.variableNames.push("preluActivationWeights"),r&&this.variableNames.push("leakyreluAlpha"),this.userCode=`
|
|
${g}
|
|
|
|
const ivec2 strides = ivec2(${d}, ${p});
|
|
const ivec2 pads = ivec2(${u}, ${l});
|
|
|
|
void main() {
|
|
|
|
ivec4 coords = getOutputCoords();
|
|
int batch = coords.x;
|
|
ivec2 xRCCorner = coords.yz * strides - pads;
|
|
int d2 = coords.w;
|
|
int d1 = d2 / ${s};
|
|
int q = d2 - d1 * ${s};
|
|
int xRCorner = xRCCorner.x;
|
|
int xCCorner = xRCCorner.y;
|
|
|
|
//intialize dotProd with a small epsilon seems to reduce GPU accuracy loss.
|
|
vec4 dotProd = vec4(0.000000000000001);
|
|
|
|
${y}
|
|
|
|
vec4 result = dotProd - vec4(0.000000000000001);
|
|
${v}
|
|
${x}
|
|
setOutput(result);
|
|
}
|
|
`}};function dH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:u,dimRoundingMode:l}=a,d=u;d==null&&(d=[1,1]),k.assert(F.eitherStridesOrDilationsAreOne(i,d),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${i} and dilations '${d}'`);let p=F.computeConv2DInfo(r.shape,s.shape,i,d,o,l,!0),c;return te().getBool("WEBGL_PACK_DEPTHWISECONV")&&p.strideWidth<=2&&p.outChannels/p.inChannels==1?c=new Jw(p):c=new Yw(p),n.runWebGLProgram(c,[r,s],"float32")}var pH={kernelName:Cs,backendName:"webgl",kernelFunc:dH},cH=class{constructor(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;let t=e.strideHeight,n=e.strideWidth,a=e.padInfo.top,r=e.padInfo.left,s=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 * ${s} + 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} - ${a};
|
|
|
|
if (xR < 0 || xR >= ${e.inHeight}) {
|
|
continue;
|
|
}
|
|
|
|
for (int yC = 0; yC < ${e.outWidth}; yC++) {
|
|
int xC = wC + yC * ${n} - ${r};
|
|
|
|
if (xC < 0 || xC >= ${e.inWidth}) {
|
|
continue;
|
|
}
|
|
|
|
float dyValue = getDy(b, yR, yC, d2);
|
|
float xValue = getX(b, xR, xC, d1);
|
|
dotProd += (xValue * dyValue);
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},hH=class{constructor(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;let t=e.filterHeight,n=e.filterWidth,a=e.strideHeight,r=e.strideWidth,s=t-1-e.padInfo.top,i=n-1-e.padInfo.left,o=e.outChannels/e.inChannels;this.userCode=`
|
|
const ivec2 pads = ivec2(${s}, ${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) / ${a}.0;
|
|
|
|
if (dyR < 0.0 || dyR >= ${e.outHeight}.0 || fract(dyR) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyR = int(dyR);
|
|
|
|
int wRPerm = ${t} - 1 - wR;
|
|
|
|
for (int wC = 0; wC < ${n}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${r}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
int wCPerm = ${n} - 1 - wC;
|
|
|
|
// TO DO: Vec4 over the channelMul
|
|
for (int dm = 0; dm < ${o}; dm++) {
|
|
int d2 = d1 * ${o} + dm;
|
|
float xValue = getDy(batch, idyR, idyC, d2);
|
|
float wValue = getW(wRPerm, wCPerm, d1, dm);
|
|
dotProd += xValue * wValue;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function fH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,dy:s}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,filterShape:d}=a,p=F.computeConv2DInfo(r.shape,d,i,o,u,l,!0),c=new cH(p);return n.runWebGLProgram(c,[r,s],"float32")}var mH={kernelName:tc,backendName:"webgl",kernelFunc:fH};function AH(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,filter:s}=t,{strides:i,dilations:o,pad:u,dimRoundingMode:l,inputShape:d}=a,p=F.computeConv2DInfo(d,s.shape,i,o,u,l,!0),c=new hH(p);return n.runWebGLProgram(c,[r,s],"float32")}var yH={kernelName:nc,backendName:"webgl",kernelFunc:AH},gH=class{constructor(e){this.variableNames=["X"],this.outputShape=[e,e],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
float val = coords[0] == coords[1] ? getX(coords[0]) : 0.0;
|
|
setOutput(val);
|
|
}
|
|
`}};function xH(e){let{inputs:t,backend:n}=e,{x:a}=t,r=[...a.shape,...a.shape],s=k.sizeFromShape(a.shape),i=ye({inputs:{x:a},backend:n,attrs:{shape:[s]}}),o=new gH(s),u=n.runWebGLProgram(o,[i],i.dtype),l=ye({inputs:{x:u},backend:n,attrs:{shape:r}});return n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(u),l}var bH={kernelName:ac,backendName:"webgl",kernelFunc:xH},vH=class{constructor(e){this.variableNames=["x","W"],this.outputShape=e.outShape;let{inHeight:t,inWidth:n,padInfo:a,strideHeight:r,strideWidth:s,filterHeight:i,filterWidth:o,dilationHeight:u,dilationWidth:l}=e,{top:d,left:p}=a;this.userCode=`
|
|
const ivec2 strides = ivec2(${r}, ${s});
|
|
const ivec2 pads = ivec2(${d}, ${p});
|
|
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 < ${o}; w++) {
|
|
int wIn = wBeg + w * ${l};
|
|
|
|
if (wIn >= 0 && wIn < ${n}) {
|
|
float xVal = getX(batch, hIn, wIn, d1);
|
|
float wVal = getW(h, w, d1);
|
|
|
|
float val = xVal + wVal;
|
|
if (val > curVal) {
|
|
curVal = val;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
float result = curVal;
|
|
setOutput(result);
|
|
}
|
|
`}};function wH(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s}=t,{strides:i,pad:o,dilations:u}=a,l=F.computeDilation2DInfo(r.shape,s.shape,i,o,"NHWC",u),d,p=new vH(l);d=n.runWebGLProgram(p,[r,s],"float32");let c=ye({inputs:{x:d},backend:n,attrs:{shape:l.outShape}});return n.disposeIntermediateTensorInfo(d),c}var kH={kernelName:Ou,backendName:"webgl",kernelFunc:wH};function IH(e){let{inputs:t,backend:n,attrs:a}=e,{equation:r}=a,s=t,{allDims:i,summedDims:o,idDims:u}=F.decodeEinsumEquation(r,s.length);F.checkEinsumDimSizes(i.length,u,s);let{path:l,steps:d}=F.getEinsumComputePath(o,u),p=d.length,c=null,h=i.length,m=[];for(let f=0;f<p;++f){for(let A of d[f]){let{permutationIndices:y,expandDims:g}=F.getEinsumPermutation(h,u[A]),x;F.isIdentityPermutation(y)?x=s[A]:(x=xn({inputs:{x:s[A]},backend:n,attrs:{perm:y}}),m.push(x));let v=x.shape.slice();for(let b=0;b<g.length;++b)v.splice(g[b],0,1);k.arraysEqual(x.shape,v)||(x=ye({inputs:{x},backend:n,attrs:{shape:v}}),m.push(x)),c===null?c=x:(c=GA({inputs:{a:x,b:c},backend:n}),m.push(c))}f<p-1&&(l[f]>=0&&(c=Gh({inputs:{x:c},backend:n,attrs:{axis:l[f]-(i.length-h),keepDims:!1}}),m.push(c)),h--)}for(let f of m)f!==c&&n.disposeIntermediateTensorInfo(f);return c}var SH={kernelName:ic,backendName:"webgl",kernelFunc:IH},NH="return (x >= 0.0) ? x : (exp(x) - 1.0);",TH=`
|
|
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;
|
|
`,CH=Ke({opSnippet:NH,packedOpSnippet:TH}),EH={kernelName:Co,backendName:"webgl",kernelFunc:CH},RH="return (b >= 1.0) ? a : a * (b + 1.0);",MH=`
|
|
vec4 bGTEZero = vec4(greaterThanEqual(b, vec4(0.)));
|
|
return (bGTEZero * a) + ((vec4(1.0) - bGTEZero) * (a * (b + vec4(1.0))));
|
|
`,FH=e=>{let{inputs:t,backend:n}=e,{dy:a,y:r}=t,s=te().getBool("WEBGL_PACK_BINARY_OPERATIONS")?new $d(MH,a.shape,r.shape):new Kl(RH,a.shape,r.shape);return n.runWebGLProgram(s,[a,r],a.dtype)},$H={kernelName:oc,backendName:"webgl",kernelFunc:FH},DH=`
|
|
return vec4(equal(a, b));
|
|
`,_H="return float(a == b);",OH=rn({opSnippet:_H,packedOpSnippet:DH,dtype:"bool",cpuKernelImpl:IB}),zH={kernelName:Ro,backendName:"webgl",kernelFunc:OH},PH=`
|
|
// Error function is calculated approximately with elementary function.
|
|
// See "Handbook of Mathematical Functions with Formulas,
|
|
// Graphs, and Mathematical Tables", Abramowitz and Stegun.
|
|
float p = ${F.ERF_P};
|
|
float a1 = ${F.ERF_A1};
|
|
float a2 = ${F.ERF_A2};
|
|
float a3 = ${F.ERF_A3};
|
|
float a4 = ${F.ERF_A4};
|
|
float a5 = ${F.ERF_A5};
|
|
|
|
float sign = sign(x);
|
|
x = abs(x);
|
|
float t = 1.0 / (1.0 + p * x);
|
|
return sign * (1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x));
|
|
`,LH=Ke({opSnippet:PH}),WH={kernelName:Eo,backendName:"webgl",kernelFunc:LH},Qw="return exp(x);",e6=Ke({opSnippet:Qw,packedOpSnippet:Qw,cpuKernelImpl:SB}),BH={kernelName:Rs,backendName:"webgl",kernelFunc:e6};function ZA(e){let{inputs:t,attrs:n,backend:a}=e,{dim:r}=n,{input:s}=t,i=s.shape.length,o=s.shape.slice(),u=r;return r<0&&(k.assert(-(i+1)<=r,()=>`Axis must be in the interval [${-(i+1)}, ${i}]`),u=i+r+1),o.splice(u,0,1),ye({inputs:{x:s},backend:a,attrs:{shape:o}})}var VH={kernelName:Mo,backendName:"webgl",kernelFunc:ZA},t6="return exp(x) - 1.0;",jH=Ke({opSnippet:t6,packedOpSnippet:t6,cpuKernelImpl:NB}),UH={kernelName:Fo,backendName:"webgl",kernelFunc:jH},n6=class{constructor(e,t,n){this.variableNames=["real","imag"];let a=t[1];this.outputShape=t;let r=n?`2.0 * ${Math.PI}`:`-2.0 * ${Math.PI}`,s=n?`${a}.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 = ${r};
|
|
|
|
float unaryOpComplex(float real, float expR, float imag, float expI) {
|
|
${i}
|
|
}
|
|
|
|
float mulMatDFT(int batch, int index) {
|
|
float indexRatio = float(index) / float(${a});
|
|
float exponentMultiplierTimesIndexRatio =
|
|
exponentMultiplier * indexRatio;
|
|
|
|
float result = 0.0;
|
|
|
|
for (int i = 0; i < ${a}; 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) / ${s};
|
|
}
|
|
|
|
return result;
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
setOutput(mulMatDFT(coords[0], coords[1]));
|
|
}
|
|
`}};function a6(e,t,n){let a=n.texData.get(e.dataId),r=k.sizeFromShape(e.shape),s=e.shape[e.shape.length-1],i=r/s,o=ye({inputs:{x:e},backend:n,attrs:{shape:[i,s]}}),u=o.shape,l=new n6("real",u,t),d=new n6("imag",u,t),p=[{dataId:a.complexTensorInfos.real.dataId,dtype:a.complexTensorInfos.real.dtype,shape:u},{dataId:a.complexTensorInfos.imag.dataId,dtype:a.complexTensorInfos.imag.dtype,shape:u}],c=n.runWebGLProgram(l,p,"float32"),h=n.runWebGLProgram(d,p,"float32"),m=Yr({inputs:{real:c,imag:h},backend:n});n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h);let f=ye({inputs:{x:m},backend:n,attrs:{shape:e.shape}});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(m),f}function HH(e){let{inputs:t,backend:n}=e,{input:a}=t;return a6(a,!1,n)}var GH={kernelName:lc,backendName:"webgl",kernelFunc:HH},qH=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,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}};function YA(e){let{backend:t,attrs:n}=e,{shape:a,value:r}=n,{dtype:s}=n;if(s=s||k.inferDtype(r),s==="string"){let i=k.getArrayFromDType(s,k.sizeFromShape(a));return i.fill(r),t.makeTensorInfo(a,s,i)}else{let i=new qH(a,r),o=i.getCustomSetupFunc(r);return t.runWebGLProgram(i,[],s,o)}}var XH={kernelName:zu,backendName:"webgl",kernelFunc:YA},KH=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 < ${t}) {
|
|
outputValue = getImage(coords[0], coords[1], coordX, coords[3]);
|
|
} else {
|
|
outputValue = getImage(coords[0], coords[1], coords[2], coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}},ZH={kernelName:$o,backendName:"webgl",kernelFunc:({inputs:e,backend:t})=>{let{image:n}=e,a=t,r=new KH(n.shape);return a.runWebGLProgram(r,[n],n.dtype)}},r6="return floor(x);",YH=Ke({opSnippet:r6,packedOpSnippet:r6,cpuKernelImpl:TB}),JH={kernelName:Ms,backendName:"webgl",kernelFunc:YH},QH=`
|
|
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;
|
|
}
|
|
`,eG=`
|
|
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);
|
|
`,tG=rn({opSnippet:QH,packedOpSnippet:eG,dtype:"int32"}),nG={kernelName:Fs,backendName:"webgl",kernelFunc:tG},aG=class{constructor(e){this.variableNames=["A"];let t=yn(),[n,a]=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(${a}.0, ${n}.0);
|
|
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
setOutput(floor(value * 255.0 + 0.5));
|
|
}
|
|
`}},rG=class{constructor(e){this.variableNames=["A"],this.packedInputs=!1,this.packedOutput=!0;let t=yn(),[n,a]=e;this.outputShape=e,this.userCode=`
|
|
void main() {
|
|
ivec3 coords = getOutputCoords();
|
|
int texR = coords[0];
|
|
int texC = coords[1];
|
|
int depth = coords[2];
|
|
|
|
vec4 result = vec4(0.);
|
|
|
|
for(int row=0; row<=1; row++) {
|
|
for(int col=0; col<=1; col++) {
|
|
texC = coords[1] + row;
|
|
depth = coords[2] + col;
|
|
|
|
vec2 uv = (vec2(texC, texR) + halfCR) /
|
|
vec2(${a}.0, ${n}.0);
|
|
vec4 values = ${t.texture2D}(A, uv);
|
|
float value;
|
|
if (depth == 0) {
|
|
value = values.r;
|
|
} else if (depth == 1) {
|
|
value = values.g;
|
|
} else if (depth == 2) {
|
|
value = values.b;
|
|
} else if (depth == 3) {
|
|
value = values.a;
|
|
}
|
|
|
|
result[row * 2 + col] = floor(value * 255.0 + 0.5);
|
|
}
|
|
}
|
|
|
|
${t.output} = result;
|
|
}
|
|
`}},sG={kernelName:Ec,backendName:"webgl",kernelFunc:iG},Yl;function iG(e){let{inputs:t,backend:n,attrs:a}=e,{pixels:r}=t,{numChannels:s}=a,i=typeof HTMLVideoElement!="undefined"&&r instanceof HTMLVideoElement,o=typeof HTMLImageElement!="undefined"&&r instanceof HTMLImageElement,[u,l]=i?[r.videoWidth,r.videoHeight]:[r.width,r.height],d=[l,u],p=[l,u,s];(o||i)&&(Yl==null&&(Yl=document.createElement("canvas").getContext("2d")),Yl.canvas.width=u,Yl.canvas.height=l,Yl.drawImage(r,0,0,u,l),r=Yl.canvas);let c=n.makeTensorInfo(d,"int32");n.texData.get(c.dataId).usage=oa.PIXELS,n.gpgpu.uploadPixelDataToTexture(n.getTexture(c.dataId),r);let h=te().getBool("WEBGL_PACK")?new rG(p):new aG(p),m=n.runWebGLProgram(h,[c],"int32");return n.disposeData(c.dataId),m}function oG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dataFormat:d,dilations:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=a,f=F.convertConv2DDataFormat(d),A=F.computeConv2DInfo(r.shape,s.shape,u,p,l,c,!1,f),y,g=[];if(A.filterHeight===1&&A.filterWidth===1&&A.dilationHeight===1&&A.dilationWidth===1&&A.strideHeight===1&&A.strideWidth===1&&(A.padInfo.type==="SAME"||A.padInfo.type==="VALID"))y=Gw({x:r,filter:s,convInfo:A,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else if(te().getBool("WEBGL_CONV_IM2COL")&&r.shape[0]===1)y=qw({x:r,filter:s,convInfo:A,backend:n,bias:i,activation:h,preluActivationWeights:o,leakyreluAlpha:m});else{let v=i!=null,b=o!=null,w=h==="leakyrelu",N=h?Uh(h,!1):null,C=new Hw(A,v,N,b,w),E=[r,s];if(i&&E.push(i),o&&E.push(o),w){let z=n.makeTensorInfo([],"float32",k.createScalarValue(m,"float32"));E.push(z),g.push(z)}y=n.runWebGLProgram(C,E,"float32")}let x=ye({inputs:{x:y},backend:n,attrs:{shape:A.outShape}});return g.push(y),g.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var lG={kernelName:hi,backendName:"webgl",kernelFunc:oG};function uG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:d,dimRoundingMode:p,activation:c,leakyreluAlpha:h}=a,m=[],f=d;f==null&&(f=[1,1]),k.assert(F.eitherStridesOrDilationsAreOne(u,f),()=>`Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides ${u} and dilations '${f}'`);let A=F.computeConv2DInfo(r.shape,s.shape,u,f,l,p,!0),y=te().getBool("WEBGL_PACK_DEPTHWISECONV")&&A.strideWidth<=2&&A.outChannels/A.inChannels==1,g=c?Uh(c,y):null,x=[r,s],v=i!=null,b=o!=null,w=c==="leakyrelu";if(v&&x.push(i),b&&x.push(o),w){let E=n.makeTensorInfo([],"float32",k.createScalarValue(h,"float32"));x.push(E),m.push(E)}let N;y?N=new Jw(A,v,g,b,w):N=new Yw(A,v,g,b,w);let C=n.runWebGLProgram(N,x,"float32");return m.forEach(E=>n.disposeIntermediateTensorInfo(E)),C}var dG={kernelName:fi,backendName:"webgl",kernelFunc:uG},pG=class{constructor(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;let a=ut(t.length),r=ut(n.length),s=this.sliceDim>1?"strides[j]":"strides";this.userCode=`
|
|
${a} strides = ${a}(${this.strides});
|
|
void main() {
|
|
${r} coords = getOutputCoords();
|
|
int flattenIndex = 0;
|
|
for (int j = 0; j < ${this.sliceDim}; j++) {
|
|
int index = round(getIndices(coords[0], j));
|
|
flattenIndex += index * ${s};
|
|
}
|
|
setOutput(getX(flattenIndex, coords[1]));
|
|
}
|
|
`}};function cG(e){let{inputs:t,backend:n}=e,{params:a,indices:r}=t,s=r.shape,i=s[s.length-1],o=k.sizeFromShape(a.shape),[u,l,d,p]=F.prepareAndValidate(a,r),c=ye({inputs:{x:r},backend:n,attrs:{shape:[l,i]}}),h=ye({inputs:{x:a},backend:n,attrs:{shape:[k.sizeFromShape(a.shape)/d,d]}});if(n.shouldExecuteOnCPU([a,r])||a.dtype==="string"){let y=n.readSync(r.dataId),g=n.bufferSync(a),x=CB(y,g,a.dtype,l,i,d,p,a.shape,o);return n.makeTensorInfo(u,a.dtype,x.values)}let m=new pG(i,p,[l,d]),f=n.runWebGLProgram(m,[h,c],h.dtype),A=ye({inputs:{x:f},backend:n,attrs:{shape:u}});return n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(f),A}var hG={kernelName:_o,backendName:"webgl",kernelFunc:cG},fG=class{constructor(e,t){this.variableNames=["A","indices"],this.outputShape=t,this.rank=t.length;let n=ut(this.rank),a=mG(e,2);this.userCode=`
|
|
void main() {
|
|
${n} resRC = getOutputCoords();
|
|
setOutput(getA(${a}));
|
|
}
|
|
`}};function mG(e,t){let n=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[];for(let r=0;r<e.length;r++)r===2?a.push("int(getIndices(resRC.x, resRC.z))"):a.push(`${n[r]}`);return a.join()}function AG(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,indices:s}=t,{axis:i,batchDims:o}=a,u=k.parseAxisParam(i,r.shape)[0],l=F.segment_util.collectGatherOpShapeInfo(r,s,u,o),d=k.sizeFromShape(s.shape),p=[],c=ye({inputs:{x:r},backend:n,attrs:{shape:[l.batchSize,l.outerSize,l.dimSize,l.sliceSize]}}),h=ye({inputs:{x:s},backend:n,attrs:{shape:[l.batchSize,d/l.batchSize]}});p.push(c),p.push(h);let m=[l.batchSize,l.outerSize,d/l.batchSize,l.sliceSize];if(n.shouldExecuteOnCPU([r,s])||r.dtype==="string"){let g=n.bufferSync(h),x=n.bufferSync(c),v=EB(x,g,m);return p.forEach(b=>n.disposeIntermediateTensorInfo(b)),n.makeTensorInfo(l.outputShape,v.dtype,v.values)}let f=new fG(c.shape,m),A=n.runWebGLProgram(f,[c,h],c.dtype);p.push(A);let y=ye({inputs:{x:A},backend:n,attrs:{shape:l.outputShape}});return p.forEach(g=>n.disposeIntermediateTensorInfo(g)),y}var yG={kernelName:Do,backendName:"webgl",kernelFunc:AG},gG="return float(a > b);",xG=`
|
|
return vec4(greaterThan(a, b));
|
|
`,bG=rn({opSnippet:gG,packedOpSnippet:xG,cpuKernelImpl:RB,dtype:"bool"}),vG={kernelName:Oo,backendName:"webgl",kernelFunc:bG},wG="return float(a >= b);",kG=`
|
|
return vec4(greaterThanEqual(a, b));
|
|
`,IG=rn({opSnippet:wG,packedOpSnippet:kG,dtype:"bool",cpuKernelImpl:MB}),SG={kernelName:Ds,backendName:"webgl",kernelFunc:IG};function NG(e){let{inputs:t,backend:n}=e,{input:a}=t;return a6(a,!0,n)}var TG={kernelName:uc,backendName:"webgl",kernelFunc:NG},CG="return float(!isnan(x) && !isinf(x));",EG=Ke({opSnippet:CG,dtype:"bool"}),RG={kernelName:zo,backendName:"webgl",kernelFunc:EG},MG="return float(isinf(x));",FG=Ke({opSnippet:MG,dtype:"bool"}),$G={kernelName:Po,backendName:"webgl",kernelFunc:FG},DG="return float(isnan(x));",_G=Ke({opSnippet:DG,dtype:"bool"}),OG={kernelName:Lo,backendName:"webgl",kernelFunc:_G},zG="return float(a < b);",PG=`
|
|
return vec4(lessThan(a, b));
|
|
`,LG=rn({opSnippet:zG,packedOpSnippet:PG,cpuKernelImpl:FB,dtype:"bool"}),WG={kernelName:Wo,backendName:"webgl",kernelFunc:LG},BG="return float(a <= b);",VG=`
|
|
return vec4(lessThanEqual(a, b));
|
|
`,jG=rn({opSnippet:BG,packedOpSnippet:VG,cpuKernelImpl:$B,dtype:"bool"}),UG={kernelName:Bo,backendName:"webgl",kernelFunc:jG};function HG(e){let{backend:t,attrs:n}=e,{start:a,stop:r,num:s}=n,i=DB(a,r,s);return t.makeTensorInfo([i.length],"float32",i)}var GG={kernelName:pc,backendName:"webgl",kernelFunc:HG},qG=`if (x < 0.0) return NAN;
|
|
return log(x);`,XG=`
|
|
vec4 result = log(x);
|
|
vec4 isNaN = vec4(lessThan(x, vec4(0.0)));
|
|
result.r = isNaN.r == 1.0 ? NAN : result.r;
|
|
result.g = isNaN.g == 1.0 ? NAN : result.g;
|
|
result.b = isNaN.b == 1.0 ? NAN : result.b;
|
|
result.a = isNaN.a == 1.0 ? NAN : result.a;
|
|
|
|
return result;
|
|
`,KG=Ke({opSnippet:qG,packedOpSnippet:XG,cpuKernelImpl:_B}),ZG={kernelName:zs,backendName:"webgl",kernelFunc:KG},YG="return log(1.0 + x);",JG=Ke({opSnippet:YG}),QG={kernelName:Vo,backendName:"webgl",kernelFunc:JG},eq="return float(a >= 1.0 && b >= 1.0);",tq=`
|
|
return vec4(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) *
|
|
vec4(greaterThanEqual(b, vec4(1.0))));
|
|
`,nq=rn({opSnippet:eq,packedOpSnippet:tq,dtype:"bool"}),aq={kernelName:jo,backendName:"webgl",kernelFunc:nq},rq="return float(!(x >= 1.0));",sq=Ke({opSnippet:rq}),iq={kernelName:Pu,backendName:"webgl",kernelFunc:sq},oq="return float(a >= 1.0 || b >= 1.0);",lq=`
|
|
return min(
|
|
vec4(greaterThanEqual(a, vec4(1.0))) +
|
|
vec4(greaterThanEqual(b, vec4(1.0))),
|
|
vec4(1.0));
|
|
`,uq=rn({opSnippet:oq,packedOpSnippet:lq,dtype:"bool"}),dq={kernelName:Lu,backendName:"webgl",kernelFunc:uq},pq=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[];let s=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
int d = coords[3];
|
|
float x = getX(b, r, c, d);
|
|
float sum = 0.0;
|
|
for (int j = -${s}; j <= ${s}; j++) {
|
|
int idx = d + j;
|
|
if (idx >= 0 && idx <= ${i}) {
|
|
float z = getX(b, r, c, idx);
|
|
sum += z * z;
|
|
}
|
|
}
|
|
float val = x * ${o};
|
|
setOutput(val);
|
|
}
|
|
`}},cq=class{constructor(e,t,n,a,r){this.variableNames=["x"],this.outputShape=[],this.packedInputs=!0,this.packedOutput=!0;let s=t,i=e[3]-1;this.outputShape=e;let o,u=`float(${n}) + float(${a}) * sum`;r===.5?o=`inversesqrt(${u})`:r===1?o=`1.0/(${u})`:o=`exp(log(${u}) * float(-${r}));`,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords.x;
|
|
int r = coords.y;
|
|
int c = coords.z;
|
|
int d = coords.w;
|
|
|
|
bool hasNextCol = d < ${this.outputShape[3]};
|
|
bool hasNextRow = c < ${this.outputShape[2]};
|
|
|
|
vec4 sum = vec4(0.);
|
|
vec4 xFragAtOutputCoords = getX(b, r, c, d);
|
|
|
|
vec4 xAtOutputCoords = vec4(
|
|
getChannel(xFragAtOutputCoords, vec2(c, d)),
|
|
hasNextCol ?
|
|
getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,
|
|
hasNextRow ?
|
|
getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0
|
|
);
|
|
|
|
int firstChannel = d - ${s};
|
|
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 = - ${s}; j <= ${s}; j++) {
|
|
ivec2 idx = depth + j;
|
|
bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));
|
|
bvec2 belowUpperBound = lessThanEqual(idx, ivec2(${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 * ${o};
|
|
setOutput(result);
|
|
}
|
|
`}},hq=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{depthRadius:s,bias:i,alpha:o,beta:u}=a,l=te().getBool("WEBGL_PACK_NORMALIZATION")?new cq(r.shape,s,i,o,u):new pq(r.shape,s,i,o,u);return n.runWebGLProgram(l,[r],r.dtype)},fq={kernelName:Wu,backendName:"webgl",kernelFunc:hq},mq=class{constructor(e,t,n,a,r){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=a,this.beta=r,this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float result = 0.0;
|
|
for (int d = 0; d < ${this.depth}; ++d) {
|
|
int depthBegin = int(max(0.0, float(d - ${t})));
|
|
int depthEnd = int(min(float(${this.depth}),
|
|
float(d + ${t} + 1)));
|
|
|
|
const int MIN_DEPTH_BEGIN = 0;
|
|
const int MAX_DEPTH_END = ${this.depth};
|
|
|
|
float norm = 0.0;
|
|
for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd) {
|
|
norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
|
|
norm = float(${a}) * norm + float(${n});
|
|
|
|
for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){
|
|
if (k < depthBegin){
|
|
continue;
|
|
}
|
|
else if (k >= depthBegin && k < depthEnd){
|
|
float dyi = -2.0 * float(${a})
|
|
* float(${r})
|
|
* getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)
|
|
/ norm;
|
|
if (k == d) {
|
|
dyi += pow(norm, -1.0 * ${r});
|
|
}
|
|
if (k == coords[3]) {
|
|
dyi *= getDy(b, r, c, d);
|
|
result += dyi;
|
|
}
|
|
}
|
|
else {
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`}},Aq=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r,y:s,dy:i}=t,{depthRadius:o,bias:u,alpha:l,beta:d}=a,p=new mq(r.shape,o,u,l,d);return n.runWebGLProgram(p,[r,s,i],r.dtype)},yq={kernelName:cc,backendName:"webgl",kernelFunc:Aq};function gq(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=ye({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Wi(i,e.dtype,"max",a),u=ye({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),u}function s6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reductionIndices:s,keepDims:i}=a,o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=u,d=F.getAxesPermutation(l,o),p=d!=null,c=n.shouldExecuteOnCPU([r]),h=r;if(p){if(c){let g=n.texData.get(h.dataId).values,x=new Array(o);for(let w=0;w<x.length;w++)x[w]=r.shape[d[w]];let v=UA(g,r.shape,r.dtype,d,x);h=n.makeTensorInfo(x,r.dtype);let b=n.texData.get(h.dataId);b.values=v}else h=Hh(r,d,n);l=F.getInnerMostAxes(l.length,o)}F.assertAxesAreInnerMostDims("max",l,o);let[m,f]=F.computeOutAndReduceShapes(h.shape,l),A=m;i&&(A=F.expandShapeToKeepDim(m,u));let y;if(c){let g=n.texData.get(h.dataId).values,x=OB(g,k.sizeFromShape(f),A,r.dtype);y=n.makeTensorInfo(A,r.dtype);let v=n.texData.get(y.dataId);v.values=x}else y=gq(h,f,A,n);return p&&n.disposeIntermediateTensorInfo(h),y}var xq={kernelName:Ps,backendName:"webgl",kernelFunc:s6},bq=Iw+`
|
|
return max(a, b);
|
|
`,vq=`
|
|
vec4 result = vec4(max(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+jh+`
|
|
return result;
|
|
`,wq=rn({opSnippet:bq,packedOpSnippet:vq,cpuKernelImpl:zB}),kq={kernelName:Ls,backendName:"webgl",kernelFunc:wq};function Iq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t;Vl(r,"maxPool");let{filterSize:s,strides:i,pad:o,dimRoundingMode:u}=a,l=1;k.assert(F.eitherStridesOrDilationsAreOne(i,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${i} and dilations '${l}'`);let d=F.computePool2DInfo(r.shape,s,i,l,o,u);if(d.filterWidth===1&&d.filterHeight===1&&k.arraysEqual(d.inShape,d.outShape))return Gn({inputs:{x:r},backend:n});let p=new Dd(d,"max",!1);return n.runWebGLProgram(p,[r],r.dtype)}var Sq={kernelName:Ws,backendName:"webgl",kernelFunc:Iq};function Nq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{filterSize:s,strides:i,pad:o,dataFormat:u,dimRoundingMode:l}=a,d=[1,1,1],p=F.computePool3DInfo(r.shape,s,i,d,o,l,u),c=new qA(p,"max",!1);return n.runWebGLProgram(c,[r],r.dtype)}var Tq={kernelName:Bu,backendName:"webgl",kernelFunc:Nq},Cq=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideHeight,n=e.strideWidth,a=e.dilationHeight,r=e.effectiveFilterHeight,s=e.effectiveFilterWidth,i=r-1-e.padInfo.top,o=s-1-e.padInfo.left,u=r*s-1;this.userCode=`
|
|
const ivec2 pads = ivec2(${i}, ${o});
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
|
|
ivec2 dyRCCorner = coords.yz - pads;
|
|
int dyRCorner = dyRCCorner.x;
|
|
int dyCCorner = dyRCCorner.y;
|
|
|
|
// Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).
|
|
// ? = to be determined. : = across all values in that axis.
|
|
float dotProd = 0.0;
|
|
for (int wR = 0; wR < ${r};
|
|
wR += ${a}) {
|
|
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 < ${s}; wC++) {
|
|
float dyC = float(dyCCorner + wC) / ${n}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(b, idyR, idyC, d);
|
|
int maxPosValue = ${u} - int(getMaxPos(b, idyR, idyC, d));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue = wR * ${s} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}},Eq=class{constructor(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;let t=e.strideDepth,n=e.strideHeight,a=e.strideWidth,r=e.dilationDepth,s=e.dilationHeight,i=e.dilationWidth,o=e.effectiveFilterDepth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,d=o-1-e.padInfo.front,p=u-1-e.padInfo.top,c=l-1-e.padInfo.left,h=o*u*l-1;this.userCode=`
|
|
const ivec3 pads = ivec3(${d}, ${p}, ${c});
|
|
|
|
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 < ${o};
|
|
wD += ${r}) {
|
|
float dyD = float(dyDCorner + wD) / ${t}.0;
|
|
|
|
if (dyD < 0.0 || dyD >= ${e.outDepth}.0 || fract(dyD) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyD = int(dyD);
|
|
|
|
for (int wR = 0; wR < ${u};
|
|
wR += ${s}) {
|
|
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 < ${l};
|
|
wC += ${i}) {
|
|
float dyC = float(dyCCorner + wC) / ${a}.0;
|
|
|
|
if (dyC < 0.0 || dyC >= ${e.outWidth}.0 ||
|
|
fract(dyC) > 0.0) {
|
|
continue;
|
|
}
|
|
int idyC = int(dyC);
|
|
|
|
float dyValue = getDy(batch, idyD, idyR, idyC, ch);
|
|
int maxPosValue = ${h} -
|
|
int(getMaxPos(batch, idyD, idyR, idyC, ch));
|
|
|
|
// Get the current value, check it against the value from the
|
|
// position matrix.
|
|
int curPosValue =
|
|
wD * ${u} * ${l} +
|
|
wR * ${l} + wC;
|
|
float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);
|
|
|
|
dotProd += dyValue * mask;
|
|
}
|
|
}
|
|
}
|
|
setOutput(dotProd);
|
|
}
|
|
`}};function Rq(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s}=t,i=s,{filterSize:o,strides:u,pad:l,dimRoundingMode:d}=a,p=[1,1,1],c=F.computePool3DInfo(i.shape,o,u,p,l,d),h=new qA(c,"max",!0),m=n.runWebGLProgram(h,[i],i.dtype),f=new Eq(c),A=n.runWebGLProgram(f,[r,m],i.dtype);return n.disposeIntermediateTensorInfo(m),A}var Mq={kernelName:fc,backendName:"webgl",kernelFunc:Rq};function Fq(e){let{inputs:t,backend:n,attrs:a}=e,{dy:r,input:s,output:i}=t,o=s;Vl([s,i],"maxPoolGrad");let{filterSize:u,strides:l,pad:d,dimRoundingMode:p}=a,c=F.computePool2DInfo(o.shape,u,l,1,d,p),h=!0,m=new Dd(c,"max",h),f=n.runWebGLProgram(m,[o],o.dtype),A=new Cq(c),y=n.runWebGLProgram(A,[r,f],o.dtype);return n.disposeIntermediateTensorInfo(f),y}var $q={kernelName:hc,backendName:"webgl",kernelFunc:Fq};function Dq(e,t,n,a){let r=new Dd(n,"max",!1),s=a.runWebGLProgram(r,[e],"float32");r=new Dd(n,"max",!0,!0,t);let i=a.runWebGLProgram(r,[e],"float32");return[s,i]}var _q={kernelName:mc,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{filterSize:r,strides:s,pad:i,includeBatchInIndex:o}=t,u=n;k.assert(a.shape.length===4,()=>`Error in maxPool: input must be rank 4 but got rank ${a.shape.length}.`);let l=[1,1];k.assert(F.eitherStridesOrDilationsAreOne(s,l),()=>`Error in maxPool: Either strides or dilations must be 1. Got strides ${s} and dilations '${l}'`);let d=F.computePool2DInfo(a.shape,r,s,l,i),[p,c]=Dq(a,o,d,u);return[p,c]}};function Oq(e,t,n,a){let r=k.sizeFromShape(t),s=k.sizeFromShape(e.shape)/r,i=ye({inputs:{x:e},attrs:{shape:[s,r]},backend:a}),o=Wi(i,"float32","mean",a),u=ye({inputs:{x:o},attrs:{shape:n},backend:a});return a.disposeIntermediateTensorInfo(i),a.disposeIntermediateTensorInfo(o),u}var zq={kernelName:Bs,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{x:a}=e,{keepDims:r,axis:s}=t,i=n,o=a.shape.length,u=k.parseAxisParam(s,a.shape),l=u,d=F.getAxesPermutation(l,o),p=d!=null,c=i.shouldExecuteOnCPU([a]),h=[],m=a;if(p){if(c){let x=i.texData.get(m.dataId).values,v=new Array(o);for(let N=0;N<v.length;N++)v[N]=a.shape[d[N]];let b=UA(x,a.shape,a.dtype,d,v);m=i.makeTensorInfo(v,a.dtype);let w=i.texData.get(m.dataId);w.values=b}else m=Hh(a,d,i);h.push(m),l=F.getInnerMostAxes(l.length,o)}F.assertAxesAreInnerMostDims("sum",l,o);let[f,A]=F.computeOutAndReduceShapes(m.shape,l),y=f;r&&(y=F.expandShapeToKeepDim(f,u));let g=Oq(m,A,y,i);for(let x of h)i.disposeIntermediateTensorInfo(x);return g}};function Pq(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,u=k.parseAxisParam(s,r.shape),l=u,d=F.getAxesPermutation(l,o),p=r;d!=null&&(p=xn({inputs:{x:r},backend:n,attrs:{perm:d}}),l=F.getInnerMostAxes(l.length,r.shape.length)),F.assertAxesAreInnerMostDims("min",l,o);let[c,h]=F.computeOutAndReduceShapes(p.shape,l),m=k.sizeFromShape(h),f=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,m]}}),A=Wi(f,f.dtype,"min",n),y;if(i){let g=F.expandShapeToKeepDim(c,u);y=ye({inputs:{x:A},backend:n,attrs:{shape:g}})}else y=ye({inputs:{x:A},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(f),n.disposeIntermediateTensorInfo(A),d!=null&&n.disposeIntermediateTensorInfo(p),y}var Lq={kernelName:Vs,backendName:"webgl",kernelFunc:Pq},Wq=Iw+`
|
|
return min(a, b);
|
|
`,Bq=`
|
|
vec4 result = vec4(min(a, b));
|
|
vec4 isNaN = min(vec4(isnan(a)) + vec4(isnan(b)), vec4(1.0));
|
|
`+jh+`
|
|
return result;
|
|
`,Vq=rn({opSnippet:Wq,packedOpSnippet:Bq,cpuKernelImpl:PB}),jq={kernelName:js,backendName:"webgl",kernelFunc:Vq},Uq=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((l,d)=>l[0]+e[d]+l[1]);let a=e.length,r=ut(a),s=t.map(l=>l[0]).join(","),i=t.map((l,d)=>l[0]+e[d]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a),u=n==="reflect"?0:1;if(a===1){this.userCode=`
|
|
int start = ${s};
|
|
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=`
|
|
${r} start = ${r}(${s});
|
|
${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
for (int i = 0; i < ${a}; 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};
|
|
}
|
|
}
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
`}},Hq=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((h,m)=>h[0]+e[m]+h[1]);let a=e.length,r=ut(a),s=t.map(h=>h[0]).join(","),i=t.map((h,m)=>h[0]+e[m]).join(","),o=gn("rc",a),u=gn("source",a),l=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${u.slice(-2).join()})`,p=n==="reflect"?0:1,c="";if(a===1){let h=`
|
|
${r} source = rc;
|
|
if (source < start) {
|
|
source = start * 2 - source - ${p};
|
|
} else if (source >= end) {
|
|
source = (end - 1) * 2 - source + ${p};
|
|
}
|
|
source -= start;
|
|
`;c=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${u.join()}), ${d});
|
|
${o[a-1]} += 1;
|
|
if(${l}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${u.join()}), ${d});
|
|
}
|
|
`}else{let h=`
|
|
${r} source = rc;
|
|
${r} lt = ${r}(lessThan(source, start));
|
|
${r} gte = ${r}(greaterThanEqual(source, end));
|
|
${r} orig = 1 - (lt + gte);
|
|
source = orig * source +
|
|
lt * (start * 2 - source - ${p}) +
|
|
gte * ((end - 1) * 2 - source + ${p});
|
|
source -= start;
|
|
`;c=`
|
|
${r} rc = outputLoc;
|
|
${h}
|
|
result[0] = getChannel(getX(${u.join()}), ${d});
|
|
${o[a-1]} += 1;
|
|
if(${l}) {
|
|
${h}
|
|
result[1] = getChannel(getX(${u.join()}), ${d});
|
|
}
|
|
rc = outputLoc;
|
|
${o[a-2]} += 1;
|
|
if(${o[a-2]} < ${this.outputShape[a-2]}) {
|
|
${h}
|
|
result[2] = getChannel(getX(${u.join()}), ${d});
|
|
${o[a-1]} += 1;
|
|
if(${l}) {
|
|
${h}
|
|
result[3] = getChannel(getX(${u.join()}), ${d});
|
|
}
|
|
}
|
|
`}this.userCode=`
|
|
const ${r} start = ${r}(${s});
|
|
const ${r} end = ${r}(${i});
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${c}
|
|
setOutput(result);
|
|
}
|
|
`}},Gq=({inputs:e,backend:t,attrs:n})=>{let{x:a}=e,{paddings:r,mode:s}=n,i=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new Hq(a.shape,r,s):new Uq(a.shape,r,s);return t.runWebGLProgram(i,[a],a.dtype)},qq={kernelName:Us,backendName:"webgl",kernelFunc:Gq},Xq=`if (b == 0.0) return NAN;
|
|
return mod(a, b);`,Kq=`
|
|
vec4 result = mod(a, b);
|
|
vec4 isNaN = vec4(equal(b, vec4(0.0)));
|
|
`+jh+`
|
|
return result;
|
|
`,Zq=rn({opSnippet:Xq,packedOpSnippet:Kq}),Yq={kernelName:Uo,backendName:"webgl",kernelFunc:Zq},Jq=class{constructor(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],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,n)=>{this.seedLoc==null&&(this.seedLoc=t.getUniformLocation(n,"seed")),t.gl.uniform1f(this.seedLoc,e)}}},Qq=`
|
|
if (a == b) {
|
|
return 1.0;
|
|
};
|
|
return a / b;`,eX=`
|
|
// 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;
|
|
`,i6=rn({opSnippet:Qq,packedOpSnippet:eX,checkOutOfBounds:!0}),tX={kernelName:Es,backendName:"webgl",kernelFunc:i6},o6="return a - b;",l6=rn({opSnippet:o6,packedOpSnippet:o6,supportsComplex:!0,cpuKernelImpl:JB}),nX={kernelName:li,backendName:"webgl",kernelFunc:l6};function u6(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{dim:s}=a,i=k.parseAxisParam([s],r.shape),o=s6({inputs:{x:r},backend:n,attrs:{reductionIndices:i,keepDims:!1}}),u=F.expandShapeToKeepDim(o.shape,i),l=ye({inputs:{x:o},backend:n,attrs:{shape:u}}),d=l6({inputs:{a:r,b:l},backend:n}),p=e6({inputs:{x:d},backend:n}),c=Gh({inputs:{x:p},backend:n,attrs:{axis:i,keepDims:!1}}),h=ye({inputs:{x:c},backend:n,attrs:{shape:u}}),m=i6({inputs:{a:p,b:h},backend:n});return n.disposeIntermediateTensorInfo(o),n.disposeIntermediateTensorInfo(l),n.disposeIntermediateTensorInfo(d),n.disposeIntermediateTensorInfo(p),n.disposeIntermediateTensorInfo(c),n.disposeIntermediateTensorInfo(h),m}var aX={kernelName:ii,backendName:"webgl",kernelFunc:u6};function rX(e){let{inputs:t,backend:n,attrs:a}=e,{logits:r}=t,{numSamples:s,seed:i,normalized:o}=a,u=o?r:u6({inputs:{logits:r},backend:n,attrs:{dim:r.shape.length-1}}),l=u.shape[0],d=u.shape[1],p=new Jq(l,d,s),c=p.getCustomSetupFunc(i),h=n.runWebGLProgram(p,[u],"int32",c);return o||n.disposeIntermediateTensorInfo(u),h}var sX={kernelName:Ac,backendName:"webgl",kernelFunc:rX},d6="return -x;";function iX(e){let{inputs:t,backend:n}=e,{x:a}=t;if(n.shouldExecuteOnCPU([a])){let s=n.texData.get(a.dataId),[i,o]=WB(s.values,a.shape,a.dtype);return n.makeTensorInfo(o,a.dtype,i)}let r;return te().getBool("WEBGL_PACK_UNARY_OPERATIONS")?r=new ql(a.shape,d6):r=new Zr(a.shape,d6),n.runWebGLProgram(r,[a],a.dtype)}var oX={kernelName:Ho,backendName:"webgl",kernelFunc:iX},lX=Ka.nonMaxSuppressionV3Impl;function uX(e){F.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u}=a,l=n.readSync(r.dataId),d=n.readSync(s.dataId),{selectedIndices:p}=lX(l,d,i,o,u);return n.makeTensorInfo([p.length],"int32",new Int32Array(p))}var dX={kernelName:qo,backendName:"webgl",kernelFunc:uX},pX=Ka.nonMaxSuppressionV4Impl;function cX(e){F.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,padToMaxOutputSize:l}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),{selectedIndices:c,validOutputs:h}=pX(d,p,i,o,u,l);return[n.makeTensorInfo([c.length],"int32",new Int32Array(c)),n.makeTensorInfo([],"int32",new Int32Array([h]))]}var hX={kernelName:Xo,backendName:"webgl",kernelFunc:cX},fX=Ka.nonMaxSuppressionV5Impl;function mX(e){F.warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead");let{inputs:t,backend:n,attrs:a}=e,{boxes:r,scores:s}=t,{maxOutputSize:i,iouThreshold:o,scoreThreshold:u,softNmsSigma:l}=a,d=n.readSync(r.dataId),p=n.readSync(s.dataId),c=i,h=o,m=u,f=l,{selectedIndices:A,selectedScores:y}=fX(d,p,c,h,m,f);return[n.makeTensorInfo([A.length],"int32",new Int32Array(A)),n.makeTensorInfo([y.length],"float32",new Float32Array(y))]}var AX={kernelName:Ko,backendName:"webgl",kernelFunc:mX},yX=class{constructor(e,t,n,a){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode=`
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int index = round(getIndices(coords.x));
|
|
setOutput(mix(float(${a}), float(${n}),
|
|
float(index == coords.y)));
|
|
}
|
|
`}},gX=e=>{let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,u=k.sizeFromShape(r.shape),l=new yX(u,s,i,o),d=ye({inputs:{x:r},backend:n,attrs:{shape:[u]}}),p=n.runWebGLProgram(l,[d],r.dtype);n.disposeIntermediateTensorInfo(d);let c=[...r.shape,s],h=ye({inputs:{x:p},backend:n,attrs:{shape:c}});return n.disposeIntermediateTensorInfo(p),h},xX={kernelName:Gs,backendName:"webgl",kernelFunc:gX};function Yh(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="complex64"){let r=Od({inputs:{input:a},backend:n}),s=Yh({inputs:{x:r},backend:n}),i=Zh({inputs:{input:a},backend:n}),o=Yh({inputs:{x:i},backend:n}),u=Yr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return YA({attrs:{shape:a.shape,dtype:a.dtype,value:a.dtype==="string"?"":0},backend:n})}var bX={kernelName:hl,backendName:"webgl",kernelFunc:Yh};function p6(e){let{inputs:t,backend:n}=e,{x:a}=t;if(a.dtype==="string")throw new Error("onesLike is not supported under string dtype");if(a.dtype==="complex64"){let r=Od({inputs:{input:a},backend:n}),s=p6({inputs:{x:r},backend:n}),i=Zh({inputs:{input:a},backend:n}),o=Yh({inputs:{x:i},backend:n}),u=Yr({inputs:{real:s,imag:o},backend:n});return n.disposeIntermediateTensorInfo(r),n.disposeIntermediateTensorInfo(s),n.disposeIntermediateTensorInfo(i),n.disposeIntermediateTensorInfo(o),u}else return YA({attrs:{shape:a.shape,dtype:a.dtype,value:1},backend:n})}var vX={kernelName:Zo,backendName:"webgl",kernelFunc:p6};function wX(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return ZA({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(d=>{let p=ZA({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),l=Uw({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeIntermediateTensorInfo(d)),l}var kX={kernelName:Yo,backendName:"webgl",kernelFunc:wX},IX=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=t.map((u,l)=>u[0]+e[l]+u[1]);let a=e.length,r=ut(a),s=t.map(u=>u[0]).join(","),i=t.map((u,l)=>u[0]+e[l]).join(","),o=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,a);if(a===1){this.userCode=`
|
|
int start = ${s};
|
|
int end = ${i};
|
|
uniform float value;
|
|
|
|
void main() {
|
|
int outC = getOutputCoords();
|
|
if (outC < start || outC >= end) {
|
|
setOutput(value);
|
|
} else {
|
|
setOutput(getX(outC - start));
|
|
}
|
|
}
|
|
`;return}this.userCode=`
|
|
${r} start = ${r}(${s});
|
|
${r} end = ${r}(${i});
|
|
uniform float value;
|
|
|
|
void main() {
|
|
${r} outC = getOutputCoords();
|
|
if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {
|
|
setOutput(value);
|
|
} else {
|
|
${r} coords = outC - start;
|
|
setOutput(getX(${o}));
|
|
}
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},SX=class{constructor(e,t,n){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=t.map((m,f)=>m[0]+e[f]+m[1]);let a=e.length,r=ut(a),s=t.map(m=>m[0]).join(","),i=t.map((m,f)=>m[0]+e[f]).join(","),o=gn("rc",a),u=gn("source",a),l=`${o[a-1]} < ${this.outputShape[a-1]}`,d=a===1?"source":`vec2(${u.slice(-2).join()})`,p=[`${r} rc = outputLoc;`,`${o[a-1]} += 1;
|
|
if(${l}) {
|
|
`,a===1?"":`}
|
|
rc = outputLoc;
|
|
${o[a-2]} += 1;
|
|
if(${o[a-2]} < ${this.outputShape[a-2]}) {`,a===1?"":` ${o[a-1]} += 1;
|
|
if(${l}) {`],c=a===1?"rc < start || rc >= end":"any(lessThan(rc, start)) || any(greaterThanEqual(rc, end))",h="";for(let m=0,f=a===1?2:4;m<f;m++)h+=`
|
|
${p[m]}
|
|
if (${c}) {
|
|
result[${m}] = float(value);
|
|
} else {
|
|
${r} source = rc - start;
|
|
result[${m}] = getChannel(getX(${u.join()}), ${d});
|
|
}
|
|
`;h+=a===1?"} ":"}}",this.userCode=`
|
|
const ${r} start = ${r}(${s});
|
|
const ${r} end = ${r}(${i});
|
|
uniform float value;
|
|
|
|
void main() {
|
|
${r} outputLoc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
${h}
|
|
setOutput(result);
|
|
}
|
|
`}getCustomSetupFunc(e){return(t,n)=>{this.valueLoc==null&&(this.valueLoc=t.getUniformLocationNoThrow(n,"value")),t.gl.uniform1f(this.valueLoc,e)}}},c6=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{paddings:s,constantValue:i}=a,o=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new SX(r.shape,s,i):new IX(r.shape,s,i),u=o.getCustomSetupFunc(i);return n.runWebGLProgram(o,[r],r.dtype,u)},NX={kernelName:qs,backendName:"webgl",kernelFunc:c6},TX=`
|
|
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);
|
|
`,CX=`
|
|
// 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));
|
|
`+jh+`
|
|
return result;
|
|
`,EX=rn({opSnippet:TX,packedOpSnippet:CX}),RX={kernelName:Xs,backendName:"webgl",kernelFunc:EX};function MX(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{axis:s,keepDims:i}=a,o=r.shape.length,u=[],l=k.parseAxisParam(s,r.shape),d=l,p=F.getAxesPermutation(d,o),c=r;p!=null&&(c=xn({inputs:{x:r},backend:n,attrs:{perm:p}}),d=F.getInnerMostAxes(d.length,o),u.push(c)),F.assertAxesAreInnerMostDims("prod",d,o);let h;if(n.shouldExecuteOnCPU([c])){let m=n.texData.get(c.dataId).values,{outVals:f,outShape:A,outDtype:y}=VB(c.shape,c.dtype,m,d);h=n.makeTensorInfo(A,y,f)}else{let[m,f]=F.computeOutAndReduceShapes(c.shape,d),A=k.sizeFromShape(f),y=ye({inputs:{x:c},backend:n,attrs:{shape:[-1,A]}}),g=_c(r.dtype),x=Wi(y,g,"prod",n);h=ye({inputs:{x},backend:n,attrs:{shape:m}}),u.push(y),u.push(x)}if(i){u.push(h);let m=F.expandShapeToKeepDim(h.shape,l);h=ye({inputs:{x:h},backend:n,attrs:{shape:m}})}return u.forEach(m=>n.disposeIntermediateTensorInfo(m)),h}var FX={kernelName:Jo,backendName:"webgl",kernelFunc:MX},h6=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=jB(a,r,s,i);return t.makeTensorInfo([o.length],i,o)},$X={kernelName:Vu,backendName:"webgl",kernelFunc:h6},DX="return 1.0 / x;",_X=Ke({opSnippet:DX}),OX={kernelName:Qo,backendName:"webgl",kernelFunc:_X},zX=Na+`
|
|
return (x < 0.0) ? 0.0 : x;
|
|
`,PX=`
|
|
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;
|
|
`,LX=Ke({opSnippet:zX,packedOpSnippet:PX}),WX={kernelName:Zs,backendName:"webgl",kernelFunc:LX},BX=Na+`
|
|
return (x < 0.0) ? 0.0 : min(6.0, x);
|
|
`,VX=`
|
|
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;
|
|
`,jX=Ke({opSnippet:BX,packedOpSnippet:VX}),UX={kernelName:Js,backendName:"webgl",kernelFunc:jX},HX=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,u]=e;this.outputShape=[s,t,n,u];let l=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC - vec2(0.5)":p="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${l[0]/d[0]},
|
|
${l[1]/d[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${p};
|
|
|
|
// Compute the 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);
|
|
}
|
|
`}},GX=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,u]=e;this.outputShape=[s,t,n,u];let l=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p;r?p="(vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC - vec3(0.5)":p="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${l[0]/d[0]},
|
|
${l[1]/d[1]},
|
|
${l[1]/d[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
|
|
${o}.0);
|
|
|
|
float getAValue(int b, int r, int c, int d) {
|
|
return getChannel(getA(b, r, c, d), vec2(c, d));
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
// Calculate values for next column in yRC.z.
|
|
ivec3 yRC = coords.yzz + ivec3(0, 0, 1);
|
|
|
|
// Fractional source index.
|
|
vec3 sourceFracIndexRC = ${p};
|
|
|
|
// Compute the 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 < ${n-1};
|
|
|
|
// In parallel, construct four corners for all four components in
|
|
// packed 2x2 cell.
|
|
vec4 topLeft = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomLeft = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceFloorRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceFloorRC.z, d + 1) : 0.0);
|
|
|
|
vec4 topRight = vec4(
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceFloorRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceFloorRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec4 bottomRight = vec4(
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceCeilRC.x, sourceCeilRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceCeilRC.x, sourceCeilRC.z, d + 1) : 0.0);
|
|
|
|
vec3 fracRC = sourceFracIndexRC - vec3(sourceFloorRC);
|
|
|
|
vec4 top = mix(topLeft, topRight, fracRC.yyzz);
|
|
vec4 bottom = mix(bottomLeft, bottomRight, fracRC.yyzz);
|
|
vec4 newValue = mix(top, bottom, fracRC.x);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function qX(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[u,l]=o,d=te().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new GX(r.shape,u,l,s,i):new HX(r.shape,u,l,s,i);return n.runWebGLProgram(d,[r],"float32")}var XX={kernelName:Ys,backendName:"webgl",kernelFunc:qX},KX=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],u=[n&&s>1?s-1:s,n&&i>1?i-1:i],l=o[0]/u[0],d=o[1]/u[1],p=1/l,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*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(${l});
|
|
const float widthScale = float(${d});
|
|
|
|
const float invHeightScale = float(${p});
|
|
const float invWidthScale = float(${c});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${m});
|
|
|
|
// 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 >= ${s}) {
|
|
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), ${a-1}.0));
|
|
float dxRLerp = dxR - float(topDxRIndex);
|
|
float inverseDxRLerp = 1.0 - dxRLerp;
|
|
|
|
float dxC = float(dyC) * widthScale;
|
|
int leftDxCIndex = int(floor(dxC));
|
|
int rightDxCIndex = int(min(ceil(dxC), ${r-1}.0));
|
|
float dxCLerp = dxC - float(leftDxCIndex);
|
|
float inverseDxCLerp = 1.0 - dxCLerp;
|
|
|
|
if (r == topDxRIndex && c == leftDxCIndex) {
|
|
// topLeft
|
|
accumulator +=
|
|
getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == topDxRIndex && c == rightDxCIndex) {
|
|
// topRight
|
|
accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == leftDxCIndex) {
|
|
// bottomLeft
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;
|
|
}
|
|
|
|
if (r == bottomDxRIndex && c == rightDxCIndex) {
|
|
// bottomRight
|
|
accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function ZX(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new KX(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var YX={kernelName:xc,backendName:"webgl",kernelFunc:ZX},JX=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.outputShape=[];let[s,i,o,u]=e;this.outputShape=[s,t,n,u];let l=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec2(yRC) + vec2(0.5)) * effectiveInputOverOutputRatioRC, vec2(0.0))":c="vec2(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec2 effectiveInputOverOutputRatioRC = vec2(
|
|
${l[0]/d[0]},
|
|
${l[1]/d[1]});
|
|
const vec2 inputShapeRC = vec2(${i}.0, ${o}.0);
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
ivec2 yRC = coords.yz;
|
|
|
|
// Fractional source index.
|
|
vec2 sourceFracIndexRC = ${c};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec2 sourceNearestRC = ivec2(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
|
|
float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}},QX=class{constructor(e,t,n,a,r){this.variableNames=["A"],this.packedInputs=!0,this.packedOutput=!0,this.outputShape=[];let[s,i,o,u]=e;this.outputShape=[s,t,n,u];let l=[a&&t>1?i-1:i,a&&n>1?o-1:o],d=[a&&t>1?t-1:t,a&&n>1?n-1:n],p=a?"0.5":"0.0",c;r?c="max((vec3(yRC) + vec3(0.5)) * effectiveInputOverOutputRatioRC, vec3(0.0))":c="vec3(yRC) * effectiveInputOverOutputRatioRC",this.userCode=`
|
|
const vec3 effectiveInputOverOutputRatioRC = vec3(
|
|
${l[0]/d[0]},
|
|
${l[1]/d[1]},
|
|
${l[1]/d[1]});
|
|
const vec3 inputShapeRC = vec3(${i}.0, ${o}.0,
|
|
${o}.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 = ${c};
|
|
|
|
// Compute the coordinators of nearest neighbor point.
|
|
ivec3 sourceNearestRC = ivec3(
|
|
min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${p})));
|
|
|
|
// Should we calculate next column and row elements in 2x2 packed cell.
|
|
bool hasNextCol = d < ${u-1};
|
|
bool hasNextRow = coords.z < ${n-1};
|
|
|
|
vec4 newValue = vec4(
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d),
|
|
hasNextCol ? getAValue(b, sourceNearestRC.x, sourceNearestRC.y, d + 1)
|
|
: 0.0,
|
|
hasNextRow ? getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d)
|
|
: 0.0,
|
|
(hasNextRow && hasNextCol) ?
|
|
getAValue(b, sourceNearestRC.x, sourceNearestRC.z, d + 1) : 0.0);
|
|
|
|
setOutput(newValue);
|
|
}
|
|
`}};function eK(e){let{inputs:t,backend:n,attrs:a}=e,{images:r}=t,{alignCorners:s,halfPixelCenters:i,size:o}=a,[u,l]=o,d=te().getBool("WEBGL_PACK_IMAGE_OPERATIONS")?new QX(r.shape,u,l,s,i):new JX(r.shape,u,l,s,i);return n.runWebGLProgram(d,[r],r.dtype)}var tK={kernelName:ju,backendName:"webgl",kernelFunc:eK},nK=class{constructor(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t;let[,a,r]=t,[,s,i]=e,o=[n&&s>1?a-1:a,n&&i>1?r-1:r],u=[n&&s>1?s-1:s,n&&i>1?i-1:i],l=o[0]/u[0],d=o[1]/u[1],p=1/l,c=1/d,h=Math.ceil(p)*2+2,m=Math.ceil(c)*2+2;this.userCode=`
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int b = coords[0];
|
|
int d = coords[3];
|
|
int r = coords[1];
|
|
int c = coords[2];
|
|
|
|
float accumulator = 0.0;
|
|
|
|
const float heightScale = float(${l});
|
|
const float widthScale = float(${d});
|
|
|
|
const float invHeightScale = float(${p});
|
|
const float invWidthScale = float(${c});
|
|
|
|
const int winHeight = int(${h});
|
|
const int winWidth = int(${m});
|
|
|
|
// 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 >= ${s}) {
|
|
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 >= ${i}) {
|
|
continue;
|
|
}
|
|
|
|
float sourceFracRow =
|
|
float(${o[0]}) *
|
|
(float(dyR) / float(${u[0]}));
|
|
|
|
float sourceFracCol =
|
|
float(${o[1]}) *
|
|
(float(dyC) / float(${u[1]}));
|
|
|
|
int sourceNearestRow = int(min(
|
|
float(int(${a}) - 1),
|
|
${n} ? float(round(sourceFracRow)) :
|
|
float(floor(sourceFracRow))));
|
|
|
|
int sourceNearestCol = int(min(
|
|
float(int(${r}) - 1),
|
|
${n} ? float(round(sourceFracCol)) :
|
|
float(floor(sourceFracCol))));
|
|
|
|
if (r == sourceNearestRow && c == sourceNearestCol) {
|
|
accumulator += getDy(b, dyR, dyC, d);
|
|
}
|
|
}
|
|
}
|
|
// End loop over dy
|
|
|
|
setOutput(accumulator);
|
|
}
|
|
`}};function aK(e){let{inputs:t,backend:n,attrs:a}=e,{images:r,dy:s}=t,{alignCorners:i}=a,o=new nK(s.shape,r.shape,i);return n.runWebGLProgram(o,[s],s.dtype)}var rK={kernelName:gc,backendName:"webgl",kernelFunc:aK},sK=class{constructor(e,t){this.variableNames=["x"];let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);if(this.outputShape=e,n===1){this.userCode=`
|
|
void main() {
|
|
int coord = getOutputCoords();
|
|
setOutput(getX(${e[0]} - coord - 1));
|
|
}
|
|
`;return}let a=i=>t.indexOf(i)!==-1&&e[i]!==1?`${e[i]} - coords[${i}] - 1`:`coords[${i}]`,r=e.map((i,o)=>a(o)).join(","),s=ut(n);this.userCode=`
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${r}));
|
|
}
|
|
`}},iK=class{constructor(e,t){this.variableNames=["x"],this.packedInputs=!0,this.packedOutput=!0;let n=e.length;if(n>4)throw new Error(`WebGL backend: Reverse of rank-${n} tensor is not yet supported`);this.outputShape=e;let a=gn("rc",n),r=`${a[n-1]} + 1 < ${this.outputShape[n-1]}`,s=`${a[n-2]} + 1 < ${this.outputShape[n-2]}`,i=ut(n);n===1?this.userCode=`
|
|
void main(){
|
|
int rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = getChannel(getX(${e[0]} - rc - 1),
|
|
${e[0]} - rc - 1);
|
|
if(${r}){
|
|
result.g = getChannel(getX(${e[0]} - (rc + 1) - 1),
|
|
${e[0]} - (rc + 1) - 1);
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`:this.userCode=`
|
|
void main() {
|
|
${i} rc = getOutputCoords();
|
|
vec4 result = vec4(0.);
|
|
result.r = ${o(a.slice())};
|
|
if(${r}){
|
|
result.g = ${u(a.slice())};
|
|
}
|
|
if(${s}) {
|
|
result.b = ${l(a.slice())};
|
|
if(${r}) {
|
|
result.a = ${d(a.slice())};
|
|
}
|
|
}
|
|
setOutput(result);
|
|
}
|
|
`;function o(h){return p(h)}function u(h){return h[n-1]="("+h[n-1]+" + 1)",p(h)}function l(h){return h[n-2]="("+h[n-2]+" + 1)",p(h)}function d(h){return h[n-1]="("+h[n-1]+" + 1)",h[n-2]="("+h[n-2]+" + 1)",p(h)}function p(h){let m=e.map((y,g)=>c(g,h)),f=m.join(","),A=m.slice(-2).join(",");return`getChannel(getX(${f}), vec2(${A}))`}function c(h,m){return t.indexOf(h)!==-1&&e[h]!==1?`${e[h]} - ${m[h]} - 1`:`${m[h]}`}}};function oK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=r.shape.length,o=k.parseAxisParam(s,r.shape);if(i===0)return Gn({inputs:{x:r},backend:n});let u=te().getBool("WEBGL_PACK_ARRAY_OPERATIONS")?new iK(r.shape,o):new sK(r.shape,o);return n.runWebGLProgram(u,[r],r.dtype)}var lK={kernelName:Qs,backendName:"webgl",kernelFunc:oK},uK=class{constructor(e,t){this.variableNames=["Image"],this.outputShape=[];let n=e[1],a=e[2];this.outputShape=e;let r="";typeof t=="number"?r=`float outputValue = ${t.toFixed(2)};`:r=`
|
|
vec3 fill = vec3(${t.join(",")});
|
|
float outputValue = fill[coords[3]];`,this.userCode=`
|
|
uniform vec4 params;
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
float coordXFloat = (float(x) - params[0]) * params[3] -
|
|
(float(y) - params[1]) * params[2];
|
|
float coordYFloat = (float(x) - params[0]) * params[2] +
|
|
(float(y) - params[1]) * params[3];
|
|
int coordX = int(round(coordXFloat + params[0]));
|
|
int coordY = int(round(coordYFloat + params[1]));
|
|
${r}
|
|
if(coordX >= 0 && coordX < ${a} && coordY >= 0 && coordY < ${n}) {
|
|
outputValue = getImage(coords[0], coordY, coordX, coords[3]);
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}getCustomSetupFunc(e,t,n,a){return(r,s)=>{this.paramsLoc==null&&(this.paramsLoc=r.getUniformLocationNoThrow(s,"params")),r.gl.uniform4f(this.paramsLoc,e,t,n,a)}}},dK={kernelName:fl,backendName:"webgl",kernelFunc:({inputs:e,attrs:t,backend:n})=>{let{image:a}=e,{radians:r,fillValue:s,center:i}=t,o=n,u=new uK(a.shape,s),[l,d]=F.getImageCenter(i,a.shape[1],a.shape[2]),p=u.getCustomSetupFunc(l,d,Math.sin(r),Math.cos(r));return o.runWebGLProgram(u,[a],a.dtype,p)}},pK=`
|
|
// OpenGL ES does not support round function.
|
|
// The algorithm is based on banker's rounding.
|
|
float base = floor(x);
|
|
if ((x - base) < 0.5) {
|
|
return floor(x);
|
|
} else if ((x - base) > 0.5) {
|
|
return ceil(x);
|
|
} else {
|
|
if (mod(base, 2.0) == 0.0) {
|
|
return base;
|
|
} else {
|
|
return base + 1.0;
|
|
}
|
|
}
|
|
`,cK=Ke({opSnippet:pK}),hK={kernelName:ei,backendName:"webgl",kernelFunc:cK},fK="return inversesqrt(x);",mK=Ke({opSnippet:fK,cpuKernelImpl:UB}),AK={kernelName:ti,backendName:"webgl",kernelFunc:mK},f6=class{constructor(e,t,n,a,r,s,i=!0){this.variableNames=["updates","indices","defaultValue"],this.outputShape=s;let o=ut(r.length),u=ut(s.length),l="";n===1?l="i":n===2&&(l="i, j");let d=`getIndices(${l})`,p="";a===1?p="i":a===2&&(p="i, coords[1]");let c=`getUpdates(${p})`,h=t>1?"strides[j]":"strides";this.userCode=`
|
|
${o} strides = ${o}(${r});
|
|
|
|
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 < ${t}; j++) {
|
|
int index = round(${d});
|
|
flattenedIndex += index * ${h};
|
|
}
|
|
if (flattenedIndex == coords[0]) {
|
|
sum += ${c};
|
|
found = true;
|
|
}
|
|
}
|
|
setOutput(mix(getDefaultValue(), sum, float(found)));
|
|
}
|
|
`}};function yK(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r,updates:s}=t,{shape:i}=a,{sliceRank:o,numUpdates:u,sliceSize:l,strides:d,outputSize:p}=F.calculateShapes(s,r,i),c=[p/l,l];if(p===0)return n.makeTensorInfo(i,r.dtype);let h=ye({inputs:{x:r},backend:n,attrs:{shape:[u,o]}}),m=ye({inputs:{x:s},backend:n,attrs:{shape:[u,l]}}),f=n.makeTensorInfo([],"float32",new Float32Array([0])),A=new f6(u,o,h.shape.length,m.shape.length,d,c),y=n.runWebGLProgram(A,[m,h,f],m.dtype),g=ye({inputs:{x:y},backend:n,attrs:{shape:i}});return n.disposeIntermediateTensorInfo(h),n.disposeIntermediateTensorInfo(m),n.disposeIntermediateTensorInfo(y),n.disposeIntermediateTensorInfo(f),g}var gK={kernelName:tl,backendName:"webgl",kernelFunc:yK},xK=class{constructor(e,t,n){this.variableNames=["c","a","b"],this.outputShape=t;let a,r;if(n>4)throw Error(`Where for rank ${n} is not yet supported`);if(n===1)r="resRC",a="resRC";else{let i=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],u=[];for(let l=0;l<t.length;l++)u.push(`${i[l]}`),l<e&&o.push(`${i[l]}`);a=o.join(),r=u.join()}let s=ut(n);this.userCode=`
|
|
void main() {
|
|
${s} resRC = getOutputCoords();
|
|
float cVal = getC(${a});
|
|
if (cVal >= 1.0) {
|
|
setOutput(getA(${r}));
|
|
} else {
|
|
setOutput(getB(${r}));
|
|
}
|
|
}
|
|
`}};function bK(e){let{inputs:t,backend:n}=e,{condition:a,t:r,e:s}=t,i=new xK(a.shape.length,r.shape,r.shape.length);return n.runWebGLProgram(i,[a,r,s],fa(r.dtype,s.dtype))}var vK={kernelName:nl,backendName:"webgl",kernelFunc:bK},wK=`
|
|
// Stable and Attracting Fixed Point (0, 1) for Normalized Weights.
|
|
// see: https://arxiv.org/abs/1706.02515
|
|
float scaleAlpha = ${F.SELU_SCALEALPHA};
|
|
float scale = ${F.SELU_SCALE};
|
|
return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);
|
|
`,kK=Ke({opSnippet:wK}),IK={kernelName:al,backendName:"webgl",kernelFunc:kK},SK="return 1.0 / (1.0 + exp(-1.0 * x));",NK=Ke({opSnippet:SK}),TK={kernelName:ai,backendName:"webgl",kernelFunc:NK},CK=`
|
|
if (isnan(x)) { return 0.0; }
|
|
return sign(x);
|
|
`,EK=Ke({opSnippet:CK}),RK={kernelName:il,backendName:"webgl",kernelFunc:EK},MK=Ew+`
|
|
return sin(x);
|
|
`,FK=Ke({opSnippet:MK}),$K={kernelName:ni,backendName:"webgl",kernelFunc:FK},DK=`
|
|
float e2x = exp(x);
|
|
return (e2x - 1.0 / e2x) / 2.0;
|
|
`,_K=Ke({opSnippet:DK}),OK={kernelName:sl,backendName:"webgl",kernelFunc:_K},zK=`
|
|
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;
|
|
`,PK=Ke({opSnippet:zK}),LK={kernelName:ol,backendName:"webgl",kernelFunc:PK},WK=e=>{let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{blockShape:s,paddings:i}=a;k.assert(r.shape.length<=4,()=>"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");let o=s.reduce((y,g)=>y*g),u=[[0,0]];u.push(...i);for(let y=1+s.length;y<r.shape.length;++y)u.push([0,0]);let l=[],d=c6({inputs:{x:r},backend:n,attrs:{paddings:u,constantValue:0}}),p=F.getReshaped(d.shape,s,o,!1),c=F.getPermuted(p.length,s.length,!1),h=F.getReshapedPermuted(d.shape,s,o,!1),m=ye({inputs:{x:d},backend:n,attrs:{shape:p}}),f=xn({inputs:{x:m},backend:n,attrs:{perm:c}}),A=ye({inputs:{x:f},backend:n,attrs:{shape:h}});return l.push(d),l.push(m),l.push(f),l.forEach(y=>n.disposeIntermediateTensorInfo(y)),A},BK={kernelName:Uu,backendName:"webgl",kernelFunc:WK};function VK(e){let{inputs:t,backend:n}=e,{indices:a,values:r,denseShape:s,defaultValue:i}=t;if(s.shape.length!==1)throw new Error(`Dense shape must be a vector, saw:
|
|
${s.shape}`);if(a.shape.length!==2)throw new Error(`Indices must be a matrix, saw:
|
|
${a.shape}`);if(r.shape.length!==1)throw new Error(`Values must be a vector, saw:
|
|
${r.shape}`);if(i.shape.length!==0)throw new Error(`Default value must be a scalar, saw:
|
|
${i.shape}`);let o=n.readSync(a.dataId),u=n.readSync(r.dataId),l=n.readSync(s.dataId),d=n.readSync(i.dataId)[0],[p,c,h,m,f]=GB(o,a.shape,a.dtype,u,r.dtype,l,d);return[n.makeTensorInfo(c,a.dtype,p),n.makeTensorInfo([c[0]],r.dtype,h),n.makeTensorInfo([m.length],"bool",new Uint8Array(m.map(A=>Number(A)))),n.makeTensorInfo([f.length],a.dtype,new Int32Array(f))]}var jK={kernelName:bc,backendName:"webgl",kernelFunc:VK};function UK(e){let{inputs:t,backend:n}=e,{inputIndices:a,inputShape:r,newShape:s}=t;if(a.shape.length!==2)throw new Error(`Input indices should be a matrix but received shape ${a.shape}`);if(r.shape.length!==1)throw new Error(`Input shape should be a vector but received shape ${r.shape}`);if(s.shape.length!==1)throw new Error(`Target shape should be a vector but received shape ${s.shape}`);let i=Array.from(n.readSync(r.dataId)),o=n.readSync(a.dataId),u=Array.from(n.readSync(s.dataId)),[l,d,p]=qB(o,a.shape,a.dtype,i,u);return[n.makeTensorInfo(d,a.dtype,l),n.makeTensorInfo([p.length],s.dtype,new Int32Array(p))]}var HK={kernelName:vc,backendName:"webgl",kernelFunc:UK};function GK(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),u=n.readSync(s.dataId),[l,d]=mw(i,a.shape,a.dtype,o,u,!0);return n.makeTensorInfo(d,a.dtype,l)}var qK={kernelName:wc,backendName:"webgl",kernelFunc:GK};function XK(e){let{inputs:t,backend:n}=e,{data:a,indices:r,segmentIds:s}=t;if(a.shape.length<1)throw new Error("Data should be at least 1 dimensional but received scalar");if(r.shape.length!==1)throw new Error(`Indices should be a vector but received shape
|
|
${r.shape}`);if(s.shape.length!==1)throw new Error(`Segment ids should be a vector but received shape
|
|
${s.shape}`);let i=n.readSync(a.dataId),o=n.readSync(r.dataId),u=n.readSync(s.dataId),[l,d]=mw(i,a.shape,a.dtype,o,u);return n.makeTensorInfo(d,a.dtype,l)}var KK={kernelName:kc,backendName:"webgl",kernelFunc:XK};function ZK(e){let{inputs:t,backend:n,attrs:a}=e,{sparseIndices:r,sparseValues:s,defaultValue:i}=t,{outputShape:o}=a,{sliceRank:u,numUpdates:l,strides:d,outputSize:p}=F.calculateShapes(s,r,o),c=!1,h=new f6(l,u,r.shape.length,s.shape.length,d,[p,1],c),m=n.runWebGLProgram(h,[s,r,i],s.dtype),f=ye({inputs:{x:m},backend:n,attrs:{shape:o}});return n.disposeIntermediateTensorInfo(m),f}var YK={kernelName:Ic,backendName:"webgl",kernelFunc:ZK};function JK(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{numOrSizeSplits:s,axis:i}=a,o=k.parseAxisParam(i,r.shape)[0],u=F.prepareSplitSize(r,s,o),l=r.shape.length,d=new Array(l).fill(0),p=r.shape.slice();return u.map(c=>{let h=[...p];h[o]=c;let m=_d({inputs:{x:r},backend:n,attrs:{begin:d,size:h}});return d[o]+=c,m})}var QK={kernelName:ll,backendName:"webgl",kernelFunc:JK},eZ="return sqrt(x);",tZ=Ke({opSnippet:eZ}),nZ={kernelName:ri,backendName:"webgl",kernelFunc:tZ},aZ="return x * x;",rZ=Ke({opSnippet:aZ}),sZ={kernelName:Hu,backendName:"webgl",kernelFunc:rZ},m6="return (a - b) * (a - b);",iZ=rn({opSnippet:m6,packedOpSnippet:m6}),oZ={kernelName:oi,backendName:"webgl",kernelFunc:iZ};function lZ({inputs:e,attrs:t,backend:n}){let{x:a}=e,r=Na+`
|
|
return x > 0.0 ? 1.0 : float(${t.alpha});
|
|
`,s=new Zr(a.shape,r);return n.runWebGLProgram(s,[a],a.dtype)}var uZ={kernelName:zr,backendName:"webgl",kernelFunc:lZ},dZ=class{constructor(e,t,n){this.variableNames=["x"],this.outputShape=n;let a=n.length,r=ut(n.length),s=ut(n.length),i="";if(a===1)i="coords * strides + begin";else{let o=0;i=n.map((u,l)=>(o++,n.length===1?`coords * strides[${l}] + begin[${l}]`:`coords[${o-1}] * strides[${l}] + begin[${l}]`)).join(",")}this.userCode=`
|
|
${r} begin = ${r}(${e});
|
|
${r} strides = ${r}(${t});
|
|
|
|
void main() {
|
|
${s} coords = getOutputCoords();
|
|
setOutput(getX(${i}));
|
|
}
|
|
`}};function pZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{begin:s,end:i,strides:o,beginMask:u,endMask:l,ellipsisMask:d,newAxisMask:p,shrinkAxisMask:c}=a,{nonStrided:h,$begin:m,$strides:f,size:A,newShape:y,outShape:g}=hn.sliceInfo(r.shape,s,i,o,u,l,d,p,c),x=ye({inputs:{x:r},backend:n,attrs:{shape:y}}),v;if(h){let w=_d({inputs:{x},backend:n,attrs:{begin:m,size:A}});v=ye({inputs:{x:w},backend:n,attrs:{shape:g}}),n.disposeIntermediateTensorInfo(w)}else if(g.some(w=>w===0))v=n.makeTensorInfo(g,r.dtype,[]);else if(n.shouldExecuteOnCPU([x])){let w=n.texData.get(x.dataId).values,N=Be(x.shape,x.dtype,w),C=XB(g,N,f,m);v=n.makeTensorInfo(g,x.dtype,C.values)}else{let w=new dZ(m,f,g);v=n.runWebGLProgram(w,[x],x.dtype)}let b=ye({inputs:{x:v},backend:n,attrs:{shape:g}});return n.disposeIntermediateTensorInfo(x),n.disposeIntermediateTensorInfo(v),b}var cZ={kernelName:ul,backendName:"webgl",kernelFunc:pZ};function hZ(e){let{inputs:t,backend:n,attrs:a}=e,{separator:r,nGramWidths:s,leftPad:i,rightPad:o,padWidth:u,preserveShortSequences:l}=a,{data:d,dataSplits:p}=t,c=n.readSync(d.dataId),h=n.readSync(p.dataId),[m,f]=KB(c,h,r,s,i,o,u,l);return[n.makeTensorInfo([m.length],"string",m),n.makeTensorInfo(p.shape,"int32",f)]}var fZ={kernelName:Sc,backendName:"webgl",kernelFunc:hZ};function mZ(e){let{inputs:t,backend:n,attrs:a}=e,{skipEmpty:r}=a,{input:s,delimiter:i}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(s.shape.length!==1)throw new Error(`Input must be a vector, got shape: ${s.shape}`);if(i.shape.length!==0)throw new Error(`Delimiter must be a scalar, got shape: ${i.shape}`);let o=n.readSync(s.dataId),u=n.readSync(i.dataId)[0],[l,d,p]=ZB(o,u,r),c=d.length;return[n.makeTensorInfo([c,2],"int32",l),n.makeTensorInfo([c],"string",d),n.makeTensorInfo([2],"int32",new Int32Array(p))]}var AZ={kernelName:Nc,backendName:"webgl",kernelFunc:mZ};function yZ(e){let{inputs:t,backend:n,attrs:a}=e,{numBuckets:r}=a,{input:s}=t;if(s.dtype!=="string")throw new Error("Input must be of datatype string");if(r<=0)throw new Error("Number of buckets must be at least 1");let i=n.readSync(s.dataId),o=YB(i,r);return n.makeTensorInfo(s.shape,"int32",o)}var gZ={kernelName:Tc,backendName:"webgl",kernelFunc:yZ},xZ="return tan(x);",bZ=Ke({opSnippet:xZ}),vZ={kernelName:ui,backendName:"webgl",kernelFunc:bZ},wZ=`
|
|
float e2x = exp(-2.0 * abs(x));
|
|
return sign(x) * (1.0 - e2x) / (1.0 + e2x);
|
|
`,kZ=Ke({opSnippet:wZ}),IZ={kernelName:di,backendName:"webgl",kernelFunc:kZ},SZ=class{constructor(e,t){this.variableNames=["A"];let n=new Array(e.length);for(let s=0;s<n.length;s++)n[s]=e[s]*t[s];this.outputShape=n,this.rank=n.length;let a=ut(this.rank),r=NZ(e);this.userCode=`
|
|
void main() {
|
|
${a} resRC = getOutputCoords();
|
|
setOutput(getA(${r}));
|
|
}
|
|
`}};function NZ(e){let t=e.length;if(t>5)throw Error(`Tile for rank ${t} is not yet supported`);if(t===1)return`imod(resRC, ${e[0]})`;let n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],a=[];for(let r=0;r<e.length;r++)a.push(`imod(${n[r]}, ${e[r]})`);return a.join()}function A6(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{reps:s}=a;if(r.dtype==="string"||r.shape.length>5){let o=n.readSync(r.dataId),u=r.dtype==="string"?o.map(p=>k.decodeString(p)):o,l=Be(r.shape,r.dtype,u),d=QB(l,s);return n.makeTensorInfo(d.shape,d.dtype,d.values)}let i=new SZ(r.shape,s);return n.runWebGLProgram(i,[r],r.dtype)}var TZ={kernelName:Or,backendName:"webgl",kernelFunc:A6};function CZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{k:s,sorted:i}=a,o=n.readSync(r.dataId),[u,l]=eV(o,r.shape,r.dtype,s,i);return[n.makeTensorInfo(u.shape,u.dtype,u.values),n.makeTensorInfo(l.shape,l.dtype,l.values)]}var EZ={kernelName:dl,backendName:"webgl",kernelFunc:CZ},RZ=class{constructor(e,t,n,a,r,s){this.variableNames=["Image","Transforms"],this.outputShape=s;let i=n==="nearest"?1:2,o;switch(a){case"constant":o=1;break;case"reflect":o=2;break;case"wrap":o=3;break;case"nearest":o=4;break;default:o=1;break}this.userCode=`
|
|
float mapCoord(float outCoord, float len) {
|
|
float inCoord = outCoord;
|
|
if(${o} == 2) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
if (inCoord < sz2) {
|
|
inCoord = sz2 * float(int(float(-inCoord / sz2))) +
|
|
inCoord;
|
|
}
|
|
inCoord = inCoord < -len ? inCoord + sz2 : -inCoord - 1.0;
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz2 = 2.0 * len;
|
|
inCoord -= sz2 * float(int(float(inCoord / sz2)));
|
|
if (inCoord >= len) {
|
|
inCoord = sz2 - inCoord - 1.0;
|
|
}
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${o} == 3) {
|
|
if (inCoord < 0.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord += len * (float(int(float(-inCoord / sz))) + 1.0);
|
|
}
|
|
} else if (inCoord > len - 1.0) {
|
|
if (len <= 1.0) {
|
|
inCoord = 0.0;
|
|
} else {
|
|
float sz = len - 1.0;
|
|
inCoord -= len * float(int(float(inCoord / sz)));
|
|
}
|
|
}
|
|
return clamp(inCoord, 0.0, len - 1.0);
|
|
} else if (${o} == 4) {
|
|
return clamp(outCoord, 0.0, len - 1.0);
|
|
} else {
|
|
return outCoord;
|
|
}
|
|
}
|
|
|
|
float readWithFillValue(int batch, int coordY, int coordX,
|
|
int channel) {
|
|
float outputValue;
|
|
if (0 <= coordY && coordY < ${e} && 0 <= coordX && coordX < ${t}) {
|
|
outputValue = getImage(batch, coordY, coordX, channel);
|
|
} else {
|
|
outputValue = float(${r});
|
|
}
|
|
return outputValue;
|
|
}
|
|
|
|
void main() {
|
|
ivec4 coords = getOutputCoords();
|
|
float outputValue;
|
|
int batch = coords[0];
|
|
int x = coords[2];
|
|
int y = coords[1];
|
|
int channel = coords[3];
|
|
float xf = float(x);
|
|
float yf = float(y);
|
|
float a1 = getTransforms(batch, 0);
|
|
float a2 = getTransforms(batch, 1);
|
|
float a3 = getTransforms(batch, 2);
|
|
float b1 = getTransforms(batch, 3);
|
|
float b2 = getTransforms(batch, 4);
|
|
float b3 = getTransforms(batch, 5);
|
|
float c1 = getTransforms(batch, 6);
|
|
float c2 = getTransforms(batch, 7);
|
|
float projection = c1 * xf + c2 * yf + 1.0;
|
|
if (projection == 0.0) {
|
|
outputValue = float(${r});
|
|
} else {
|
|
float inX = (a1 * xf + a2 * yf + a3) / projection;
|
|
float inY = (b1 * xf + b2 * yf + b3) / projection;
|
|
float mapX = mapCoord(inX, float(${t}));
|
|
float mapY = mapCoord(inY, float(${e}));
|
|
|
|
if (${i} == 1) {
|
|
int coordY = int(round(mapY));
|
|
int coordX = int(round(mapX));
|
|
outputValue = readWithFillValue(batch, coordY, coordX,
|
|
channel);
|
|
} else {
|
|
float yFloor = floor(mapY);
|
|
float xFloor = floor(mapX);
|
|
float yCeil = yFloor + 1.0;
|
|
float xCeil = xFloor + 1.0;
|
|
float valueYFloor = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yFloor), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yFloor), int(xCeil), channel);
|
|
float valueYCeil = (xCeil - mapX) *
|
|
readWithFillValue(batch, int(yCeil), int(xFloor), channel) +
|
|
(mapX - xFloor) *
|
|
readWithFillValue(batch, int(yCeil), int(xCeil), channel);
|
|
outputValue = (yCeil - mapY) * valueYFloor +
|
|
(mapY - yFloor) * valueYCeil;
|
|
}
|
|
}
|
|
setOutput(outputValue);
|
|
}
|
|
`}};function MZ(e){let{inputs:t,backend:n,attrs:a}=e,{image:r,transforms:s}=t,{interpolation:i,fillMode:o,fillValue:u,outputShape:l}=a,[d,p,c,h]=r.shape,[m,f]=l!=null?l:[p,c],A=[d,m,f,h],y=new RZ(p,c,i,o,u,A);return n.runWebGLProgram(y,[r,s],"float32")}var FZ={kernelName:pl,backendName:"webgl",kernelFunc:MZ};function $Z(e){let{inputs:t,attrs:n,backend:a}=e,{axis:r}=n,{x:s}=t;Vl(s,"unique"),console.warn("WARNING: ","UI might be locked temporarily as data is being downloaded");let i=a.readSync(s.dataId),{outputValues:o,outputShape:u,indices:l}=tV(i,r,s.shape,s.dtype);return[a.makeTensorInfo(u,s.dtype,o),a.makeTensorInfo([l.length],"int32",l)]}var DZ={kernelName:Cc,backendName:"webgl",kernelFunc:$Z};function _Z(e){let{inputs:t,backend:n,attrs:a}=e,{value:r}=t,{axis:s}=a;s<0&&(s+=r.shape.length);let i=r,o=i.shape.length,u=r.shape[s],l=new Array(o-1),d=0;for(let f=0;f<o;f++)f!==s&&(l[d++]=i.shape[f]);let p=[],c=new Array(o).fill(0),h=i.shape.slice();h[s]=1;let m=new Array(u);for(let f=0;f<m.length;f++){c[s]=f;let A=_d({inputs:{x:i},backend:n,attrs:{begin:c,size:h}}),y=ye({inputs:{x:A},backend:n,attrs:{shape:l}});m[f]=y,p.push(A)}return p.forEach(f=>n.disposeIntermediateTensorInfo(f)),m}var OZ={kernelName:cl,backendName:"webgl",kernelFunc:_Z},zZ=class{constructor(e,t){this.variableNames=["x","segmentIds"];let n=e.windowSize,a=e.batchSize,r=e.inSize,s=e.numSegments,i=s*Math.ceil(r/n);this.outputShape=[a,i];let o="0.0",u="sumValue",l=Math.floor(n/4)*4,d=n%4,p=`
|
|
sumValue += dot(values, segFilter);
|
|
`,c="";r%n>0&&(c=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return initializationValue;
|
|
}
|
|
`);let h="";r%n>0&&(h=`
|
|
if (inIdx < 0 || inIdx >= ${r}) {
|
|
return -1.0;
|
|
}
|
|
`),this.userCode=`
|
|
const float initializationValue = ${o};
|
|
|
|
float getValue(int batch, int inIdx) {
|
|
${c}
|
|
return getX(batch, inIdx);
|
|
}
|
|
|
|
float getSegmentIdAtIndex(int inIdx) {
|
|
${h}
|
|
return getSegmentIds(inIdx);
|
|
}
|
|
|
|
void main() {
|
|
ivec2 coords = getOutputCoords();
|
|
int batch = coords[0];
|
|
int outIdx = coords[1];
|
|
int inOffset = int(floor(float(outIdx) / float(
|
|
${s})) * float(${n}));
|
|
int currentSeg = int(mod(float(outIdx), float(${s})));
|
|
|
|
float sumValue = 0.0;
|
|
|
|
for (int i = 0; i < ${l}; 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
|
|
);
|
|
|
|
${p}
|
|
}
|
|
|
|
int inIdx = inOffset + ${l};
|
|
if (${d===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
|
|
);
|
|
|
|
${p}
|
|
} else if (${d===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
|
|
);
|
|
|
|
${p}
|
|
} else if (${d===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
|
|
);
|
|
|
|
${p}
|
|
}
|
|
setOutput(${u});
|
|
}
|
|
`}};function PZ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r,segmentIds:s}=t,{numSegments:i}=a,o=r.shape.length,u=[],l=0,d=F.getAxesPermutation([l],o),p=r;d!=null&&(p=xn({inputs:{x:r},backend:n,attrs:{perm:d}}),u.push(p),l=F.getInnerMostAxes(1,o)[0]);let c=F.segment_util.computeOutShape(p.shape,l,i),h=k.sizeFromShape([p.shape[l]]),m=ye({inputs:{x:p},backend:n,attrs:{shape:[-1,h]}});u.push(m);let f=_c(r.dtype),A=(v,b,w,N,C)=>{let E=v.shape[0],z=v.shape[1],$=F.segment_util.segOpComputeOptimalWindowSize(z,C),S={windowSize:$,inSize:z,batchSize:E,numSegments:C},O=new zZ(S,b),_=n.compileAndRun(O,[v,w],N);if(u.push(_),_.shape[1]===C)return _;let W=h6({backend:n,attrs:{start:0,stop:C,step:1,dtype:"float32"}}),G=A6({inputs:{x:W},backend:n,attrs:{reps:[z/$]}});return u.push(W),u.push(G),A(_,b,G,N,C)},y=A(m,"unsortedSegmentSum",s,f,i),g=ye({inputs:{x:y},backend:n,attrs:{shape:c}}),x=g;if(d!=null){u.push(g);let v=F.getUndoAxesPermutation(d);x=xn({inputs:{x},backend:n,attrs:{perm:v}})}return u.forEach(v=>n.disposeIntermediateTensorInfo(v)),x}var LZ={kernelName:Gu,backendName:"webgl",kernelFunc:PZ},WZ=[fq,yq,QV,tj,rj,oj,uj,cj,fj,Aj,bj,wj,Sj,Cj,_j,Mj,Pj,Vj,Wj,Gj,Xj,Zj,eU,oU,uU,mU,yU,vU,IU,DV,EU,LU,BU,$U,HU,qU,jU,ZU,QU,nH,rH,iH,uH,mH,yH,pH,bH,kH,SH,EH,$H,zH,WH,BH,VH,UH,GH,XH,ZH,JH,nG,sG,lG,dG,hG,yG,vG,SG,$V,TG,TU,RG,$G,OG,OV,WG,UG,GG,QG,ZG,aq,iq,dq,xq,Tq,Sq,Mq,$q,_q,kq,zq,Lq,jq,qq,Yq,sX,BV,oX,dX,hX,AX,pU,xX,vX,kX,NX,RX,PV,FX,$X,cU,tX,OX,UX,WX,jV,XX,YX,tK,rK,lK,dK,hK,AK,gK,vK,IK,TK,RK,$K,OK,sU,aX,LK,BK,jK,HK,qK,KK,YK,QK,nZ,sZ,oZ,uZ,cZ,fZ,AZ,gZ,nX,ZV,vZ,IZ,TZ,EZ,FZ,YV,DZ,OZ,LZ,bX];for(let e of WZ)mi(e);var Mn;(function(e){e[e.float32=0]="float32",e[e.int32=1]="int32",e[e.bool=2]="bool",e[e.string=3]="string",e[e.complex64=4]="complex64"})(Mn||(Mn={}));var zd;(function(e){e[e.linear=0]="linear",e[e.relu=1]="relu",e[e.relu6=2]="relu6",e[e.prelu=3]="prelu",e[e.leakyrelu=4]="leakyrelu",e[e.sigmoid=5]="sigmoid"})(zd||(zd={}));var y6;function BZ(e){y6=e.wasm.cwrap(ci,null,["number","array","number","number","array","number","number","number","number","number","number","number","number"])}function VZ(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s,bias:i,preluActivationWeights:o}=t;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("_FusedMatMul for non non-float32 tensors not yet supported.");let{transposeA:u,transposeB:l,activation:d,leakyreluAlpha:p}=a,c=n.dataIdMap.get(r.dataId).id,h=n.dataIdMap.get(s.dataId).id,m=0;if(i!=null){let C=n.dataIdMap.get(i.dataId);if(C.shape.length!==1)throw new Error(`_FusedMatMul only supports rank-1 bias but got rank ${C.shape.length}.`);m=C.id}let f=o==null?0:n.dataIdMap.get(o.dataId).id,A=zd[d];if(A==null)throw new Error(`${d} activation not yet supported for FusedConv2D in the wasm backend.`);let y=u?r.shape[2]:r.shape[1],g=l?s.shape[1]:s.shape[2],x=r.shape[0],v=n.makeOutput([x,y,g],r.dtype),b=n.dataIdMap.get(v.dataId).id,w=new Uint8Array(new Int32Array(r.shape).buffer),N=new Uint8Array(new Int32Array(s.shape).buffer);return y6(c,w,r.shape.length,h,N,s.shape.length,u,l,A,m,f,p||0,b),v}var jZ={kernelName:ci,backendName:"wasm",setupFunc:BZ,kernelFunc:VZ};function bn(e){let t;function n(r){t=r.wasm.cwrap(e,null,["number","number"])}function a(r){let{backend:s,inputs:{x:i}}=r,o=s.dataIdMap.get(i.dataId).id,u=s.makeOutput(i.shape,i.dtype),l=s.dataIdMap.get(u.dataId).id;return k.sizeFromShape(u.shape)===0||t(o,l),u}return{kernelName:e,backendName:"wasm",setupFunc:n,kernelFunc:a}}var UZ=bn(fo);function vn(e,t,n){let a;function r(i){a=i.wasm.cwrap(e,null,["number","array","number","number","array","number","number","number"])}function s(i){let{backend:o,inputs:u}=i,{a:l,b:d}=u,p=o.dataIdMap.get(l.dataId).id,c=o.dataIdMap.get(d.dataId).id,h=n!=null?n:l.dtype,m=F.assertAndGetBroadcastShape(l.shape,d.shape),f=o.makeOutput(m,h);if(k.sizeFromShape(m)===0)return f;let A=new Uint8Array(new Int32Array(l.shape).buffer),y=new Uint8Array(new Int32Array(d.shape).buffer),g=o.dataIdMap.get(f.dataId).id,x=()=>a(p,A,l.shape.length,c,y,d.shape.length,Mn[l.dtype],g);if(t&&l.dtype==="float32")return x(),f;let v=F.getBroadcastDims(l.shape,m),b=F.getBroadcastDims(d.shape,m),w=v.every((C,E)=>C===E),N=b.every((C,E)=>C===E);if(w&&N)return x(),f;throw new Error(`Broadcasting along outer dims is not yet supported for ${l.dtype} ${e}.`)}return{kernelName:e,backendName:"wasm",setupFunc:r,kernelFunc:s}}var HZ=!0,GZ=vn(Dr,HZ),g6;function qZ(e){g6=e.wasm.cwrap(gs,null,["array","number","number","number"])}function XZ(e){let{inputs:t,backend:n}=e,a=n.makeOutput(t[0].shape,t[0].dtype);if(k.sizeFromShape(a.shape)===0)return a;let r=t.map(o=>n.dataIdMap.get(o.dataId).id),s=new Uint8Array(new Int32Array(r).buffer),i=n.dataIdMap.get(a.dataId).id;return g6(s,r.length,Mn[a.dtype],i),a}var KZ={kernelName:gs,backendName:"wasm",setupFunc:qZ,kernelFunc:XZ};function Jh(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype),r=n.typedArrayFromHeap(t);return n.typedArrayFromHeap(a).set(r),a}var ZZ={kernelName:_s,backendName:"wasm",kernelFunc:Jh},x6;function YZ(e){x6=e.wasm.cwrap(pi,null,["number","array","number","number","number","array","number"])}function Qh(e){let{inputs:t,backend:n,attrs:a}=e,[r,s]=QZ(t.x.shape,a.perm),i=!0;for(let m=0;m<s.length;m++)s[m]!==m&&(i=!1);let o=JZ(t.x.shape,a.perm),u={dataId:t.x.dataId,shape:r,dtype:t.x.dtype};if(i){let m=Jh({inputs:t,backend:n});return m.shape=o,m}let l=n.makeOutput(o,u.dtype),d=n.dataIdMap.get(u.dataId).id,p=n.dataIdMap.get(l.dataId).id,c=new Uint8Array(new Int32Array(s).buffer),h=new Uint8Array(new Int32Array(u.shape).buffer);return x6(d,h,u.shape.length,Mn[u.dtype],p,c,s.length),l}function JZ(e,t){let n=new Array(e.length);for(let a=0;a<n.length;a++)n[a]=e[t[a]];return n}function QZ(e,t){let n=[],a=[];for(let r=0;r<e.length;++r)e[r]!==1&&n.push(e[r]),e[t[r]]!==1&&a.push(t[r]);for(let r=0;r<a.length;++r){let s=-1;for(let i=0;i<a.length;++i)a[i]>=r&&(s===-1||a[s]>a[i])&&(s=i);a[s]=r}return[n,a]}var eY={kernelName:pi,backendName:"wasm",kernelFunc:Qh,setupFunc:YZ};function Jr(e,t,n){let a=e.shape,r=e.shape.length,s=k.parseAxisParam(t,a),i=s,o=F.getAxesPermutation(i,r),u=null,l=!1;if(o!=null){let d=new Array(r);for(let c=0;c<d.length;c++)d[c]=a[o[c]];i=F.getInnerMostAxes(i.length,r),u=Qh({inputs:{x:e},attrs:{perm:o},backend:n});let p=n.dataIdMap.get(e.dataId).id;n.dataIdMap.get(u.dataId).id!==p&&(l=!0)}return{transposed:u,originalAxes:s,axes:i,inputWasTransposed:l}}var b6;function tY(e){b6=e.wasm.cwrap(yo,null,["number, number, number"])}function nY(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=i,{transposed:l,axes:d,originalAxes:p,inputWasTransposed:c}=Jr(i,r,t);if(c){let g=t.dataIdMap.get(l.dataId).id;u=l,o=g}let h=u.shape.length;F.assertAxesAreInnerMostDims("all",d,h);let[m,f]=F.computeOutAndReduceShapes(u.shape,d),A=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(u.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;b6(o,A,g)}if(c&&t.disposeData(l.dataId),s){let g=F.expandShapeToKeepDim(y.shape,p);y.shape=g}return y}var aY={kernelName:yo,backendName:"wasm",setupFunc:tY,kernelFunc:nY},v6;function rY(e){v6=e.wasm.cwrap(go,null,["number, number, number"])}function sY(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=i,{transposed:l,axes:d,originalAxes:p,inputWasTransposed:c}=Jr(i,r,t);if(c){let g=t.dataIdMap.get(l.dataId).id;u=l,o=g}let h=u.shape.length;F.assertAxesAreInnerMostDims("any",d,h);let[m,f]=F.computeOutAndReduceShapes(u.shape,d),A=k.sizeFromShape(f),y=t.makeOutput(m,i.dtype);if(k.sizeFromShape(u.shape)!==0){let g=t.dataIdMap.get(y.dataId).id;v6(o,A,g)}if(c&&t.disposeData(l.dataId),s){let g=F.expandShapeToKeepDim(y.shape,p);y.shape=g}return y}var iY={kernelName:go,backendName:"wasm",setupFunc:rY,kernelFunc:sY},w6;function oY(e){w6=e.wasm.cwrap(xs,null,["number","number","number","number","number"])}function lY(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r}=a,{x:s}=n,i=t.dataIdMap.get(s.dataId).id,o=i,u=s,{transposed:l,axes:d,inputWasTransposed:p}=Jr(s,r,t);if(p){let y=t.dataIdMap.get(l.dataId).id;y!==i&&(u=l,o=y)}let c=u.shape.slice(0,-1),h=t.makeOutput(c,"int32"),m=t.dataIdMap.get(h.dataId).id,f=k.sizeFromShape(h.shape),A=u.shape[d[0]];return w6(o,Mn[u.dtype],f,A,m),p&&t.disposeData(l.dataId),h}var uY={kernelName:xs,backendName:"wasm",kernelFunc:lY,setupFunc:oY},k6;function dY(e){k6=e.wasm.cwrap(bs,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function pY(e){let{inputs:t,attrs:n,backend:a}=e,r=t.x,s=a.dataIdMap.get(r.dataId).id,{filterSize:i,strides:o,pad:u,dimRoundingMode:l}=n,d=F.computePool2DInfo(r.shape,i,o,1,u,l),p=d.filterHeight,c=d.filterWidth,h=d.padInfo.top,m=d.padInfo.right,f=d.padInfo.bottom,A=d.padInfo.left,y=d.strideHeight,g=d.strideWidth,x=d.inChannels;if(d.dataFormat!=="channelsLast")throw new Error(`wasm backend does not support dataFormat:'${d.dataFormat}'. Please use 'channelsLast'.`);if(d.dilationWidth!==1||d.dilationHeight!==1)throw new Error(`was backend only supports average pooling with dilation = [1, 1], got [${d.dilationHeight}, ${d.dilationWidth}].`);let v=a.makeOutput(d.outShape,"float32"),b=a.dataIdMap.get(v.dataId).id;return k6(s,r.shape[0],r.shape[1],r.shape[2],p,c,h,m,f,A,y,g,x,b),v}var cY={kernelName:bs,backendName:"wasm",setupFunc:dY,kernelFunc:pY};function Ta(e){let{inputs:t,attrs:n}=e,{x:a}=t,{shape:r}=n,s=k.sizeFromShape(a.shape),i=k.inferFromImplicitShape(r,s);return k.assert(s===k.sizeFromShape(i),()=>`new shape: ${i}, old shape: ${a.shape}. New shape and old shape must have the same number of elements.`),e.backend.incRef(a.dataId),{dataId:a.dataId,shape:i,dtype:a.dtype}}var hY={kernelName:el,backendName:"wasm",kernelFunc:Ta},I6;function fY(e){I6=e.wasm.cwrap(vs,null,["number","array","number","number","array","number","number","number","number"])}function mY(e){let{inputs:t,backend:n,attrs:a}=e,{a:r,b:s}=t,{transposeA:i,transposeB:o}=a;if(r.dtype!=="float32"||s.dtype!=="float32")throw new Error("BatchMatMul for non non-float32 tensors not yet supported.");let u=r.shape.length,l=s.shape.length,d=i?r.shape[u-2]:r.shape[u-1],p=o?s.shape[l-1]:s.shape[l-2],c=i?r.shape[u-1]:r.shape[u-2],h=o?s.shape[l-2]:s.shape[l-1],m=r.shape.slice(0,-2),f=s.shape.slice(0,-2),A=k.sizeFromShape(m),y=k.sizeFromShape(f),g=A===y||A===1||y===1;k.assert(u>=2&&l>=2&&g,()=>`Error in matMul: the input batch dimensions must either be the same or at least one input batch dimension must be 1. Got input batch dimensions of (${m}) and (${f}).`);let x=(A>y?r.shape.slice(0,-2):s.shape.slice(0,-2)).concat([c,h]);k.assert(d===p,()=>`Error in matMul: inner shapes (${d}) and (${p}) of Tensors with shapes ${r.shape} and ${s.shape} and transposeA=${i} and transposeB=${o} must match.`);let v=i?[A,d,c]:[A,c,d],b=o?[y,h,p]:[y,p,h],w=Ta({inputs:{x:r},backend:n,attrs:{shape:v}}),N=Ta({inputs:{x:s},backend:n,attrs:{shape:b}}),C=n.dataIdMap.get(w.dataId).id,E=n.dataIdMap.get(N.dataId).id,z=i?w.shape[2]:w.shape[1],$=o?N.shape[1]:N.shape[2],S=Math.max(A,y),O=n.makeOutput([S,z,$],w.dtype),_=n.dataIdMap.get(O.dataId).id,W=new Uint8Array(new Int32Array(w.shape).buffer),G=new Uint8Array(new Int32Array(N.shape).buffer);return I6(C,W,w.shape.length,E,G,N.shape.length,i,o,_),n.disposeData(w.dataId),n.disposeData(N.dataId),O.shape=x,O}var AY={kernelName:vs,backendName:"wasm",setupFunc:fY,kernelFunc:mY};function e0(e){let{inputs:{x:t},attrs:{dtype:n},backend:a}=e,r=a.makeOutput(t.shape,n),s=a.typedArrayFromHeap(t);return a.typedArrayFromHeap(r).set(s),r}var yY={kernelName:ws,backendName:"wasm",kernelFunc:e0},gY=bn(ks),S6;function xY(e){S6=e.wasm.cwrap(_r,null,["number","number","number","number"])}function bY(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{clipValueMin:s,clipValueMax:i}=a,o=n.dataIdMap.get(r.dataId).id,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(u.dataId).id;return S6(o,s,i,l),u}var vY={kernelName:_r,backendName:"wasm",setupFunc:xY,kernelFunc:bY};function N6(e){let{inputs:t,backend:n}=e,a=k.parseAxisParam(e.attrs.axis,t[0].shape)[0],r=F.computeOutShape(t.map(h=>h.shape),a),s=t.filter(h=>k.sizeFromShape(h.shape)>0);if(s.length===1)return Jh({inputs:{x:s[0]},backend:n});let i=n.makeOutput(r,t[0].dtype);if(k.sizeFromShape(r)===0)return i;let o=s.map(h=>h.shape);if(F.assertParamsConsistent(o,a),s[0].dtype==="string"){let h=s.map(x=>{let v=k.sizeFromShape(x.shape.slice(a));return Ta({inputs:{x},backend:n,attrs:{shape:[-1,v]}})}),m=h.map(x=>({vals:n.readSync(x.dataId),shape:x.shape}));r=F.computeOutShape(h.map(x=>x.shape),1);let f=h[0].shape[0]===1,A=bA(m,r,t[0].dtype,f),y=F.computeOutShape(s.map(x=>x.shape),a);i.shape=y;let g=n.dataIdMap.get(i.dataId);return g.stringBytes=F.fromStringArrayToUint8(A),h.forEach(x=>n.disposeData(x.dataId)),i}let u=k.sizeFromShape(s[0].shape.slice(0,a)),l=0,d=s.map(h=>{let m=k.sizeFromShape(h.shape.slice(a));return l+=m,m}),p=s.map(h=>n.typedArrayFromHeap(h)),c=n.typedArrayFromHeap(i);for(let h=0;h<u;h++){let m=h*l;for(let f=0;f<p.length;f++){let A=d[f],y=h*A,g=p[f].subarray(y,y+A);c.set(g,m),m+=A}}return i}var wY={kernelName:Io,backendName:"wasm",kernelFunc:N6},T6;function kY(e){T6=e.wasm.cwrap(Is,null,["number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number","number"])}function IY(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s}=t,i=a.dataIdMap.get(r.dataId).id,o=a.dataIdMap.get(s.dataId).id,{strides:u,dilations:l,pad:d,dimRoundingMode:p,dataFormat:c}=n,h=F.convertConv2DDataFormat(c),m=F.computeConv2DInfo(r.shape,s.shape,u,l,d,p,!1,h),f=m.filterHeight,A=m.filterWidth,y=m.padInfo.top,g=m.padInfo.right,x=m.padInfo.bottom,v=m.padInfo.left,b=m.dilationHeight,w=m.dilationWidth,N=m.strideHeight,C=m.strideWidth,E=m.inChannels,z=m.outChannels,$=m.padInfo.type==="SAME"?1:0;if(m.dataFormat!=="channelsLast")throw new Error(`wasm backend Conv2D does not support dataFormat:'${m.dataFormat}'. 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Please use 'NHWC'.`);let ne=a.makeOutput(f.outShape,"float32"),Q=a.dataIdMap.get(ne.dataId).id,se=o==null?0:a.dataIdMap.get(o.dataId).id;return _6(y,H,J,K,g,b,w,v,N,C,E,z,G,$,S,O,_,W,x,A,se,m||0,Q),ne}var sJ={kernelName:hi,backendName:"wasm",setupFunc:aJ,kernelFunc:rJ},O6;function iJ(e){O6=e.wasm.cwrap(fi,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 oJ(e){let{inputs:t,attrs:n,backend:a}=e,{x:r,filter:s,bias:i,preluActivationWeights:o}=t,{strides:u,pad:l,dilations:d,dataFormat:p,dimRoundingMode:c,activation:h,leakyreluAlpha:m}=n,f=F.computeConv2DInfo(r.shape,s.shape,u,d,l,c,!0),A=zd[h];if(A==null)throw new Error(`${h} activation not yet supported for FusedDepthwiseConv2D in the wasm backend.`);let y=a.dataIdMap.get(r.dataId).id,g=a.dataIdMap.get(s.dataId).id,x=f.outChannels,v=0;if(i!=null){let Z=a.dataIdMap.get(i.dataId);if(Z.shape.length!==1)throw new Error(`FusedDepthwiseConv2D only supports rank-1 bias but got rank ${Z.shape.length}.`);if(Z.shape[0]!==x)throw new Error(`FusedDepthwiseConv2D bias shape (${Z.shape}) does not match the number of output channels (${x})`);v=Z.id}let b=f.filterHeight,w=f.filterWidth,N=f.padInfo.top,C=f.padInfo.right,E=f.padInfo.bottom,z=f.padInfo.left,$=f.dilationHeight,S=f.dilationWidth,O=f.strideHeight,_=f.strideWidth,W=f.inChannels,G=f.padInfo.type==="SAME"?1:0,H=f.batchSize,J=f.inHeight,K=f.inWidth;if(p!=="NHWC")throw new Error(`wasm backend FusedDepthwiseConv2D does not support dataFormat:'${p}'. 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HJ(e){U6=e.wasm.cwrap(Us,null,["number","array","number","number","array","array","number","number"])}function GJ(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,mode:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),d=a.map(m=>m[0]),p=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(d).buffer),h=new Uint8Array(new Int32Array(p).buffer);return U6(i,l,t.shape.length,Mn[t.dtype],c,h,ey[r],u),o}var qJ={kernelName:Us,backendName:"wasm",kernelFunc:GJ,setupFunc:HJ},XJ=!0,KJ=vn(Hs,XJ),ZJ=bn(Ho);function ty(e,t){let n=new Int32Array(e.wasm.HEAPU8.buffer,t,4),a=n[0],r=n[1],s=n[2],i=n[3];return e.wasm._free(t),{pSelectedIndices:a,selectedSize:r,pSelectedScores:s,pValidOutputs:i}}var H6;function YJ(e){H6=e.wasm.cwrap(qo,"number",["number","number","number","number","number"])}function JJ(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i}=a,{boxes:o,scores:u}=n,l=t.dataIdMap.get(o.dataId).id,d=t.dataIdMap.get(u.dataId).id,p=H6(l,d,s,r,i),{pSelectedIndices:c,selectedSize:h,pSelectedScores:m,pValidOutputs:f}=ty(t,p);return t.wasm._free(m),t.wasm._free(f),t.makeOutput([h],"int32",c)}var QJ={kernelName:qo,backendName:"wasm",setupFunc:YJ,kernelFunc:JJ},G6;function eQ(e){G6=e.wasm.cwrap(Xo,"number",["number","number","number","number","number","bool"])}function tQ(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,padToMaxOutputSize:o}=a,{boxes:u,scores:l}=n,d=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=G6(d,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:A}=ty(t,c);t.wasm._free(f);let y=t.makeOutput([m],"int32",h),g=t.makeOutput([],"int32",A);return[y,g]}var nQ={kernelName:Xo,backendName:"wasm",setupFunc:eQ,kernelFunc:tQ},q6;function aQ(e){q6=e.wasm.cwrap(Ko,"number",["number","number","number","number","number","number"])}function rQ(e){let{backend:t,inputs:n,attrs:a}=e,{iouThreshold:r,maxOutputSize:s,scoreThreshold:i,softNmsSigma:o}=a,{boxes:u,scores:l}=n,d=t.dataIdMap.get(u.dataId).id,p=t.dataIdMap.get(l.dataId).id,c=q6(d,p,s,r,i,o),{pSelectedIndices:h,selectedSize:m,pSelectedScores:f,pValidOutputs:A}=ty(t,c);t.wasm._free(A);let y=t.makeOutput([m],"int32",h),g=t.makeOutput([m],"float32",f);return[y,g]}var sQ={kernelName:Ko,backendName:"wasm",setupFunc:aQ,kernelFunc:rQ},iQ=!1,oQ=vn(Go,iQ,"bool"),X6;function lQ(e){X6=e.wasm.cwrap(Gs,null,["number","number","number","number","number"])}function uQ(e){let{inputs:t,backend:n,attrs:a}=e,{indices:r}=t,{depth:s,onValue:i,offValue:o}=a,u=n.makeOutput([...r.shape,s],"int32"),l=n.dataIdMap.get(u.dataId).id,d=n.dataIdMap.get(r.dataId).id;return X6(d,s,i,o,l),u}var dQ={kernelName:Gs,backendName:"wasm",setupFunc:lQ,kernelFunc:uQ};function pQ(e){let{inputs:{x:t},backend:n}=e,a=n.makeOutput(t.shape,t.dtype);return n.typedArrayFromHeap(a).fill(1),a}var cQ={kernelName:Zo,backendName:"wasm",kernelFunc:pQ};function hQ(e){let{inputs:t,backend:n,attrs:a}=e,{axis:r}=a;if(t.length===1)return QA({inputs:{input:t[0]},backend:n,attrs:{dim:r}});let s=t[0].shape,i=t[0].dtype;t.forEach(d=>{k.assertShapesMatch(s,d.shape,"All tensors passed to stack must have matching shapes"),k.assert(i===d.dtype,()=>"All tensors passed to stack must have matching dtypes")});let o=[],u=t.map(d=>{let p=QA({inputs:{input:d},backend:n,attrs:{dim:r}});return o.push(p),p}),l=N6({inputs:u,backend:n,attrs:{axis:r}});return o.forEach(d=>n.disposeData(d.dataId)),l}var fQ={kernelName:Yo,backendName:"wasm",kernelFunc:hQ},K6;function mQ(e){K6=e.wasm.cwrap(qs,null,["number","array","number","number","array","array","number","number"])}function AQ(e){let{inputs:{x:t},backend:n,attrs:{paddings:a,constantValue:r}}=e,s=a.map((m,f)=>m[0]+t.shape[f]+m[1]),i=n.dataIdMap.get(t.dataId).id,o=n.makeOutput(s,t.dtype),u=n.dataIdMap.get(o.dataId).id,l=new Uint8Array(new Int32Array(t.shape).buffer),d=a.map(m=>m[0]),p=a.map(m=>m[1]),c=new Uint8Array(new Int32Array(d).buffer),h=new Uint8Array(new Int32Array(p).buffer);return K6(i,l,t.shape.length,Mn[t.dtype],c,h,r,u),o}var yQ={kernelName:qs,backendName:"wasm",kernelFunc:AQ,setupFunc:mQ},gQ=!1,xQ=vn(Xs,gQ),Z6;function bQ(e){Z6=e.wasm.cwrap(Ks,null,["number","number","number"])}function vQ(e){let{inputs:t,backend:n}=e,{x:a,alpha:r}=t,s=n.dataIdMap.get(a.dataId).id,i=n.dataIdMap.get(r.dataId).id,o=n.makeOutput(a.shape,"float32"),u=n.dataIdMap.get(o.dataId).id;return Z6(s,i,u),o}var wQ={kernelName:Ks,backendName:"wasm",setupFunc:bQ,kernelFunc:vQ},Y6;function kQ(e){Y6=e.wasm.cwrap(Jo,null,["number","number","number","number"])}function IQ(e){let{backend:t,inputs:n,attrs:a}=e,{axis:r,keepDims:s}=a,{x:i}=n,o=t.dataIdMap.get(i.dataId).id,u=o,l=i,{transposed:d,axes:p,originalAxes:c,inputWasTransposed:h}=Jr(i,r,t),m=p;if(h){let x=t.dataIdMap.get(d.dataId).id;x!==o&&(l=d,u=x,m=F.getInnerMostAxes(m.length,l.shape.length))}F.assertAxesAreInnerMostDims("prod",m,l.shape.length);let[f,A]=F.computeOutAndReduceShapes(l.shape,m),y=k.sizeFromShape(A),g=t.makeOutput(f,l.dtype);if(k.sizeFromShape(l.shape)!==0){let x=t.dataIdMap.get(g.dataId).id;Y6(u,y,Mn[g.dtype],x)}if(h&&t.disposeData(d.dataId),s){let x=F.expandShapeToKeepDim(g.shape,c);g.shape=x}return g}var SQ={kernelName:Jo,backendName:"wasm",setupFunc:kQ,kernelFunc:IQ},NQ=e=>{let{backend:t,attrs:n}=e,{start:a,stop:r,step:s,dtype:i}=n,o=kA(a,r,s,i),u=t.makeOutput([o.length],i);return t.typedArrayFromHeap(u).set(o),u},TQ={kernelName:Vu,backendName:"wasm",kernelFunc:NQ},CQ=!0,EQ=vn(Es,CQ),RQ=bn(Zs),MQ=bn(Js),J6;function FQ(e){J6=e.wasm.cwrap(Ys,null,["number","number","number","number","number","number","number","number","number","number"])}function $Q(e){let{backend:t,inputs:n,attrs:a}=e,{images:r}=n,{alignCorners:s,halfPixelCenters:i,size:o}=a,[u,l]=o,[d,p,c,h]=r.shape,m=[d,u,l,h],f=t.dataIdMap.get(r.dataId),A;f.dtype!=="float32"&&(A=e0({backend:t,inputs:{x:r},attrs:{dtype:"float32"}}),f=t.dataIdMap.get(A.dataId));let y=f.id,g=t.makeOutput(m,"float32");if(k.sizeFromShape(r.shape)===0)return g;let x=t.dataIdMap.get(g.dataId).id;return J6(y,d,p,c,h,u,l,s?1:0,i?1:0,x),A!=null&&t.disposeData(A.dataId),g}var DQ={kernelName:Ys,backendName:"wasm",setupFunc:FQ,kernelFunc:$Q},Q6;function _Q(e){Q6=e.wasm.cwrap(Qs,null,["number","array","number","array","number","number"])}function OQ(e){let{inputs:t,backend:n,attrs:a}=e,{x:r}=t,{dims:s}=a,i=k.parseAxisParam(s,r.shape);if(r.shape.length===0)return Jh({inputs:{x:r},backend:n});let o=n.makeOutput(r.shape,r.dtype),u=n.dataIdMap.get(r.dataId).id,l=n.dataIdMap.get(o.dataId).id,d=new Uint8Array(new Int32Array(i).buffer),p=new Uint8Array(new Int32Array(r.shape).buffer);Q6(u,d,i.length,p,r.shape.length,l);let c=Ta({inputs:{x:o},attrs:{shape:r.shape},backend:n});return n.disposeData(o.dataId),c}var zQ={kernelName:Qs,backendName:"wasm",kernelFunc:OQ,setupFunc:_Q},e4;function PQ(e){e4=e.wasm.cwrap(fl,null,["number","number","number","number","number","number","number","number","array","number","number"])}function LQ(e){let{inputs:t,backend:n,attrs:a}=e,{image:r}=t,{radians:s,fillValue:i,center:o}=a,u=n.makeOutput(r.shape,r.dtype),l=n.dataIdMap.get(r.dataId).id,d=n.dataIdMap.get(u.dataId).id,[p,c,h,m]=r.shape,[f,A]=F.getImageCenter(o,c,h),y=i===0,g=255,x=typeof i=="number"?[i,i,i,y?0:g]:[...i,g],v=new Uint8Array(new Int32Array(x).buffer);return e4(l,p,c,h,m,s,f,A,v,x.length,d),u}var 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All inputs should only appear once. Found: ${this.inputs.map(y=>y.name)}`);Qr(this.outputs).length!==this.outputs.length&&console.warn(`The list of outputs passed to the model is redundant. All outputs should only appear once. Found: ${this.outputs.map(y=>y.name)}`),this.inputLayers=[],this.inputLayersNodeIndices=[],this.inputLayersTensorIndices=[],this.outputLayers=[],this.outputLayersNodeIndices=[],this.outputLayersTensorIndices=[],this.layers=[],this.internalContainerRefs=[];for(let y of this.outputs){let g=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;this.outputLayers.push(g),this.outputLayersNodeIndices.push(x),this.outputLayersTensorIndices.push(v)}for(let y of this.inputs){let g=y.sourceLayer,x=y.nodeIndex,v=y.tensorIndex;Ja(x===0,"input layer has >1 nodes"),Ja(v===0,"input layer has >1 tensors"),this.inputLayers.push(g),this.inputLayersNodeIndices.push(x),this.inputLayersTensorIndices.push(v)}this.inputNames=[],this.outputNames=[],this.feedInputShapes=[],this.feedInputNames=[],this.feedOutputNames=[];for(let y=0;y<this.inputLayers.length;y++){let g=this.inputLayers[y];if(!(g instanceof eu))throw new TypeError(`Input layers to a LayersModel must be InputLayer objects. Received inputs: ${e.inputs}. Input ${y} (0-based) originates from layer type ${g.getClassName()}.`);this.inputNames.push(g.name),this.feedInputShapes.push(g.batchInputShape),this.feedInputNames.push(g.name)}for(let y of this.outputLayers)this.outputNames.push(y.name);this.internalInputShapes=this.inputs.map(y=>y.shape),this.internalOutputShapes=this.outputs.map(y=>y.shape);let t={},n={},a={},r={},s={},i=[],o=(y,g,x,v,b,w)=>{(v==null||b==null||w==null)&&(v=y.sourceLayer,b=y.nodeIndex,w=y.tensorIndex);let N=v.inboundNodes[b];if(x.indexOf(N)!==-1)throw new Ea(`The tensor ${y.name} at layer "${v.name}" is part of a cycle.`);if(g.indexOf(N)!==-1)return;this.containerNodes.add(er.nodeKey(v,b)),v.id in s||(s[v.id]=Object.keys(s).length),x.indexOf(N)===-1&&x.push(N);let C=N.inboundLayers.length;for(let E=0;E<C;E++){let z=N.inputTensors[E],$=N.inboundLayers[E],S=N.nodeIndices[E],O=N.tensorIndices[E];o(z,g,x,$,S,O)}for(g.push(N);x.indexOf(N)>=0;)x.splice(x.indexOf(N),1);i.push(N)},u=[],l=[];for(let y of this.outputs)o(y,u,l);let d=i.slice().reverse();for(let y of d){n[y.id]=y,y.id in t||(t[y.id]=0);let g=t[y.id],x=a[y.outboundLayer.id]==null?0:a[y.outboundLayer.id];g=Math.max(g,x),a[y.outboundLayer.id]=g,r[y.outboundLayer.id]=y.outboundLayer,t[y.id]=g;for(let v=0;v<y.inboundLayers.length;v++){let b=y.inboundLayers[v],w=y.nodeIndices[v],N=b.inboundNodes[w],C=t[N.id]==null?0:t[N.id];t[N.id]=Math.max(g+1,C),n[N.id]=N}}let p={};for(let y in t){let g=t[y];g in p||(p[g]=[]),p[g].push(n[y])}let c={};for(let y in a){let g=a[y];g in c||(c[g]=[]),c[g].push(r[y])}let h=Object.keys(c).map(y=>parseInt(y,10)).sort(a0);this.layers=[];for(let y of h){let g=c[y];g.sort((x,v)=>{let b=s[x.id],w=s[v.id];return b<w?-1:b>w?1:0});for(let x of g)x instanceof er&&this.internalContainerRefs.push(x),this.layers.push(x)}this.layersByDepth=c,h=Object.keys(p).map(y=>parseInt(y,10)).sort(a0);let m=this.inputs.slice(),f=[];for(let y of h)for(let g of p[y]){let x=g.outboundLayer;if(x!=null){for(let v of g.inputTensors)if(m.indexOf(v)===-1)throw new Ea(`Graph disconnected: cannot obtain value for tensor ${v} at layer "${x.name}". The following previous layers were accessed without issue: ${f}`);for(let v of g.outputTensors)m.push(v);f.push(x.name)}}this.nodesByDepth=p;let A=this.layers.map(y=>y.name);for(let y of A){let g=A.filter(x=>x===y).length;if(g!==1)throw new Ea(`The name "${y}" is used ${g} times in the model. All layer names should be unique. Layer names: `+JSON.stringify(A))}this.outboundNodes=[],this.inboundNodes=[],new y0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:this.inputs.map(y=>null),outputMasks:this.outputs.map(y=>null),inputShapes:this.inputs.map(y=>y.shape),outputShapes:this.outputs.map(y=>y.shape)}),this.built=!0,this._refCount=1}assertNotDisposed(){if(this._refCount===0)throw new Error(`Container '${this.name}' is already disposed.`)}dispose(){this.assertNotDisposed();let e={refCountAfterDispose:null,numDisposedVariables:0};if(--this._refCount==0){for(let t of this.layers)e.numDisposedVariables+=t.dispose().numDisposedVariables;for(let t of this.internalContainerRefs)e.numDisposedVariables+=t.dispose().numDisposedVariables}return e.refCountAfterDispose=this._refCount,e}get trainable(){return this.trainable_}set trainable(e){this.layers.forEach(t=>{t._trainableWeights.forEach(n=>n.trainable=e)}),this.trainable_=e}get trainableWeights(){if(this._trainableWeights.length>0)throw new U("Container instance unexpectedly contains _trainableWeights.The trainable weights of a Container are a union of the trainable weights of its consituent Layers. Its own _trainableWeights must remain an empty Array.");if(!this.trainable)return[];let e=[];for(let t of this.layers)e=e.concat(t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.layers)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.layers)t.push(...n.trainableWeights);return t.concat(e)}return e}get weights(){return this.trainableWeights.concat(this.nonTrainableWeights)}loadWeights(e,t=!0){let n={},a=0;for(let s of this.layers)for(let i of s.weights){if(n[i.originalName]!=null)throw new U(`Duplicate weight name: ${i.originalName}`);n[i.originalName]=i,a++}let r=[];for(let s in e){let i=s;if(n[s]==null){let o=s.split("/");i=o.slice(0,-2).concat([o[o.length-1]]).join("/")}if(n[i]!=null)r.push([n[i],e[s]]);else if(t)throw new U(`Provided weight data has no target variable: ${s}`);delete n[i]}if(t){let s=[];for(let i in n)s.push(i);if(s.length>0)throw new U(`${s.length} of ${a} weights are not set: ${s}`)}Ny(r)}updatedConfig(){let e=this.getConfig(),t={};return t.className=this.getClassName(),t.config=e,t.kerasVersion=`tfjs-layers ${Dy}`,t.backend="TensorFlow.js",t}toJSON(e,t=!0){let n=$y(this.updatedConfig());return t?JSON.stringify(n):n}call(e,t){return V(()=>{e=mt(e);let n=new qi;for(let a=0;a<this.inputs.length;++a)n.add(this.inputs[a],e[a]);return Zd(this.outputs,n,t)})}computeMask(e,t){return V(()=>{e=mt(e);let n;return t==null?n=Bi(null,e.length):n=mt(t),this.runInternalGraph(e,n)[1]})}computeOutputShape(e){let t=m0(e);if(t.length!==this.inputLayers.length)throw new U(`Invalid inputShape argument ${e}: model has ${this.inputLayers.length} tensor inputs.`);let n={};for(let i=0;i<t.length;i++){let o=this.inputLayers[i],u=t[i],l=o.name+"_0_0";n[l]=u}let a=Object.keys(this.nodesByDepth).map(i=>parseInt(i,10)).sort(a0);if(a.length>1)for(let i of a){let o=this.nodesByDepth[i];for(let u of o){let l=u.outboundLayer;if(this.inputLayers.map(m=>m.id).indexOf(l.id)!==-1)continue;let d=[];for(let m=0;m<u.inboundLayers.length;m++){let f=u.inboundLayers[m],A=u.nodeIndices[m],y=u.tensorIndices[m],g=`${f.name}_${A}_${y}`,x=n[g];d.push(x)}let p=l.computeOutputShape(Fn(d)),c=m0(p),h=l.inboundNodes.indexOf(u);for(let m=0;m<c.length;m++){let f=`${l.name}_${h}_${m}`;n[f]=c[m]}}}let r=[],s=[];for(let i=0;i<this.outputLayers.length;i++){let o=this.outputLayers[i],u=this.outputLayersNodeIndices[i],l=this.outputLayersTensorIndices[i],d=`${o.name}_${u}_${l}`;s.push(d)}for(let i=0;i<s.length;i++){let o=s[i];Ja(o in n),r.push(n[o])}return Fn(r)}runInternalGraph(e,t){t==null&&(t=Bi(null,e.length));let n={};for(let o=0;o<this.inputs.length;++o){let u=this.inputs[o],l=e[o],d=t[o];n[u.id]=[l,d]}let a=Object.keys(this.nodesByDepth).map(o=>parseInt(o,10)).sort(a0);for(let o of a){let u=this.nodesByDepth[o];for(let l of u){let d=l.outboundLayer,p=l.inputTensors,c=l.outputTensors,h=new Array;for(let m of p)m.id in n&&h.push(n[m.id]);if(h.length===p.length){let m={},f,A,y,g;if(l.callArgs!=null&&(m=l.callArgs),h.length===1){let[x,v]=h[0];m.mask==null&&(m.mask=v),y=mt(d.call(x,m)),g=mt(d.computeMask(x,v)),f=[x],A=[v]}else f=h.map(x=>x[0]),A=h.map(x=>x[1]),m.mask==null&&(m.mask=A),y=mt(d.call(f,m)),g=mt(d.computeMask(f,A));if(d.activityRegularizer)throw new Oe("LayersModel invocation with concrete Tensor value(s) in the presence of activity regularizer(s) is not supported yet.");for(let x=0;x<c.length;++x){let v=c[x],b=y[x],w=g[x];n[v.id]=[b,w]}}}}let r=[],s=[],i=[];for(let o of this.outputs){Ja(o.id in n,`Could not compute output ${o.name} : ${o.id}`);let[u,l]=n[o.id];i.push(u.shape),r.push(u),s.push(l)}return[r,s,i]}buildNodeConversionMap(e){let t={},n;for(let a of this.layers){n=a instanceof er?1:0;for(let r=0;r<a.inboundNodes.length;r++){let s=er.nodeKey(a,r);this.containerNodes.has(s)&&(t[s]=n,n+=1)}}return t}getLayer(e,t){if(t!=null){if(this.layers.length<=t)throw new U(`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 U("Provide either a layer name or layer index");for(let n of this.layers)if(n.name===e)return n;throw new U(`No such layer: ${e}`)}calculateLosses(){return V(()=>{let e=[];for(let t of this.layers)for(let n=0;n<t.inboundNodes.length;++n){let a=er.nodeKey(t,n);this.containerNodes.has(a)&&e.push(...t.calculateLosses())}return e})}getConfig(){let e={name:this.name},t=this.buildNodeConversionMap(this.layers),n=[];for(let s of this.layers){let i=s.getClassName(),o=s.getConfig(),u=[];for(let d=0;d<s.inboundNodes.length;d++){let p=s.inboundNodes[d],c=er.nodeKey(s,d),h={};if(this.containerNodes.has(c)){if(p.callArgs)try{JSON.stringify(p.callArgs),h=p.callArgs}catch(m){console.warn(`Layer ${s.name} was passed non-serializable keyword arguments: ${p.callArgs}. They will not be included in the serialized model (and thus will be missing at deserialization time).`),h={}}if(p.inboundLayers.length>0){let m=[];for(let f=0;f<p.inboundLayers.length;f++){let A=p.inboundLayers[f],y=p.nodeIndices[f],g=p.tensorIndices[f],x=er.nodeKey(A,y),v=t[x];v==null&&(v=0),m.push([A.name,v,g,h])}u.push(m)}}}let l={};l.name=s.name,l.className=i,l.config=o,l.inboundNodes=u,n.push(l)}e.layers=n;let a=[];for(let s=0;s<this.inputLayers.length;s++){let i=this.inputLayers[s],o=this.inputLayersNodeIndices[s],u=er.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let d=this.inputLayersTensorIndices[s];a.push([i.name,l,d])}e.inputLayers=a;let r=[];for(let s=0;s<this.outputLayers.length;s++){let i=this.outputLayers[s],o=this.outputLayersNodeIndices[s],u=er.nodeKey(i,o);if(!this.containerNodes.has(u))continue;let l=t[u];l==null&&(l=0);let d=this.outputLayersTensorIndices[s];r.push([i.name,l,d])}return e.outputLayers=r,e}static fromConfig(e,t,n={},a=!1){let r={},s={};function i(f,A){f.name in s?s[f.name].push(A):s[f.name]=[A]}function o(f,A){let y=[],g;for(let x of A){let v=x[0],b=x[1],w=x[2];if(g=x[3]==null?{}:x[3],!(v in r)){i(f,A);return}let N=r[v];if(N.inboundNodes.length<=b){i(f,A);return}let C=N.inboundNodes[b];y.push(C.outputTensors[w])}y.length>0&&f.apply(Fn(y),g)}function u(f){let A=f.name,y=$a(f,t.customObjects!=null?t.customObjects:{});y.setFastWeightInitDuringBuild(a),r[A]=y,f.inboundNodes.forEach(g=>{if(!(g instanceof Array))throw new U(`Corrupted configuration, expected array for nodeData: ${g}`);i(y,g)})}let l=t.name,d=t.layers;for(let f of d)u(f);for(;!Zne(s);)for(let f of d){let A=r[f.name];if(A.name in s){let y=s[A.name];delete s[A.name];for(let g of y)o(A,g)}}let p=[],c=[],h=t.inputLayers;for(let f of h){let A=f[0],y=f[1],g=f[2];Ja(A in r);let x=r[A].inboundNodes[y].outputTensors;p.push(x[g])}let m=t.outputLayers;for(let f of m){let A=f[0],y=f[1],g=f[2];Ja(A in r);let x=r[A].inboundNodes[y].outputTensors;c.push(x[g])}return new e({inputs:p,outputs:c,name:l})}get stateful(){if(this._stateful)throw new U("Container instance unexpectedly has _stateful = true. The statefulness of a Container is determined by the Layers it contains. Its _stateful property must remain the default false.");for(let e of this.layers)if(e.stateful)return!0;return!1}resetStates(){V(()=>{this.layers.forEach(e=>{e.stateful&&e.resetStates()})})}};function bre(e,t,n){let a=t.length;if(e==null||Array.isArray(e)&&e.length===0)return t.map(r=>null);if(a===1)return Array.isArray(e)&&e.length===1?e:typeof e=="object"&&t[0]in e?[e[t[0]]]:[e];if(Array.isArray(e)){if(e.length!==a)throw new Error(`Provided ${n} is an array of ${e.length} element(s), but the model has ${a} outputs. Make sure a set of weights is provided for each model output.`);return e}else if(typeof e=="object"&&Object.keys(e).length>0&&typeof e[Object.keys(e)[0]]=="object"){let r=[];return t.forEach(s=>{s in e?r.push(e[s]):r.push(null)}),r}else throw new Error(`The model has multiple (${a}) outputs, so ${n} must be either an array with ${a} elements or an object with ${t} keys. Provided ${n} not understood: ${JSON.stringify(e)}`)}function d8(e,t){return bre(e,t,"classWeight")}async function p8(e,t,n,a){if(t!=null||a!=null)throw new Error("Support sampleWeight is not implemented yet");if(n!=null){let r=V(()=>{if(e.shape.length===1)return e.clone();if(e.shape.length===2)if(e.shape[1]>1){let o=1;return e.argMax(o)}else{if(e.shape[1]===1)return e.reshape([e.shape[0]]);throw new Error(`Encountered unexpected last-dimension size (${e.shape[1]}) during handling of class weights. The size is expected to be >= 1.`)}else throw new Error(`Unexpected rank of target (y) tensor (${e.rank}) during handling of class weights. The rank is expected to be 1 or 2.`)}),s=Array.from(await r.data());Ie(r);let i=[];return s.forEach(o=>{if(n[o]==null)throw new Error(`classWeight must contain all classes in the training data. The class ${o} exists in the data but not in classWeight`);i.push(n[o])}),Dt(i,"float32")}else return null}function vre(e,t){return B(e,t)}var wre=32;function c8(e,t){let n,a,r=t;n=r.xs,a=r.ys,k.assert(n!=null&&a!=null,()=>`A Dataset iterator for fitDataset() is expected to generate objects of the form \`{xs: xVal, ys: yVal}\`, where the two values may be \`tf.Tensor\`, an array of Tensors, or a map of string to Tensor. The provided Dataset instead generates ${t}`);let s=h8("input",e.inputNames,n),i=h8("output",e.outputNames,a),o=s[0].shape[0];k.assert(s.length===e.inputs.length,()=>`LayersModel has ${e.inputs.length} inputs, but the dataset provides ${s.length} inputs. (Expected input keys: ${JSON.stringify(e.inputNames)})`),k.assert(i.length===e.outputs.length,()=>`LayersModel has ${e.outputs.length} outputs, but the dataset provides ${i.length} outputs. (Expected output keys: ${JSON.stringify(e.outputNames)})`);for(let u=0;u<s.length;u++)k.assert(s[u].shape[0]===o,()=>`Batch size mismatch: input ${e.inputNames[u]} has ${s[u].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);for(let u=0;u<i.length;u++)k.assert(i[u].shape[0]===o,()=>`Batch size mismatch: output ${e.outputNames[u]} has ${i[u].shape[0]}; expected ${o} based on input ${e.inputNames[0]}.`);return{xs:s,ys:i}}function h8(e,t,n){if(n instanceof We)return[n];if(Array.isArray(n))return k.assert(n.length===t.length,()=>`Received an array of ${n.length} Tensors, but expected ${t.length} to match the ${e} keys ${t}.`),n;{let a=[];for(let r of t){if(n[r]==null)throw new U(`The feature data generated by the dataset lacks the required ${e} key '${r}'.`);a.push(n[r])}return a}}function kre(e){if(e.length===3)throw new Oe("Validation with sample weights is not implemented yet.");return{xs:e[0],ys:e[1]}}async function Ire(e,t,n){let a=n.batchesPerEpoch!=null;if(k.assert(e.optimizer!=null,()=>"You must compile a model before training/testing. Use LayersModel.compile(modelCompileConfig)."),k.assert(n!=null,()=>"For fitDataset(), the 2nd argument (config) is required, but it is not provided in this call."),k.assert(n.epochs!=null&&n.epochs>0&&Number.isInteger(n.epochs),()=>`For fitDataset(), config.epochs is expected to be a positive integer, but got ${n.epochs}`),k.assert(!a||n.batchesPerEpoch>0&&Number.isInteger(n.batchesPerEpoch),()=>`For fitDataset(), config.batchesPerEpoch is expected to be a positive integer if specified, but got ${n.batchesPerEpoch}`),k.assert(n.validationSplit==null,()=>"`validationSplit` is not supported by `fitDataset()`. Use validationData instead."),e.isTraining)throw new Error("Cannot start training because another fit() call is ongoing.");e.isTraining=!0;try{let r=n.validationData!=null,s,i;if(r)if(f8(n.validationData))k.assert(n.validationBatches==null||n.validationBatches>0&&Number.isInteger(n.validationBatches),()=>`For fitDataset() with dataset-based validation, config.validationBatches is expected not to be provided, or to be a positive integer, but got ${n.validationBatches}`);else{let A=kre(n.validationData);s=A.xs,i=A.ys}let o=e.makeTrainFunction(),u=e.getDedupedMetricsNames(),l;r?l=u.slice().concat(u.map(A=>"val_"+A)):l=u.slice();let d=J4(n.callbacks,n.yieldEvery),p=n.verbose==null?1:n.verbose,{callbackList:c,history:h}=Q4(d,p,n.epochs,null,null,Sre(t,n),null,r,l);c.setModel(e),e.history=h,await c.onTrainBegin(),e.stopTraining_=!1;let m=n.initialEpoch==null?0:n.initialEpoch,f=await t.iterator();for(;m<n.epochs;){let A={};await c.onEpochBegin(m);let y=0,g=0;for(a||(f=await t.iterator());a?y<n.batchesPerEpoch:!0;){let x=await f.next();if(a&&x.done){console.warn(`You provided \`batchesPerEpoch\` as ${n.batchesPerEpoch}, but your dataset iterator ran out of data after ${y} batches; interrupting training. 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t}makeTrainFunction(){return e=>{let t=[],n=e.slice(0,this.inputs.length),a=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),r=e.slice(this.inputs.length+this.outputs.length,this.inputs.length+this.outputs.length*2),s=[],i=()=>{let l=[];for(let h=0;h<this.inputs.length;++h)l.push({key:this.inputs[h],value:n[h]});let d=new qi(l),p=Zd(this.outputs,d,{training:!0}),c;for(let h=0;h<this.lossFunctions.length;++h){let m=this.lossFunctions[h](a[h],p[h]);r[h]!=null&&(m=vre(m,r[h]));let f=Nt(m);t.push(f),h===0?c=m:c=ie(c,m)}for(let h=0;h<this.metricsTensors.length;++h){let m;if(this.outputs.length>1&&h<this.outputs.length)m=t[h];else{let f=this.metricsTensors[h][0],A=this.metricsTensors[h][1];m=Nt(f(a[A],p[A]))}Xt(m),s.push(m)}return c=Nt(c),this.calculateLosses().forEach(h=>{c=ie(c,h)}),c},o=this.collectedTrainableWeights.map(l=>l.read()),u=!0;return[this.optimizer_.minimize(i,u,o)].concat(s)}}makeTestFunction(){this.testFunction=e=>V(()=>{let t=[],n,a=e.slice(0,this.inputs.length),r=e.slice(this.inputs.length,this.inputs.length+this.outputs.length),s=[];for(let u=0;u<this.inputs.length;++u)s.push({key:this.inputs[u],value:a[u]});let i=new qi(s),o=Zd(this.outputs,i);for(let u=0;u<this.lossFunctions.length;++u){let l=this.lossFunctions[u],d=Nt(l(r[u],o[u]));u===0?n=d:n=ie(n,d),t.push(n)}for(let u=0;u<this.metricsTensors.length;++u){let l=this.metricsTensors[u][0],d=this.metricsTensors[u][1],p=Nt(l(r[d],o[d]));t.push(p)}return t})}async fit(e,t,n={}){return Ere(this,e,t,n)}async fitDataset(e,t){return Ire(this,e,t)}async trainOnBatch(e,t){let n=await this.standardizeUserData(e,t),a=n[0],r=n[1],s=this.makeTrainFunction()(a.concat(r)),i=[];for(let o of s){let u=await o.data();i.push(u[0])}return Ie(s),Fn(i)}getNamedWeights(e){let t=[],n=e!=null&&e.trainableOnly,a=n?this.trainableWeights:this.weights,r=this.getWeights(n);for(let s=0;s<a.length;++s)n&&!a[s].trainable||t.push({name:a[s].originalName,tensor:r[s]});return t}set stopTraining(e){this.stopTraining_=e}get stopTraining(){return this.stopTraining_}get optimizer(){return this.optimizer_}set optimizer(e){this.optimizer_!==e&&(this.optimizer_=e,this.isOptimizerOwned=!1)}dispose(){let e=super.dispose();if(e.refCountAfterDispose===0&&this.optimizer!=null&&this.isOptimizerOwned){let t=Wc().numTensors;this.optimizer_.dispose(),e.numDisposedVariables+=t-Wc().numTensors}return e}getLossIdentifiers(){let e;if(typeof this.loss=="string")e=vr(this.loss);else if(Array.isArray(this.loss)){for(let t of this.loss)if(typeof t!="string")throw new Error("Serialization of non-string loss is not supported.");e=this.loss.map(t=>vr(t))}else{let t=Object.keys(this.loss);e={};let n=this.loss;for(let a of t)if(typeof n[a]=="string")e[a]=vr(n[a]);else throw new Error("Serialization of non-string loss is not supported.")}return e}getMetricIdentifiers(){if(typeof this.metrics=="string"||typeof this.metrics=="function")return[vr(I0(this.metrics))];if(Array.isArray(this.metrics))return this.metrics.map(e=>vr(I0(e)));{let e={};for(let t in this.metrics)e[t]=vr(I0(this.metrics[t]));return e}}getTrainingConfig(){return{loss:this.getLossIdentifiers(),metrics:this.getMetricIdentifiers(),optimizer_config:{class_name:this.optimizer.getClassName(),config:this.optimizer.getConfig()}}}loadTrainingConfig(e){if(e.weighted_metrics!=null)throw new Error("Loading weight_metrics is not supported yet.");if(e.loss_weights!=null)throw new Error("Loading loss_weights is not supported yet.");if(e.sample_weight_mode!=null)throw new Error("Loading sample_weight_mode is not supported yet.");let t=Kd(e.optimizer_config),n=$a(t),a;if(typeof e.loss=="string")a=Vi(e.loss);else if(Array.isArray(e.loss))a=e.loss.map(s=>Vi(s));else if(e.loss!=null){a={};for(let s in e.loss)a[s]=Vi(e.loss[s])}let r;if(Array.isArray(e.metrics))r=e.metrics.map(s=>Vi(s));else if(e.metrics!=null){r={};for(let s in e.metrics)r[s]=Vi(e.metrics[s])}this.compile({loss:a,metrics:r,optimizer:n})}async save(e,t){if(typeof e=="string"){let i=Cn.getSaveHandlers(e);if(i.length===0)throw new U(`Cannot find any save handlers for URL '${e}'`);if(i.length>1)throw new U(`Found more than one (${i.length}) save handlers for URL '${e}'`);e=i[0]}if(e.save==null)throw new U("LayersModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");let n=await Cn.encodeWeights(this.getNamedWeights(t)),a=!1,r=null,s={modelTopology:this.toJSON(r,a),format:Dre,generatedBy:`TensorFlow.js tfjs-layers v${Dy}`,convertedBy:null};if((t==null?!1:t.includeOptimizer)&&this.optimizer!=null){s.trainingConfig=this.getTrainingConfig();let i="optimizer",{data:o,specs:u}=await Cn.encodeWeights(await this.optimizer.getWeights(),i);n.specs.push(...u),n.data=Cn.concatenateArrayBuffers([n.data,o])}if(this.userDefinedMetadata!=null){let i=!0;i8(this.userDefinedMetadata,this.name,i),s.userDefinedMetadata=this.userDefinedMetadata}return s.weightData=n.data,s.weightSpecs=n.specs,e.save(s)}setUserDefinedMetadata(e){i8(e,this.name),this.userDefinedMetadata=e}getUserDefinedMetadata(){return this.userDefinedMetadata}};wr.className="Model";re.registerClass(wr);var x8=class extends wr{};x8.className="Functional";re.registerClass(x8);async function _re(e,t){"modelTopology"in e||(e={modelTopology:e}),e=e;let n=e.modelTopology;n.model_config!=null&&(n=n.model_config);let a=Kd(n),r=$a(a,t);if(e.weightsManifest!=null){let s=await Cn.loadWeights(e.weightsManifest,e.pathPrefix,r.weights.map(o=>o.originalName)),i={};for(let o of r.weights)i[o.originalName]=s[o.originalName];r.loadWeights(i),Ie(s)}return r}async function Ore(e,t){if(t==null&&(t={}),typeof e=="string"){let n=Cn.getLoadHandlers(e,t);if(n.length===0)n.push(Cn.browserHTTPRequest(e,t));else if(n.length>1)throw new U(`Found more than one (${n.length}) load handlers for URL '${e}'`);e=n[0]}return zre(e,void 0,t)}async function zre(e,t,n){if(n==null&&(n={}),e.load==null)throw new U("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let a=await e.load(),r=a.modelTopology;r.model_config!=null&&(r=r.model_config);let s=n.strict==null?!0:n.strict,i=a.weightData!=null&&a.weightSpecs!=null&&s,o=$a(Kd(r),t,i),u=a.trainingConfig;if(u!=null&&o.loadTrainingConfig(u),a.userDefinedMetadata!=null&&o.setUserDefinedMetadata(a.userDefinedMetadata),a.weightData!=null){if(a.weightSpecs==null)throw new U("LayersModel artifacts contains weight data, but not weight specs. Therefore loading of weights cannot proceed.");let{modelWeights:l,optimizerWeights:d}=Pre(a.weightData,a.weightSpecs);o.loadWeights(l,s),o.optimizer!=null&&d.length>0&&await o.optimizer.setWeights(d),Ie(l),Ie(d.map(p=>p.tensor))}return o}function Pre(e,t){let n=Cn.decodeWeights(e,t),a={},r=[];return t.forEach(s=>{s.group==="optimizer"?r.push({name:s.name,tensor:n[s.name]}):a[s.name]=n[s.name]}),{modelWeights:a,optimizerWeights:r}}var au=class extends wr{constructor(e){super({inputs:[],outputs:[]});if(e=e||{},this.trainable=!0,this.built=!1,this.name=e.name!=null?e.name:f0("sequential_"),e.layers!=null)for(let t of e.layers)this.add(t)}checkShape(e){if(e.inboundNodes[0].outputTensors[0].shape.some(t=>t<0))throw new U(`Negative dimension size caused by adding layer ${e.name} with input shape [${e.inboundNodes[0].inputTensors[0].shape}]`)}add(e){let t=e instanceof au||e instanceof wr,n;if(t){if(n=e,n.outputs.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");if(n.inputs.length!==1)throw new U("All layers in a Sequential model should have a single input tensor. For multi-input layers, use the functional API.")}if(this.outputs.length===0){if(e.inboundNodes.length===0){if(e.batchInputShape==null)throw new U("The first layer in a Sequential model must get an `inputShape` or `batchInputShape` argument.");let a=G4({batchShape:e.batchInputShape,dtype:e.dtype,name:e.name+"_input"});e.apply(a)}if(t)this.outputs=n.outputs,this.inputs=n.inputs;else{if(e.inboundNodes.length!==1)throw new U(`A layer added to a Sequential model must not already be connected somewhere else. LayersModel received layer ${e.name} which has ${e.inboundNodes.length} pre-existing inbound connections.`);if(e.inboundNodes[0].outputTensors.length!==1)throw new U("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[e.inboundNodes[0].outputTensors[0]],this.inputs=H4(this.outputs[0])}this.inboundNodes=[],new y0({outboundLayer:this,inboundLayers:[],nodeIndices:[],tensorIndices:[],inputTensors:this.inputs,outputTensors:this.outputs,inputMasks:Bi(null,this.inputs.length),outputMasks:[null],inputShapes:this.inputs.map(a=>a.shape),outputShapes:this.outputs[0].shape})}else{let a=e.apply(this.outputs[0]);if(Array.isArray(a))throw new TypeError("All layers in a Sequential model should have a single output tensor. For multi-output layers, use the functional API.");this.checkShape(e),this.outputs=[a],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}this.layers.push(e),this.built=!1}pop(){if(this.layers.length===0)throw new TypeError("There are no layers in the model.");if(this.layers.pop(),this.layers.length===0)this.outputs=[],this.inboundNodes=[],this.outboundNodes=[];else{let e=this.layers.length-1;this.layers[e].outboundNodes=[],this.outputs=[this.layers[e].output],this.inboundNodes[0].outputTensors=this.outputs,this.inboundNodes[0].outputShapes=[this.outputs[0].shape]}}call(e,t){return this.model==null&&this.build(),this.model.call(e,t)}build(e){if(st(e),this.inputs.length===0||this.outputs.length===0)throw new TypeError("Sequential model cannot be built: model is empty. Add some layers first.");this.model=new wr({inputs:this.inputs,outputs:this.outputs[0],name:this.name+"_model"}),this.model.trainable=this.trainable,this.supportsMasking=this.model.supportsMasking,this.inputLayers=this.model.inputLayers,this.inputLayersNodeIndices=this.model.inputLayersNodeIndices,this.inputLayersTensorIndices=this.model.inputLayersTensorIndices,this.outputLayers=this.model.outputLayers,this.outputLayersNodeIndices=this.model.outputLayersNodeIndices,this.outputLayersTensorIndices=this.model.outputLayersTensorIndices,this.nodesByDepth=this.model.nodesByDepth,this.containerNodes=this.model.containerNodes,this.outputNames=this.model.outputNames,this.inputNames=this.model.inputNames,this.built=!0}countParams(){return this.built||this.build(),super.countParams()}summary(e,t,n=console.log){this.built||this.build(),super.summary(e,t,n)}setWeights(e){this.model==null&&this.build(),this.model.setWeights(e)}evaluate(e,t,n={}){if(!this.built)throw new Ea("The model needs to be compiled before being used.");return this.model.evaluate(e,t,n)}async evaluateDataset(e,t){if(!this.built)throw new Ea("The model needs to be compiled before being used.");return this.model.evaluateDataset(e,t)}predict(e,t={}){return this.model==null&&this.build(),this.model.predict(e,t)}predictOnBatch(e){return this.model==null&&this.build(),this.model.predictOnBatch(e)}compile(e){this.build(),this.model.compile(e),this.optimizer_=this.model.optimizer,this.isOptimizerOwned=this.model.isOptimizerOwned,this.loss=this.model.loss,this.metrics=this.model.metrics,this.metricsTensors=this.model.metricsTensors,this.metricsNames=this.model.metricsNames}get optimizer(){return this.model==null?void 0:this.model.optimizer}set optimizer(e){this.model.optimizer=e}async fit(e,t,n={}){if(!this.built)throw new Ea("The model needs to be compiled before being used.");return this.model.fit(e,t,n)}async fitDataset(e,t){if(!this.built)throw new Ea("The model needs to be compiled before being used.");return this.model.fitDataset(e,t)}async trainOnBatch(e,t){return this.model.trainOnBatch(e,t)}static fromConfig(e,t,n={},a=!1){let r,s={};if(t instanceof Array){if(t[0].className==null||t[0].className==="Merge")throw new U("Legacy serialization format not supported yet.");r=t}else k.assert(t.layers!=null,()=>"When the config data for a Sequential model is not an Array, it must be an Object that contains the 'layers' field."),r=t.layers,delete t.layers,s=t;let i=new e(s);if(!(i instanceof au))throw new Oe(`Sequential.fromConfig called on non-Sequential input: ${i}`);for(let o of r){let u=$a(o,void 0,a);a&&u.setFastWeightInitDuringBuild(!0),i.add(u)}return i}set stopTraining(e){if(this.model==null)throw new U("Cannot set the stopTraining property of a sequential model before it is compiled.");this.model.stopTraining=e}get stopTraining(){if(this.model==null)throw new U("Cannot get the stopTraining property of a sequential model before it is compiled.");return this.model.stopTraining}getConfig(){let e=[];for(let t of this.layers){let n={};n.className=t.getClassName(),n.config=t.getConfig(),e.push(n)}return{name:this.name,layers:e}}};au.className="Sequential";re.registerClass(au);function Lre(e){return new wr(e)}function Wre(e){return new au(e)}function Bre(e,t){return t==null&&(t={}),Ore(e,t)}function b8(e){return G4(e)}function Vre(e,t){ba.registerCallbackConstructor(e,t)}var Dn=class extends re.Serializable{getConfig(){return{}}},v8=class extends Dn{apply(e,t=1){return fae(e,t)}};v8.className="elu";re.registerClass(v8);var w8=class extends Dn{apply(e){return ih(e)}};w8.className="selu";re.registerClass(w8);var k8=class extends Dn{apply(e){return Xa(e)}};k8.className="relu";re.registerClass(k8);var I8=class extends Dn{apply(e){return V(()=>Ml(6,Xa(e)))}};I8.className="relu6";re.registerClass(I8);var S8=class extends Dn{apply(e){return e}};S8.className="linear";re.registerClass(S8);var N8=class extends Dn{apply(e){return En(e)}};N8.className="sigmoid";re.registerClass(N8);var T8=class extends Dn{apply(e){return Aae(e)}};T8.className="hardSigmoid";re.registerClass(T8);var C8=class extends Dn{apply(e){return Ci(e)}};C8.className="softplus";re.registerClass(C8);var E8=class extends Dn{apply(e){return mae(e)}};E8.className="softsign";re.registerClass(E8);var R8=class extends Dn{apply(e){return Si(e)}};R8.className="tanh";re.registerClass(R8);var Wy=class extends Dn{apply(e,t=-1){return gd(e,t)}};Wy.className="softmax";re.registerClass(Wy);var M8=class extends Dn{apply(e,t=-1){return Qc(e,t)}};M8.className="logSoftmax";re.registerClass(M8);var F8=class extends Dn{apply(e,t=1){return V(()=>En(e.mul(t)).mul(e))}};F8.className="swish";re.registerClass(F8);var $8=class extends Dn{apply(e){return V(()=>B(e,Si(Ci(e))))}};$8.className="mish";re.registerClass($8);function as(e){return e.getClassName()}function By(e,t={}){return Bd(e,re.SerializationMap.getMap().classNameMap,t,"activation")}function rs(e){if(e==null){let t={};return t.className="linear",t.config={},By(t)}if(typeof e=="string"){let t={};return t.className=e,t.config={},By(t)}else return e instanceof Dn?e:By(e)}function Vy(e){if(e!=null&&typeof e!="object")throw new Error(`Argument to L1L2 regularizer's constructor is expected to be an object, but received: ${e}`)}var D8=class extends re.Serializable{},Jd=class extends D8{constructor(e){super();Vy(e),this.l1=e==null||e.l1==null?.01:e.l1,this.l2=e==null||e.l2==null?.01:e.l2,this.hasL1=this.l1!==0,this.hasL2=this.l2!==0}apply(e){return V(()=>{let t=$t([1]);return this.hasL1&&(t=ie(t,Se(B(this.l1,Wt(e))))),this.hasL2&&(t=ie(t,Se(B(this.l2,Gd(e))))),t.asScalar()})}getConfig(){return{l1:this.l1,l2:this.l2}}static fromConfig(e,t){return new e({l1:t.l1,l2:t.l2})}};Jd.className="L1L2";re.registerClass(Jd);function jre(e){return Vy(e),new Jd({l1:e!=null?e.l1:null,l2:0})}function Ure(e){return Vy(e),new Jd({l2:e!=null?e.l2:null,l1:0})}var _8={l1l2:"L1L2"};function dt(e){return sy(e)}function O8(e,t={}){return Bd(e,re.SerializationMap.getMap().classNameMap,t,"regularizer")}function xt(e){if(e==null)return null;if(typeof e=="string"){let t={className:e in _8?_8[e]:e,config:{}};return O8(t)}else return e instanceof D8?e:O8(e)}var jy=class extends Xe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null&&(this.maxValue=e.maxValue)}call(e,t){e=Pe(e);let n=Xa(e);return this.maxValue!=null&&(n=Rn(n,0,this.maxValue)),n}computeOutputShape(e){return e}getConfig(){let e={maxValue:this.maxValue},t=super.getConfig();return Object.assign(e,t),e}};jy.className="ReLU";re.registerClass(jy);var Uy=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_ALPHA=.3,e==null&&(e={}),this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Pe(e);return dd(n,this.alpha)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Uy.className="LeakyReLU";re.registerClass(Uy);var Hy=class extends Xe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA_INITIALIZER="zeros",e==null&&(e={}),this.supportsMasking=!0,this.alphaInitializer=gt(e.alphaInitializer||this.DEFAULT_ALPHA_INITIALIZER),this.alphaRegularizer=xt(e.alphaRegularizer),this.alphaConstraint=Ut(e.alphaConstraint),e.sharedAxes==null)this.sharedAxes=null;else if(Array.isArray(e.sharedAxes))this.sharedAxes=e.sharedAxes;else if(typeof e.sharedAxes=="number")this.sharedAxes=[e.sharedAxes];else throw new U(`Expected sharedAxes to be a number or an array of numbers, but got ${e.sharedAxes}`)}build(e){e=st(e);let t=e.slice(1);if(this.sharedAxes!=null)for(let a of this.sharedAxes)t[a-1]=1;this.alpha=this.addWeight("alpha",t,"float32",this.alphaInitializer,this.alphaRegularizer,!0,this.alphaConstraint);let n={};if(this.sharedAxes!=null)for(let a=1;a<e.length;++a)n[a]=e[a];this.inputSpec=[new Ot({ndim:e.length,axes:n})],this.built=!0}call(e,t){return e=Pe(e),md(e,this.alpha.read())}getConfig(){let e={alphaInitializer:Tt(this.alphaInitializer),alphaRegularizer:dt(this.alphaRegularizer),alphaConstraint:jt(this.alphaConstraint),sharedAxes:this.sharedAxes},t=super.getConfig();return Object.assign(e,t),e}};Hy.className="PReLU";re.registerClass(Hy);var Gy=class extends Xe{constructor(e){super(e==null?{}:e);if(this.DEFAULT_ALPHA=1,e==null&&(e={}),e.alpha!=null&&e.alpha!==this.DEFAULT_ALPHA)throw new Oe(`Non-default alpha value (${e.alpha}) is not supported by the ELU layer yet.`);this.alpha=e.alpha==null?this.DEFAULT_ALPHA:e.alpha}call(e,t){let n=Pe(e);return Cl(n)}computeOutputShape(e){return e}getConfig(){let e={alpha:this.alpha},t=super.getConfig();return Object.assign(e,t),e}};Gy.className="ELU";re.registerClass(Gy);var qy=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_THETA=1,e==null&&(e={}),this.theta=e.theta==null?this.DEFAULT_THETA:e.theta}call(e,t){let n=Pe(e);return n.mul(Ud(n.greater(this.theta),"float32"))}computeOutputShape(e){return e}getConfig(){let e={theta:this.theta},t=super.getConfig();return Object.assign(e,t),e}};qy.className="ThresholdedReLU";re.registerClass(qy);var Xy=class extends Xe{constructor(e){super(e==null?{}:e);this.DEFAULT_AXIS=1,e==null&&(e={}),this.softmax=new Wy().apply,this.axis=e.axis==null?this.DEFAULT_AXIS:e.axis}call(e,t){let n=Pe(e);return this.softmax(n,this.axis)}computeOutputShape(e){return e}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};Xy.className="Softmax";re.registerClass(Xy);function ru(e,t,n){if(typeof e=="number")return Bi(e,t);if(e.length!==t)throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${e.length} elements.`);for(let a=0;a<t;++a){let r=e[a];if(!dae(r))throw new U(`The ${n} argument must be an integer or tuple of ${t} integers. Received: ${JSON.stringify(e)} including a non-integer number ${r}`)}return e}function Da(e,t,n,a,r=1){if(e==null)return e;let s=t+(t-1)*(r-1),i;return n==="same"?i=e:i=e-s+1,Math.floor((i+a-1)/a)}function tr(e,t,n,a){if(e==null)return null;if(a==="valid")e=e*t+ts([n-t,0]);else if(a==="same")e=e*t;else throw new U(`Unsupport padding mode: ${a}.`);return e}function Ky(e,t){return V(()=>(Ft(t),t==="channelsFirst"?Qe(e,[0,2,3,1]):e))}function z8(e,t){return V(()=>(Ft(t),t==="channelsFirst"?Qe(e,[0,2,3,4,1]):e))}function Hre(e,t,n,a=1,r="valid",s,i=1){return V(()=>{if(s==null&&(s=Ca()),Ft(s),e.shape.length!==3)throw new U(`The input of a conv1dWithBias operation should be 3, but is ${e.shape.length} instead.`);if(t.shape.length!==3)throw new U(`The kernel for a conv1dWithBias operation should be 3, but is ${t.shape.length} instead`);if(n!=null&&n.shape.length!==1)throw new U(`The bias for a conv1dWithBias operation should be 1, but is ${t.shape.length} instead`);if(s==="channelsFirst"&&(e=Qe(e,[0,2,1])),r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");let o=Hc(e,t,a,r==="same"?"same":"valid","NWC",i);return n!=null&&(o=Ma(o,n)),o})}function P8(e,t,n,a=[1,1],r="valid",s,i,o=null){return V(()=>{if(s==null&&(s=Ca()),Ft(s),e.rank!==3&&e.rank!==4)throw new U(`conv2dWithBiasActivation expects input to be of rank 3 or 4, but received ${e.rank}.`);if(t.rank!==3&&t.rank!==4)throw new U(`conv2dWithBiasActivation expects kernel to be of rank 3 or 4, but received ${e.rank}.`);let u=Ky(e,s);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv1dWithBias is not implemented yet.");return u=Xr.conv2d({x:u,filter:t,strides:a,pad:r==="same"?"same":"valid",dilations:i,dataFormat:"NHWC",bias:n,activation:o}),s==="channelsFirst"&&(u=Qe(u,[0,3,1,2])),u})}function Gre(e,t,n,a=[1,1,1],r="valid",s,i){return V(()=>{if(s==null&&(s=Ca()),Ft(s),e.rank!==4&&e.rank!==5)throw new U(`conv3dWithBias expects input to be of rank 4 or 5, but received ${e.rank}.`);if(t.rank!==4&&t.rank!==5)throw new U(`conv3dWithBias expects kernel to be of rank 4 or 5, but received ${e.rank}.`);let o=z8(e,s);if(r==="causal")throw new Oe("The support for CAUSAL padding mode in conv3dWithBias is not implemented yet.");return o=L1(o,t,a,r==="same"?"same":"valid","NDHWC",i),n!=null&&(o=Ma(o,n)),s==="channelsFirst"&&(o=Qe(o,[0,4,1,2,3])),o})}var Zy=class extends Xe{constructor(e,t){super(t);if(this.bias=null,this.DEFAULT_KERNEL_INITIALIZER="glorotNormal",this.DEFAULT_BIAS_INITIALIZER="zeros",Zy.verifyArgs(t),this.rank=e,Yt(this.rank,"rank"),this.rank!==1&&this.rank!==2&&this.rank!==3)throw new Oe(`Convolution layer for rank other than 1, 2, or 3 (${this.rank}) is not implemented yet.`);if(this.kernelSize=ru(t.kernelSize,e,"kernelSize"),this.strides=ru(t.strides==null?1:t.strides,e,"strides"),this.padding=t.padding==null?"valid":t.padding,ua(this.padding),this.dataFormat=t.dataFormat==null?"channelsLast":t.dataFormat,Ft(this.dataFormat),this.activation=rs(t.activation),this.useBias=t.useBias==null?!0:t.useBias,this.biasInitializer=gt(t.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.biasConstraint=Ut(t.biasConstraint),this.biasRegularizer=xt(t.biasRegularizer),this.activityRegularizer=xt(t.activityRegularizer),this.dilationRate=ru(t.dilationRate==null?1:t.dilationRate,e,"dilationRate"),this.rank===1&&Array.isArray(this.dilationRate)&&this.dilationRate.length!==1)throw new U(`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 U(`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 U(`dilationRate must be a number or array of three numbers for 3D convolution, but received ${JSON.stringify(this.dilationRate)}`)}}static verifyArgs(e){if(Ja("kernelSize"in e,"required key 'kernelSize' not in config"),typeof e.kernelSize!="number"&&!oy(e.kernelSize,"number",1,3))throw new U(`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:as(this.activation),useBias:this.useBias,biasInitializer:Tt(this.biasInitializer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),biasConstraint:jt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}},Qd=class extends Zy{constructor(e,t){super(e,t);this.kernel=null,Qd.verifyArgs(t),this.filters=t.filters,Yt(this.filters,"filters"),this.kernelInitializer=gt(t.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.kernelConstraint=Ut(t.kernelConstraint),this.kernelRegularizer=xt(t.kernelRegularizer)}build(e){e=st(e);let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[t]}`);let n=e[t],a=this.kernelSize.concat([n,this.filters]);this.kernel=this.addWeight("kernel",a,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[{ndim:this.rank+2,axes:{[t]:n}}],this.built=!0}call(e,t){return V(()=>{e=Pe(e);let n,a=this.bias==null?null:this.bias.read(),r=T4(this.activation.getClassName());if(r!=null&&this.rank===2)n=P8(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate,r);else{if(this.rank===1)n=Hre(e,this.kernel.read(),a,this.strides[0],this.padding,this.dataFormat,this.dilationRate[0]);else if(this.rank===2)n=P8(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else if(this.rank===3)n=Gre(e,this.kernel.read(),a,this.strides,this.padding,this.dataFormat,this.dilationRate);else throw new Oe("convolutions greater than 3D are not implemented yet.");this.activation!=null&&(n=this.activation.apply(n))}return n})}computeOutputShape(e){e=st(e);let t=[],n=this.dataFormat==="channelsLast"?e.slice(1,e.length-1):e.slice(2);for(let r=0;r<n.length;++r){let s=Da(n[r],this.kernelSize[r],this.padding,this.strides[r],typeof this.dilationRate=="number"?this.dilationRate:this.dilationRate[r]);t.push(s)}let a=[e[0]];return this.dataFormat==="channelsLast"?(a=a.concat(t),a.push(this.filters)):(a.push(this.filters),a=a.concat(t)),a}getConfig(){let e={filters:this.filters,kernelInitializer:Tt(this.kernelInitializer),kernelRegularizer:dt(this.kernelRegularizer),kernelConstraint:jt(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 U(`Convolution layer expected config.filters to be a 'number' > 0 but got ${JSON.stringify(e.filters)}`)}},ep=class extends Qd{constructor(e){super(2,e);ep.verifyArgs(e)}getConfig(){let e=super.getConfig();return delete e.rank,e}static verifyArgs(e){if(typeof e.kernelSize!="number"&&!oy(e.kernelSize,"number",1,2))throw new U(`Conv2D expects config.kernelSize to be number or number[] with length 1 or 2, but received ${JSON.stringify(e.kernelSize)}.`)}};ep.className="Conv2D";re.registerClass(ep);var tp=class extends Qd{constructor(e){super(3,e);tp.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 U(`Conv3D expects config.kernelSize to be number or [number, number, number], but received ${JSON.stringify(e.kernelSize)}.`)}};tp.className="Conv3D";re.registerClass(tp);var Yy=class extends ep{constructor(e){super(e);if(this.inputSpec=[new Ot({ndim:4})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv2DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==4)throw new U("Input should have rank 4; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ot({ndim:4,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Pe(e);if(n.shape.length!==4)throw new U(`Conv2DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i;this.dataFormat==="channelsFirst"?(s=2,i=3):(s=1,i=2);let o=a[s],u=a[i],l=this.kernelSize[0],d=this.kernelSize[1],p=this.strides[0],c=this.strides[1],h=tr(o,p,l,this.padding),m=tr(u,c,d,this.padding),f=[r,h,m,this.filters];this.dataFormat!=="channelsLast"&&(n=Qe(n,[0,2,3,1]));let A=Gc(n,this.kernel.read(),f,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(A=Qe(A,[0,3,1,2])),this.bias!=null&&(A=Ma(A,this.bias.read(),this.dataFormat)),this.activation!=null&&(A=this.activation.apply(A)),A})}computeOutputShape(e){e=st(e);let t=e.slice(),n,a,r;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3):(n=3,a=1,r=2);let s=this.kernelSize[0],i=this.kernelSize[1],o=this.strides[0],u=this.strides[1];return t[n]=this.filters,t[a]=tr(t[a],o,s,this.padding),t[r]=tr(t[r],u,i,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Yy.className="Conv2DTranspose";re.registerClass(Yy);var Jy=class extends tp{constructor(e){super(e);if(this.inputSpec=[new Ot({ndim:5})],this.padding!=="same"&&this.padding!=="valid")throw new U(`Conv3DTranspose currently supports only padding modes 'same' and 'valid', but received padding mode ${this.padding}`)}build(e){if(e=st(e),e.length!==5)throw new U("Input should have rank 5; Received input shape: "+JSON.stringify(e));let t=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[t]==null)throw new U("The channel dimension of the inputs should be defined. Found `None`.");let n=e[t],a=this.kernelSize.concat([this.filters,n]);this.kernel=this.addWeight("kernel",a,"float32",this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.useBias&&(this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint)),this.inputSpec=[new Ot({ndim:5,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{let n=Pe(e);if(n.shape.length!==5)throw new U(`Conv3DTranspose.call() expects input tensor to be rank-4, but received a tensor of rank-${n.shape.length}`);let a=n.shape,r=a[0],s,i,o;this.dataFormat==="channelsFirst"?(o=2,s=3,i=4):(o=1,s=2,i=3);let u=a[o],l=a[s],d=a[i],p=this.kernelSize[0],c=this.kernelSize[1],h=this.kernelSize[2],m=this.strides[0],f=this.strides[1],A=this.strides[2],y=tr(u,m,p,this.padding),g=tr(l,f,c,this.padding),x=tr(d,A,h,this.padding),v=[r,y,g,x,this.filters];this.dataFormat!=="channelsLast"&&(n=Qe(n,[0,2,3,4,1]));let b=E3(n,this.kernel.read(),v,this.strides,this.padding);return this.dataFormat!=="channelsLast"&&(b=Qe(b,[0,4,1,2,3])),this.bias!==null&&(b=Ma(b,this.bias.read(),this.dataFormat)),this.activation!==null&&(b=this.activation.apply(b)),b})}computeOutputShape(e){e=st(e);let t=e.slice(),n,a,r,s;this.dataFormat==="channelsFirst"?(n=1,a=2,r=3,s=4):(n=4,a=1,r=2,s=3);let i=this.kernelSize[0],o=this.kernelSize[1],u=this.kernelSize[2],l=this.strides[0],d=this.strides[1],p=this.strides[2];return t[n]=this.filters,t[a]=tr(t[a],l,i,this.padding),t[r]=tr(t[r],d,o,this.padding),t[s]=tr(t[s],p,u,this.padding),t}getConfig(){let e=super.getConfig();return delete e.dilationRate,e}};Jy.className="Conv3DTranspose";re.registerClass(Jy);var L8=class extends Qd{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 U("The `filters` configuration field is required by SeparableConv, but is unspecified.");if(t.kernelInitializer!=null||t.kernelRegularizer!=null||t.kernelConstraint!=null)throw new U("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 U(`SeparableConv${this.rank}D supports only padding modes: 'same' and 'valid', but received ${JSON.stringify(t.padding)}`);this.depthMultiplier=t.depthMultiplier==null?1:t.depthMultiplier,this.depthwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_DEPTHWISE_INITIALIZER),this.depthwiseRegularizer=xt(t.depthwiseRegularizer),this.depthwiseConstraint=Ut(t.depthwiseConstraint),this.pointwiseInitializer=gt(t.depthwiseInitializer||this.DEFAULT_POINTWISE_INITIALIZER),this.pointwiseRegularizer=xt(t.pointwiseRegularizer),this.pointwiseConstraint=Ut(t.pointwiseConstraint)}build(e){if(e=st(e),e.length<this.rank+2)throw new U(`Inputs to SeparableConv${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 U(`The channel dimension of the inputs should be defined, but found ${JSON.stringify(e[t])}`);let n=e[t],a=this.kernelSize.concat([n,this.depthMultiplier]),r=[];for(let i=0;i<this.rank;++i)r.push(1);r.push(n*this.depthMultiplier,this.filters);let s=!0;this.depthwiseKernel=this.addWeight("depthwise_kernel",a,"float32",this.depthwiseInitializer,this.depthwiseRegularizer,s,this.depthwiseConstraint),this.pointwiseKernel=this.addWeight("pointwise_kernel",r,"float32",this.pointwiseInitializer,this.pointwiseRegularizer,s,this.pointwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[this.filters],"float32",this.biasInitializer,this.biasRegularizer,s,this.biasConstraint):this.bias=null,this.inputSpec=[new Ot({ndim:this.rank+2,axes:{[t]:n}})],this.built=!0}call(e,t){return V(()=>{e=Pe(e);let n;if(this.rank===1)throw new Oe("1D separable convolution is not implemented yet.");return this.rank===2&&(this.dataFormat==="channelsFirst"&&(e=Qe(e,[0,2,3,1])),n=aA(e,this.depthwiseKernel.read(),this.pointwiseKernel.read(),this.strides,this.padding,this.dilationRate,"NHWC")),this.useBias&&(n=Ma(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),this.dataFormat==="channelsFirst"&&(n=Qe(n,[0,3,1,2])),n})}getConfig(){let e=super.getConfig();return delete e.rank,delete e.kernelInitializer,delete e.kernelRegularizer,delete e.kernelConstraint,e.depthwiseInitializer=Tt(this.depthwiseInitializer),e.pointwiseInitializer=Tt(this.pointwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.pointwiseRegularizer=dt(this.pointwiseRegularizer),e.depthwiseConstraint=jt(this.depthwiseConstraint),e.pointwiseConstraint=jt(this.pointwiseConstraint),e}};L8.className="SeparableConv";var Qy=class extends L8{constructor(e){super(2,e)}};Qy.className="SeparableConv2D";re.registerClass(Qy);var N0=class extends Qd{constructor(e){super(1,e);N0.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"&&!oy(e.kernelSize,"number",1,1))throw new U(`Conv1D expects config.kernelSize to be number or number[] with length 1, but received ${JSON.stringify(e.kernelSize)}.`)}};N0.className="Conv1D";re.registerClass(N0);var eg=class extends Xe{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=Pe(e),this.dataFormat==="channelsLast"){let n=r0(e,this.cropping[0][0],e.shape[1]-this.cropping[0][0]-this.cropping[0][1],2);return r0(n,this.cropping[1][0],e.shape[2]-this.cropping[1][1]-this.cropping[1][0],3)}else{let n=r0(e,this.cropping[0][0],e.shape[2]-this.cropping[0][0]-this.cropping[0][1],3);return r0(n,this.cropping[1][0],e.shape[3]-this.cropping[1][1]-this.cropping[1][0],4)}})}getConfig(){let e={cropping:this.cropping,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};eg.className="Cropping2D";re.registerClass(eg);var tg=class extends Xe{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,Ft(this.dataFormat),this.interpolation=e.interpolation==null?"nearest":e.interpolation,oae(this.interpolation)}computeOutputShape(e){if(this.dataFormat==="channelsFirst"){let t=e[2]==null?null:this.size[0]*e[2],n=e[3]==null?null:this.size[1]*e[3];return[e[0],e[1],t,n]}else{let t=e[1]==null?null:this.size[0]*e[1],n=e[2]==null?null:this.size[1]*e[2];return[e[0],t,n,e[3]]}}call(e,t){return V(()=>{let n=Pe(e),a=n.shape;if(this.dataFormat==="channelsFirst"){n=Qe(n,[0,2,3,1]);let r=this.size[0]*a[2],s=this.size[1]*a[3],i=this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s]);return Qe(i,[0,3,1,2])}else{let r=this.size[0]*a[1],s=this.size[1]*a[2];return this.interpolation==="nearest"?n.resizeNearestNeighbor([r,s]):n.resizeBilinear([r,s])}})}getConfig(){let e={size:this.size,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};tg.className="UpSampling2D";re.registerClass(tg);function qre(e,t,n=[1,1],a="valid",r,s){return V(()=>{r==null&&(r=Ca()),Ft(r);let i=Ky(e,r);if(e.rank!==4)throw new U(`Input for depthwiseConv2d is required to be 4-D, but is instead ${e.rank}-D`);if(t.rank!==4)throw new U(`depthwiseKernel is required to be 4-D, but is instead ${t.rank}-D`);return i=Tl(i,t,n,a==="same"?"same":"valid","NHWC",s),r==="channelsFirst"&&(i=Qe(i,[0,3,1,2])),i})}var ng=class extends Zy{constructor(e){super(2,e);this.depthwiseKernel=null,this.depthMultiplier=e.depthMultiplier==null?1:e.depthMultiplier,this.depthwiseInitializer=gt(e.depthwiseInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.depthwiseConstraint=Ut(e.depthwiseConstraint),this.depthwiseRegularizer=xt(e.depthwiseRegularizer)}build(e){if(e=st(e),e.length<4)throw new U(`Inputs to DepthwiseConv2D should have rank 4. Received input shape: ${JSON.stringify(e)}.`);let t=this.dataFormat==="channelsFirst"?1:3;if(e[t]==null||e[t]<0)throw new U(`The channel dimension of the inputs to DepthwiseConv2D should be defined, but is not (${e[t]}).`);let n=e[t],a=[this.kernelSize[0],this.kernelSize[1],n,this.depthMultiplier];this.depthwiseKernel=this.addWeight("depthwise_kernel",a,null,this.depthwiseInitializer,this.depthwiseRegularizer,!0,this.depthwiseConstraint),this.useBias?this.bias=this.addWeight("bias",[n*this.depthMultiplier],null,this.biasInitializer,this.biasRegularizer,!0,this.biasConstraint):this.bias=null,this.built=!0}call(e,t){return V(()=>{e=Pe(e);let n=qre(e,this.depthwiseKernel.read(),this.strides,this.padding,this.dataFormat,null);return this.useBias&&(n=Ma(n,this.bias.read(),this.dataFormat)),this.activation!=null&&(n=this.activation.apply(n)),n})}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[1]*this.depthMultiplier:e[3]*this.depthMultiplier,r=Da(t,this.kernelSize[0],this.padding,this.strides[0]),s=Da(n,this.kernelSize[1],this.padding,this.strides[1]);return this.dataFormat==="channelsFirst"?[e[0],a,r,s]:[e[0],r,s,a]}getConfig(){let e=super.getConfig();return e.depthMultiplier=this.depthMultiplier,e.depthwiseInitializer=Tt(this.depthwiseInitializer),e.depthwiseRegularizer=dt(this.depthwiseRegularizer),e.depthwiseConstraint=jt(this.depthwiseRegularizer),e}};ng.className="DepthwiseConv2D";re.registerClass(ng);function W8(e,t,n,a){if(Array.isArray(e)){if(t!=null||n!=null)throw new U("When inputs is an array, neither initialState or constants should be provided");a!=null&&(n=e.slice(e.length-a,e.length),e=e.slice(0,e.length-a)),e.length>1&&(t=e.slice(1,e.length)),e=e[0]}function r(s){return s==null||Array.isArray(s)?s:[s]}return t=r(t),n=r(n),{inputs:e,initialState:t,constants:n}}function B8(e,t,n,a=!1,r,s,i=!1,o=!1){return V(()=>{let u=t.shape.length;if(u<3)throw new U(`Input should be at least 3D, but is ${u}D.`);let l=[1,0].concat(Ra(2,u));if(t=Qe(t,l),s!=null)throw new Oe("The rnn() functoin of the deeplearn.js backend does not support constants yet.");i&&console.warn("Backend rnn(): the unroll = true option is not applicable to the imperative deeplearn.js backend."),r!=null&&(r=r.asType("bool").asType("float32"),r.rank===u-1&&(r=fn(r,-1)),r=Qe(r,l)),a&&(t=Un(t,0),r!=null&&(r=Un(r,0)));let d=[],p,c=n,h=t.shape[0],m=ya(t),f;r!=null&&(f=ya(r));for(let y=0;y<h;++y){let g=m[y],x=V(()=>e(g,c));if(r==null)p=x[0],c=x[1];else{let v=V(()=>{let b=f[y],w=jn(b).sub(b),N=x[0].mul(b).add(c[0].mul(w)),C=c.map((E,z)=>x[1][z].mul(b).add(E.mul(w)));return{output:N,newStates:C}});p=v.output,c=v.newStates}o&&d.push(p)}let A;return o&&(A=mn(d,1)),[p,A,c]})}var nr=class extends Xe{constructor(e){super(e);let t;if(e.cell==null)throw new U("cell property is missing for the constructor of RNN.");if(Array.isArray(e.cell)?t=new E0({cells:e.cell}):t=e.cell,t.stateSize==null)throw new U("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 Ot({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 Ra(0,e).map(t=>null)}else return this.states_}setStates(e){this.states_=e}computeOutputShape(e){Iy(e)&&(e=e[0]),e=e;let t=this.cell.stateSize;Array.isArray(t)||(t=[t]);let n=t[0],a;if(this.returnSequences?a=[e[0],e[1],n]:a=[e[0],n],this.returnState){let r=[];for(let s of t)r.push([e[0],s]);return[a].concat(r)}else return a}computeMask(e,t){return V(()=>{Array.isArray(t)&&(t=t[0]);let n=this.returnSequences?t:null;if(this.returnState){let a=this.states.map(r=>null);return[n].concat(a)}else return n})}get states(){if(this.states_==null){let e=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1,t=[];for(let n=0;n<e;++n)t.push(null);return t}else return this.states_}set states(e){this.states_=e}build(e){let t=null;if(this.numConstants!=null)throw new Oe("Constants support is not implemented in RNN yet.");Iy(e)&&(e=e[0]),e=e;let n=this.stateful?e[0]:null,a=e.slice(2);this.inputSpec[0]=new Ot({shape:[n,null,...a]});let r=[e[0]].concat(e.slice(2));if(t!=null)throw new Oe("Constants support is not implemented in RNN yet.");this.cell.build(r);let s;if(Array.isArray(this.cell.stateSize)?s=this.cell.stateSize:s=[this.cell.stateSize],this.stateSpec!=null){if(!k.arraysEqual(this.stateSpec.map(i=>i.shape[i.shape.length-1]),s))throw new U(`An initialState was passed that is not compatible with cell.stateSize. Received stateSpec=${this.stateSpec}; However cell.stateSize is ${this.cell.stateSize}`)}else this.stateSpec=s.map(i=>new Ot({shape:[null,i]}));this.stateful&&this.resetStates()}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new br("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape[0];if(n==null)throw new U("If an RNN is stateful, it needs to know its batch size. Specify the batch size of your input tensors: \n- If using a Sequential model, specify the batch size by passing a `batchInputShape` option to your first layer.\n- If using the functional API, specify the batch size by passing a `batchShape` option to your Input layer.");if(this.states_==null)Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>$t([n,a])):this.states_=[$t([n,this.cell.stateSize])];else if(e==null)Ie(this.states_),this.keptStates!=null&&(Ie(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(a=>$t([n,a])):this.states_[0]=$t([n,this.cell.stateSize]);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`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()):Ie(this.states_);for(let a=0;a<this.states_.length;++a){let r=e[a],s=Array.isArray(this.cell.stateSize)?this.cell.stateSize[a]:this.cell.stateSize,i=[n,s];if(!k.arraysEqual(r.shape,i))throw new U(`State ${a} is incompatible with layer ${this.name}: expected shape=${i}, received shape=${r.shape}`);this.states_[a]=r}}this.states_=this.states_.map(a=>Xt(a.clone()))})}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=W8(e,n,a,this.numConstants);e=r.inputs,n=r.initialState,a=r.constants;let s=[],i=[];if(n!=null){t.initialState=n,s=s.concat(n),this.stateSpec=[];for(let o of n)this.stateSpec.push(new Ot({shape:o.shape}));i=i.concat(this.stateSpec)}if(a!=null&&(t.constants=a,s=s.concat(a),this.numConstants=a.length),s[0]instanceof Fa){let o=[e].concat(s),u=this.inputSpec.concat(i),l=this.inputSpec;this.inputSpec=u;let d=super.apply(o,t);return this.inputSpec=l,d}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;e=Pe(e),r==null&&(this.stateful?r=this.states_:r=this.getInitialState(e));let s=Array.isArray(this.cell.stateSize)?this.cell.stateSize.length:1;if(r.length!==s)throw new U(`RNN Layer has ${s} state(s) but was passed ${r.length} initial state(s).`);this.unroll&&console.warn("Ignoring unroll = true for RNN layer, due to imperative backend.");let i={training:a},o=B8((c,h)=>{let m=this.cell.call([c].concat(h),i);return[m[0],m.slice(1)]},e,r,this.goBackwards,n,null,this.unroll,this.returnSequences),u=o[0],l=o[1],d=o[2];this.stateful&&this.resetStates(d,a);let p=this.returnSequences?l:u;return this.returnState?[p].concat(d):p})}getInitialState(e){return V(()=>{let t=$t(e.shape);return t=Se(t,[1,2]),t=Hd(t),Array.isArray(this.cell.stateSize)?this.cell.stateSize.map(n=>n>1?my(t,[1,n]):t):this.cell.stateSize>1?[my(t,[1,this.cell.stateSize])]:[t]})}get trainableWeights(){return this.trainable?this.cell.trainableWeights:[]}get nonTrainableWeights(){return this.trainable?this.cell.nonTrainableWeights:this.cell.weights}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.cell!=null&&this.cell.setFastWeightInitDuringBuild(e)}getConfig(){let e=super.getConfig(),t={returnSequences:this.returnSequences,returnState:this.returnState,goBackwards:this.goBackwards,stateful:this.stateful,unroll:this.unroll};this.numConstants!=null&&(t.numConstants=this.numConstants);let n=this.cell.getConfig();return this.getClassName()===nr.className&&(t.cell={className:this.cell.getClassName(),config:n}),Object.assign({},n,e,t)}static fromConfig(e,t,n={}){let a=t.cell,r=$a(a,n);return new e(Object.assign(t,{cell:r}))}};nr.className="RNN";re.registerClass(nr);var np=class extends Xe{},T0=class extends np{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,Yt(this.units,"units"),this.activation=rs(e.activation==null?this.DEFAULT_ACTIVATION:e.activation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Ut(e.kernelConstraint),this.recurrentConstraint=Ut(e.recurrentConstraint),this.biasConstraint=Ut(e.biasConstraint),this.dropout=Ql([1,ts([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ql([1,ts([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(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 U(`SimpleRNNCell expects 2 input Tensors, got ${e.length}.`);let n=e[1];e=e[0];let a=t.training==null?!1:t.training;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ss({ones:()=>jn(e),rate:this.dropout,training:a})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ss({ones:()=>jn(n),rate:this.recurrentDropout,training:a}));let r,s=this.dropoutMask,i=this.recurrentDropoutMask;s!=null?r=Qa(B(e,s),this.kernel.read()):r=Qa(e,this.kernel.read()),this.bias!=null&&(r=Ma(r,this.bias.read())),i!=null&&(n=B(n,i));let o=ie(r,Qa(n,this.recurrentKernel.read()));return this.activation!=null&&(o=this.activation.apply(o)),[o,o]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:as(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:jt(this.kernelConstraint),recurrentConstraint:jt(this.recurrentConstraint),biasConstraint:jt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout};return Object.assign({},e,t)}};T0.className="SimpleRNNCell";re.registerClass(T0);var ag=class extends nr{constructor(e){e.cell=new T0(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return new e(t)}};ag.className="SimpleRNN";re.registerClass(ag);var C0=class extends np{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 U("GRUCell does not support reset_after parameter set to true.");this.units=e.units,Yt(this.units,"units"),this.activation=rs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=rs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Ut(e.kernelConstraint),this.recurrentConstraint=Ut(e.recurrentConstraint),this.biasConstraint=Ut(e.biasConstraint),this.dropout=Ql([1,ts([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ql([1,ts([0,e.recurrentDropout==null?0:e.recurrentDropout])]),this.implementation=e.implementation,this.stateSize=this.units,this.dropoutMask=null,this.recurrentDropoutMask=null}build(e){e=st(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 U(`GRUCell expects 2 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training==null?!1:t.training,a=e[1];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ss({ones:()=>jn(e),rate:this.dropout,training:n,count:3})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ss({ones:()=>jn(a),rate:this.recurrentDropout,training:n,count:3}));let r=this.dropoutMask,s=this.recurrentDropoutMask,i,o,u;0<this.dropout&&this.dropout<1&&(e=B(e,r[0]));let l=Qa(e,this.kernel.read());this.useBias&&(l=Ma(l,this.bias.read())),0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,s[0]));let d=this.recurrentKernel.read(),[p,c]=Kt(d,[2*this.units,this.units],d.rank-1),h=Qa(a,p),[m,f,A]=Kt(l,3,l.rank-1),[y,g]=Kt(h,2,h.rank-1);i=this.recurrentActivation.apply(ie(m,y)),o=this.recurrentActivation.apply(ie(f,g));let x=Qa(B(o,a),c);u=this.activation.apply(ie(A,x));let v=ie(B(i,a),B(ie(1,St(i)),u));return[v,v]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:as(this.activation),recurrentActivation:as(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:jt(this.kernelConstraint),recurrentConstraint:jt(this.recurrentConstraint),biasConstraint:jt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation,resetAfter:!1};return Object.assign({},e,t)}};C0.className="GRUCell";re.registerClass(C0);var rg=class extends nr{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 C0(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};rg.className="GRU";re.registerClass(rg);var ap=class extends np{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,Yt(this.units,"units"),this.activation=rs(e.activation===void 0?this.DEFAULT_ACTIVATION:e.activation),this.recurrentActivation=rs(e.recurrentActivation===void 0?this.DEFAULT_RECURRENT_ACTIVATION:e.recurrentActivation),this.useBias=e.useBias==null?!0:e.useBias,this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.recurrentInitializer=gt(e.recurrentInitializer||this.DEFAULT_RECURRENT_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.unitForgetBias=e.unitForgetBias,this.kernelRegularizer=xt(e.kernelRegularizer),this.recurrentRegularizer=xt(e.recurrentRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.kernelConstraint=Ut(e.kernelConstraint),this.recurrentConstraint=Ut(e.recurrentConstraint),this.biasConstraint=Ut(e.biasConstraint),this.dropout=Ql([1,ts([0,e.dropout==null?0:e.dropout])]),this.recurrentDropout=Ql([1,ts([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=st(e);let n=e[e.length-1];this.kernel=this.addWeight("kernel",[n,this.units*4],null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint),this.recurrentKernel=this.addWeight("recurrent_kernel",[this.units,this.units*4],null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint);let a;if(this.useBias){if(this.unitForgetBias){let r=this.biasInitializer,s=this.units;a=new(t=class extends xa{apply(i,o){let u=r.apply([s]),l=new i0().apply([s]),d=r.apply([s*2]);return O4(O4(u,l),d)}},t.className="CustomInit",t)}else a=this.biasInitializer;this.bias=this.addWeight("bias",[this.units*4],null,a,this.biasRegularizer,!0,this.biasConstraint)}else this.bias=null;this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training;if(e=e,e.length!==3)throw new U(`LSTMCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let a=e[1],r=e[2];e=e[0],0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ss({ones:()=>jn(e),rate:this.dropout,training:n,count:4})),0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ss({ones:()=>jn(a),rate:this.recurrentDropout,training:n,count:4}));let s=this.dropoutMask,i=this.recurrentDropoutMask,o,u,l,d;0<this.dropout&&this.dropout<1&&(e=B(e,s[0]));let p=Qa(e,this.kernel.read());0<this.recurrentDropout&&this.recurrentDropout<1&&(a=B(a,i[0])),p=ie(p,Qa(a,this.recurrentKernel.read())),this.useBias&&(p=Ma(p,this.bias.read()));let[c,h,m,f]=Kt(p,4,p.rank-1);o=this.recurrentActivation.apply(c),u=this.recurrentActivation.apply(h),l=ie(B(u,r),B(o,this.activation.apply(m))),d=this.recurrentActivation.apply(f);let A=B(d,this.activation.apply(l));return[A,A,l]})}getConfig(){let e=super.getConfig(),t={units:this.units,activation:as(this.activation),recurrentActivation:as(this.recurrentActivation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),recurrentInitializer:Tt(this.recurrentInitializer),biasInitializer:Tt(this.biasInitializer),unitForgetBias:this.unitForgetBias,kernelRegularizer:dt(this.kernelRegularizer),recurrentRegularizer:dt(this.recurrentRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:jt(this.kernelConstraint),recurrentConstraint:jt(this.recurrentConstraint),biasConstraint:jt(this.biasConstraint),dropout:this.dropout,recurrentDropout:this.recurrentDropout,implementation:this.implementation};return Object.assign({},e,t)}};ap.className="LSTMCell";re.registerClass(ap);var sg=class extends nr{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 ap(e),super(e)}call(e,t){return V(()=>{this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null);let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}static fromConfig(e,t){return t.implmentation===0&&(t.implementation=1),new e(t)}};sg.className="LSTM";re.registerClass(sg);var E0=class extends np{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 n=e.slice(1),a=[];for(let i of this.cells.slice().reverse())Array.isArray(i.stateSize)?a.push(n.splice(0,i.stateSize.length)):a.push(n.splice(0,1));a.reverse();let r=[],s;for(let i=0;i<this.cells.length;++i){let o=this.cells[i];n=a[i],i===0?s=[e[0]].concat(n):s=[s[0]].concat(n),s=o.call(s,t),r.push(s.slice(1))}n=[];for(let i of r.slice().reverse())n.push(...i);return[s[0]].concat(n)})}build(e){Iy(e)&&(e=e[0]),e=e;let t;this.cells.forEach((n,a)=>{Ui(`RNNCell_${a}`,()=>{n.build(e),Array.isArray(n.stateSize)?t=n.stateSize[0]:t=n.stateSize,e=[e[0],t]})}),this.built=!0}getConfig(){let e=super.getConfig(),t=a=>({className:a.getClassName(),config:a.getConfig()}),n={cells:this.cells.map(t)};return Object.assign({},e,n)}static fromConfig(e,t,n={}){let a=[];for(let r of t.cells)a.push($a(r,n));return new e({cells:a})}get trainableWeights(){if(!this.trainable)return[];let e=[];for(let t of this.cells)e.push(...t.trainableWeights);return e}get nonTrainableWeights(){let e=[];for(let t of this.cells)e.push(...t.nonTrainableWeights);if(!this.trainable){let t=[];for(let n of this.cells)t.push(...n.trainableWeights);return t.concat(e)}return e}getWeights(){let e=[];for(let t of this.cells)e.push(...t.weights);return Sy(e)}setWeights(e){let t=[];for(let n of this.cells){let a=n.weights.length,r=e.splice(a);for(let s=0;s<n.weights.length;++s)t.push([n.weights[s],r[s]])}Ny(t)}};E0.className="StackedRNNCells";re.registerClass(E0);function ss(e){let{ones:t,rate:n,training:a=!1,count:r=1}=e,s=()=>P4(t(),n),i=()=>qd(s,t,a);return!r||r<=1?Xt(i().clone()):Array(r).fill(void 0).map(i).map(o=>Xt(o.clone()))}var Xre=function(e,t){var n={};for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&t.indexOf(a)<0&&(n[a]=e[a]);if(e!=null&&typeof Object.getOwnPropertySymbols=="function")for(var r=0,a=Object.getOwnPropertySymbols(e);r<a.length;r++)t.indexOf(a[r])<0&&Object.prototype.propertyIsEnumerable.call(e,a[r])&&(n[a[r]]=e[a[r]]);return n},V8=class extends nr{constructor(e){if(e.unroll)throw new Oe("Unrolling is not possible with convolutional RNNs.");if(Array.isArray(e.cell))throw new Oe("It is not possible at the moment to stack convolutional cells.");super(e);this.inputSpec=[new Ot({ndim:5})]}call(e,t){return V(()=>{if(this.cell.dropoutMask!=null&&(Ie(this.cell.dropoutMask),this.cell.dropoutMask=null),this.cell.recurrentDropoutMask!=null&&(Ie(this.cell.recurrentDropoutMask),this.cell.recurrentDropoutMask=null),t&&t.constants)throw new U("ConvRNN2D cell does not support constants");let n=t==null?null:t.mask,a=t==null?null:t.training,r=t==null?null:t.initialState;return super.call(e,{mask:n,training:a,initialState:r})})}computeOutputShape(e){let t=this.computeSingleOutputShape(e);return this.returnSequences||(t=[t[0],...t.slice(2)]),this.returnState&&(t=[t,...Array(2).fill([e[0],...t.slice(-3)])]),t}getInitialState(e){return V(()=>{let{stateSize:t}=this.cell,n=e.shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)],s=$t(r);return Array.isArray(t)?Array(t.length).fill(s):[s]})}resetStates(e,t=!1){V(()=>{if(!this.stateful)throw new br("Cannot call resetStates() on an RNN Layer that is not stateful.");let n=this.inputSpec[0].shape,a=this.computeSingleOutputShape(n),r=[a[0],...a.slice(2)];if(n[0]==null)throw new U("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(()=>$t(r)):this.states_=[$t(r)];else if(e==null)Ie(this.states_),this.keptStates!=null&&(Ie(this.keptStates),this.keptStates=[]),Array.isArray(this.cell.stateSize)?this.states_=this.cell.stateSize.map(()=>$t(r)):this.states_[0]=$t(r);else{if(Array.isArray(e)||(e=[e]),e.length!==this.states_.length)throw new U(`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()):Ie(this.states_);for(let s=0;s<this.states_.length;++s){let i=e[s],o=r;if(!k.arraysEqual(i.shape,o))throw new U(`State ${s} is incompatible with layer ${this.name}: expected shape=${o}, received shape=${i.shape}`);this.states_[s]=i}}this.states_=this.states_.map(s=>Xt(s.clone()))})}computeSingleOutputShape(e){let{dataFormat:t,filters:n,kernelSize:a,padding:r,strides:s,dilationRate:i}=this.cell,o=t==="channelsFirst",u=e[o?3:2],l=e[o?4:3],d=Da(u,a[0],r,s[0],i[0]),p=Da(l,a[1],r,s[1],i[1]);return[...e.slice(0,2),...o?[n,d,p]:[d,p,n]]}};V8.className="ConvRNN2D";var R0=class extends ap{constructor(e){let{filters:t,kernelSize:n,strides:a,padding:r,dataFormat:s,dilationRate:i}=e;super(Object.assign({},e,{units:t}));this.filters=t,Yt(this.filters,"filters"),this.kernelSize=ru(n,2,"kernelSize"),this.kernelSize.forEach(o=>Yt(o,"kernelSize")),this.strides=ru(a||1,2,"strides"),this.strides.forEach(o=>Yt(o,"strides")),this.padding=r||"valid",ua(this.padding),this.dataFormat=s||"channelsLast",Ft(this.dataFormat),this.dilationRate=ru(i||1,2,"dilationRate"),this.dilationRate.forEach(o=>Yt(o,"dilationRate"))}build(e){var t;e=st(e);let n=this.dataFormat==="channelsFirst"?1:e.length-1;if(e[n]==null)throw new U(`The channel dimension of the input should be defined. Found ${e[n]}`);let a=e[n],r=4,s=this.kernelSize.concat([a,this.filters*r]);this.kernel=this.addWeight("kernel",s,null,this.kernelInitializer,this.kernelRegularizer,!0,this.kernelConstraint);let i=this.kernelSize.concat([this.filters,this.filters*r]);if(this.recurrentKernel=this.addWeight("recurrent_kernel",i,null,this.recurrentInitializer,this.recurrentRegularizer,!0,this.recurrentConstraint),this.useBias){let o;if(this.unitForgetBias){let u=this.biasInitializer,l=this.filters;o=new(t=class extends xa{apply(d,p){let c=u.apply([l]),h=Vn([l]),m=u.apply([l*2]);return fy([c,h,m])}},t.className="CustomInit",t)}else o=this.biasInitializer;this.bias=this.addWeight("bias",[this.filters*r],null,o,this.biasRegularizer,!0,this.biasConstraint)}this.built=!0}call(e,t){return V(()=>{if(e.length!==3)throw new U(`ConvLSTM2DCell expects 3 input Tensors (inputs, h, c), got ${e.length}.`);let n=t.training||!1,a=e[0],r=e[1],s=e[2],i=4;0<this.dropout&&this.dropout<1&&this.dropoutMask==null&&(this.dropoutMask=ss({ones:()=>jn(a),rate:this.dropout,training:n,count:i}));let o=this.dropoutMask,u=(K,ne,Q)=>!ne||!ne[Q]?K:B(ne[Q],K),l=u(a,o,0),d=u(a,o,1),p=u(a,o,2),c=u(a,o,3);0<this.recurrentDropout&&this.recurrentDropout<1&&this.recurrentDropoutMask==null&&(this.recurrentDropoutMask=ss({ones:()=>jn(r),rate:this.recurrentDropout,training:n,count:i}));let h=this.recurrentDropoutMask,m=u(r,h,0),f=u(r,h,1),A=u(r,h,2),y=u(r,h,3),g=3,[x,v,b,w]=Kt(this.kernel.read(),i,g),[N,C,E,z]=this.useBias?Kt(this.bias.read(),i):[null,null,null,null];l=this.inputConv(l,x,N,this.padding),d=this.inputConv(d,v,C,this.padding),p=this.inputConv(p,b,E,this.padding),c=this.inputConv(c,w,z,this.padding);let[$,S,O,_]=Kt(this.recurrentKernel.read(),i,g);m=this.recurrentConv(m,$),f=this.recurrentConv(f,S),A=this.recurrentConv(A,O),y=this.recurrentConv(y,_);let W=this.recurrentActivation.apply(ie(l,m)),G=this.recurrentActivation.apply(ie(d,f)),H=ie(B(G,s),B(W,this.activation.apply(ie(p,A)))),J=B(this.recurrentActivation.apply(ie(c,y)),this.activation.apply(H));return[J,J,H]})}getConfig(){let e=super.getConfig(),{units:t}=e,n=Xre(e,["units"]),a={filters:this.filters,kernelSize:this.kernelSize,padding:this.padding,dataFormat:this.dataFormat,dilationRate:this.dilationRate,strides:this.strides};return Object.assign({},n,a)}inputConv(e,t,n,a){let r=fr(e,t,this.strides,a||"valid",this.dataFormat==="channelsFirst"?"NCHW":"NHWC",this.dilationRate);return n?Ma(r,n,this.dataFormat):r}recurrentConv(e,t){return fr(e,t,1,"same",this.dataFormat==="channelsFirst"?"NCHW":"NHWC")}};R0.className="ConvLSTM2DCell";re.registerClass(R0);var ig=class extends V8{constructor(e){let t=new R0(e);super(Object.assign({},e,{cell:t}))}static fromConfig(e,t){return new e(t)}};ig.className="ConvLSTM2D";re.registerClass(ig);var M0=class extends Xe{constructor(e){super(e);this.rate=Math.max(Math.min(e.rate,1),0),this.noiseShape=e.noiseShape,this.seed=e.seed,this.supportsMasking=!0}getNoiseShape(e){if(this.noiseShape==null)return this.noiseShape;let t=e.shape,n=[];for(let a=0;a<this.noiseShape.length;++a)n.push(this.noiseShape[a]==null?t[a]:this.noiseShape[a]);return n}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(0<this.rate&&this.rate<1){let a=t.training==null?!1:t.training,r=this.getNoiseShape(n);return qd(()=>P4(n,this.rate,r,this.seed),()=>n,a)}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()}};M0.className="Dropout";re.registerClass(M0);var og=class extends M0{constructor(e){super(e);this.inputSpec=[{ndim:3}]}getNoiseShape(e){let t=e.shape;return[t[0],1,t[2]]}};og.className="SpatialDropout1D";re.registerClass(og);var lg=class extends Xe{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,Yt(this.units,"units"),this.activation=rs(e.activation),e.useBias!=null&&(this.useBias=e.useBias),this.kernelInitializer=gt(e.kernelInitializer||this.DEFAULT_KERNEL_INITIALIZER),this.biasInitializer=gt(e.biasInitializer||this.DEFAULT_BIAS_INITIALIZER),this.kernelConstraint=Ut(e.kernelConstraint),this.biasConstraint=Ut(e.biasConstraint),this.kernelRegularizer=xt(e.kernelRegularizer),this.biasRegularizer=xt(e.biasRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.supportsMasking=!0,this.inputSpec=[{minNDim:2}]}build(e){e=st(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=st(e);let t=e.slice();return t[t.length-1]=this.units,t}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=T4(this.activation.getClassName()),r;return a!=null?r=Qa(n,this.kernel.read(),a,this.bias?this.bias.read():null):(r=Qa(n,this.kernel.read()),this.bias!=null&&(r=Ma(r,this.bias.read())),this.activation!=null&&(r=this.activation.apply(r))),r})}getConfig(){let e={units:this.units,activation:as(this.activation),useBias:this.useBias,kernelInitializer:Tt(this.kernelInitializer),biasInitializer:Tt(this.biasInitializer),kernelRegularizer:dt(this.kernelRegularizer),biasRegularizer:dt(this.biasRegularizer),activityRegularizer:dt(this.activityRegularizer),kernelConstraint:jt(this.kernelConstraint),biasConstraint:jt(this.biasConstraint)},t=super.getConfig();return Object.assign(e,t),e}};lg.className="Dense";re.registerClass(lg);var ug=class extends Xe{constructor(e){e=e||{},super(e),this.inputSpec=[{minNDim:3}],this.dataFormat=e.dataFormat}computeOutputShape(e){e=st(e);for(let t of e.slice(1))if(t==null)throw new U(`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],es(e,1)]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);if(this.dataFormat==="channelsFirst"&&n.rank>1){let a=[0];for(let r=2;r<n.rank;++r)a.push(r);a.push(1),n=n.transpose(a)}return hae(n)})}getConfig(){let e={};this.dataFormat!=null&&(e.dataFormat=this.dataFormat);let t=super.getConfig();return Object.assign(e,t),e}};ug.className="Flatten";re.registerClass(ug);var dg=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.activation=rs(e.activation)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);return this.activation.apply(n)})}getConfig(){let e={activation:as(this.activation)},t=super.getConfig();return Object.assign(e,t),e}};dg.className="Activation";re.registerClass(dg);var pg=class extends Xe{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=Pe(e),pae(e,this.n)))}getConfig(){let e={n:this.n},t=super.getConfig();return Object.assign(e,t),e}};pg.className="RepeatVector";re.registerClass(pg);var cg=class extends Xe{constructor(e){super(e);this.targetShape=e.targetShape;for(let t=0;t<this.targetShape.length;++t)this.isUnknown(this.targetShape[t])&&(this.targetShape[t]=null)}isUnknown(e){return e<0||e==null}fixUnknownDimension(e,t){let n="Total size of new array must be unchanged.",a=t.slice(),r=1,s=null;for(let o=0;o<a.length;++o){let u=a[o];if(this.isUnknown(u))if(s===null)s=o;else throw new U("Can only specifiy one unknown dimension.");else r*=u}let i=es(e);if(s!==null){if(r===0||i%r!=0)throw new U(n);a[s]=i/r}else if(i!==r)throw new U(n);return a}computeOutputShape(e){let t=!1;for(let n=0;n<e.length;++n)if(this.isUnknown(e[n])){t=!0;break}return t?e.slice(0,1).concat(this.targetShape):e.slice(0,1).concat(this.fixUnknownDimension(e.slice(1),this.targetShape))}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=n.shape,r=a.slice(0,1).concat(this.fixUnknownDimension(a.slice(1),this.targetShape));return n.reshape(r)})}getConfig(){let e={targetShape:this.targetShape},t=super.getConfig();return Object.assign(e,t),e}};cg.className="Reshape";re.registerClass(cg);var hg=class extends Xe{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=Ra(1,e.dims.length+1);if(!k.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 Ot({ndim:this.dims.length+1})]}computeOutputShape(e){e=st(e);let t=e.slice();return this.dims.forEach((n,a)=>{t[a+1]=e[n]}),t}call(e,t){return Qe(Pe(e),this.dimsIncludingBatch)}getConfig(){let e={dims:this.dims},t=super.getConfig();return Object.assign(e,t),e}};hg.className="Permute";re.registerClass(hg);var fg=class extends Xe{constructor(e){super(e==null?{}:e);this.supportsMasking=!0,e!=null?this.maskValue=e.maskValue==null?0:e.maskValue:this.maskValue=0}computeOutputShape(e){return e}getConfig(){let e=super.getConfig(),t={maskValue:this.maskValue};return Object.assign(t,e),t}computeMask(e,t){let n=Pe(e),a=-1;return sd(Ri(n,this.maskValue),a)}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e),a=-1,r=!0,s=sd(Ri(n,this.maskValue),a,r);return n.mul(s.asType(n.dtype))})}};fg.className="Masking";re.registerClass(fg);var mg=class extends Xe{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(mt(e.inputLength))}this.inputDim=e.inputDim,Yt(this.inputDim,"inputDim"),this.outputDim=e.outputDim,Yt(this.outputDim,"outputDim"),this.embeddingsInitializer=gt(e.embeddingsInitializer||this.DEFAULT_EMBEDDINGS_INITIALIZER),this.embeddingsRegularizer=xt(e.embeddingsRegularizer),this.activityRegularizer=xt(e.activityRegularizer),this.embeddingsConstraint=Ut(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=Pe(e),Ri(e,Ge(e))):null)}computeOutputShape(e){if(e=st(e),this.inputLength==null)return[...e,this.outputDim];let t=mt(this.inputLength);if(t.length!==e.length-1)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);{let n=0;for(let a=0;a<t.length;++a){let r=t[a],s=e[a+1];if(r!=null&&s!=null&&r!==s)throw new U(`"inputLength" is ${this.inputLength}, but received input shape has shape ${e}`);r==null&&(t[n]=s),n++}}return[e[0],...t,this.outputDim]}call(e,t){return V(()=>{this.invokeCallHook(e,t);let n=Pe(e);return n.dtype!=="int32"&&(n=Ud(n,"int32")),z4(this.embeddings.read(),n.as1D()).reshape(st(this.computeOutputShape(n.shape)))})}getConfig(){let e={inputDim:this.inputDim,outputDim:this.outputDim,embeddingsInitializer:Tt(this.embeddingsInitializer),embeddingsRegularizer:dt(this.embeddingsRegularizer),activityRegularizer:dt(this.activityRegularizer),embeddingsConstraint:jt(this.embeddingsConstraint),maskZero:this.maskZero,inputLength:this.inputLength},t=super.getConfig();return Object.assign(e,t),e}};mg.className="Embedding";re.registerClass(mg);var Ki=class extends Xe{constructor(e){super(e||{});this.supportsMasking=!0}mergeFunction(e){throw new Oe}computeElementwiseOpOutputShape(e,t){if(e==null||t==null)return null;if(e.length<t.length)return this.computeElementwiseOpOutputShape(t,e);if(t.length===0)return e;let n=e.slice(0,e.length-t.length);for(let a=0;a<t.length;++a){let r=e[e.length-t.length+a],s=t[a];if(r==null||s==null||r<0||s<0)n.push(null);else if(r===1)n.push(s);else if(s===1)n.push(r);else{if(r!==s)throw new U("Operands could not be broadcast together with shapes "+JSON.stringify(e)+" "+JSON.stringify(t));n.push(r)}}return n}build(e){if(Array.isArray(e)&&!Array.isArray(e[0])&&(e=[st(e)]),e=e,e.length<2)throw new U(`A merge layer should be called on an Array of at least 2 inputs. Got ${e.length} input(s).`);let t=[];for(let r of e)r!=null&&r[0]!==null&&t.push(r[0]);if(t=Qr(t),t.length>1)throw new U(`Can not merge tensors with different batch sizes. Got tensors with shapes: ${JSON.stringify(e)}.`);let n=e[0]==null?null:e[0].slice(1);for(let r=1;r<e.length;++r){let s=e[r]==null?null:e[r].slice(1);n=this.computeElementwiseOpOutputShape(n,s)}let a=e.map(r=>r.length);e.indexOf(null)===-1&&Qr(a).length===1?this.reshapeRequired=!1:this.reshapeRequired=!0}call(e,t){return V(()=>{if(e=e,this.reshapeRequired){let n=[],a=e.map(r=>r.rank);if(a.indexOf(null)===-1){let r=ts(a);for(let s of e){let i=s.rank;for(let o=0;o<r-i;++o)s=Hd(s,1);n.push(s)}return this.mergeFunction(n)}else{let r=!1;for(let o of e){let u=o.rank;if(u==null){let l=o.shape,d=l[0],p=l.slice(1).concat([d]),c=o.reshape([d].concat(es(l.slice(1))));c=Qe(c,[1,0]),c=c.reshape(p),n.push(c),r=!0}else if(u>1){let l=Ra(1,u).concat([0]);n.push(Qe(o,l)),r=!0}else n.push(o)}let s=this.mergeFunction(n),i=s.rank;if(r){if(i==null){let o=s.shape,u=o.length,l=o[u-1],d=[l].concat(o.slice(0,o.length-1));s=Qe(s.reshape([-1,l]),[1,0]).reshape(d)}else if(i>1){let o=[i-1].concat(Ra(0,i-1));s=Qe(s,o)}}return s}}else return this.mergeFunction(e)})}computeOutputShape(e){e=e;let t;e[0]==null?t=null:t=e[0].slice(1);for(let a=1;a<e.length;++a){let r=e[a]==null?null:e[a].slice(1);t=this.computeElementwiseOpOutputShape(t,r)}let n=[];for(let a of e)a!=null&&a[0]!==null&&n.push(a[0]);return n=Qr(n),n.length===1?t=n.concat(t):t=[null].concat(t),t}computeMask(e,t){return V(()=>{if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an Array");if(!Array.isArray(e))throw new U("`inputs` should be an Array");if(t.length!==e.length)throw new U(`The Array 'inputs' and 'mask' are expected to have the same length, but have different lengths (${e.length} vs ${t.length})`);if(t.every(a=>a==null))return null;t=t.map(a=>a==null?a:fn(a,0));let n=t[0];for(let a=1;a<t.length-1;++a)n=ma(n,t[a]);return n})}},Ag=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return t})}};Ag.className="Add";re.registerClass(Ag);var yg=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=B(t,e[n]);return t})}};yg.className="Multiply";re.registerClass(yg);var gg=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0].clone();for(let n=1;n<e.length;++n)t=ie(t,e[n]);return B(1/e.length,t)})}};gg.className="Average";re.registerClass(gg);var xg=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=qa(t,e[n]);return t})}};xg.className="Maximum";re.registerClass(xg);var bg=class extends Ki{constructor(e){super(e)}mergeFunction(e){return V(()=>{let t=e[0];for(let n=1;n<e.length;++n)t=Ml(t,e[n]);return t})}};bg.className="Minimum";re.registerClass(bg);var vg=class extends Ki{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 U("A `Concatenate` layer should be called on a list of at least 2 inputs");e=e;let t=!0;for(let a of e)if(a!=null){t=!1;break}if(t)return;let n=[];for(let a=0;a<e.length;++a){let r=e[a].slice();r.splice(this.axis,1);let s=!1;for(let i of n)if(k.arraysEqual(i,r)){s=!0;break}s||n.push(r)}if(n.length>1)throw new U("A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got input shapes: "+JSON.stringify(e))}mergeFunction(e){return V(()=>fy(e,this.axis))}computeOutputShape(e){if(!(Array.isArray(e)&&Array.isArray(e[0])))throw new U("A `Concatenate` layer should be called on a list of inputs.");let t=e,n=t[0].slice(),a=this.axis<0?n.length+this.axis:this.axis;for(let r of t.slice(1)){if(n[a]==null||r[a]==null){n[a]=null;break}n[a]+=r[a]}return n}computeMask(e,t){if(t==null)return null;if(!Array.isArray(t))throw new U("`mask` should be an array for Concatenate");if(!Array.isArray(e))throw new U("`inputs` should be an array for Concatenate");if(t.length!==e.length)throw new U(`Mismatch in the length of mask (${t.length}) and the legnth of inputs (${e.length})`);return V(()=>{let n=!0;if(t.forEach(s=>{if(s!=null){n=!1;return}}),n)return null;let a=[];for(let s=0;s<e.length;++s)t[s]==null?a.push(jn(e[s]).asType("bool")):t[s].rank<e[s].rank?a.push(fn(t[s],-1)):a.push(t[s]);let r=lt(a,this.axis);return jc(r,-1,!1)})}getConfig(){let e={axis:this.axis},t=super.getConfig();return Object.assign(e,t),e}};vg.className="Concatenate";re.registerClass(vg);function rp(e,t){for(;e<0;)e+=t;return e}function Kre(e,t,n){if(e.shape.length>3||t.shape.length>3)throw new Oe("batchDot is not implemented for tensors of 4D or higher rank yet");if(k.assert(e.shape.length>=2,()=>`batchDot requires the rank of x to be >= 2, but got ${e.shape.length}`),k.assert(e.shape.length>=2,()=>`batchDot requires the rank of y to be >= 2, but got ${t.shape.length}`),typeof n=="number"&&(n=[n,n]),e.dtype==="complex64"||t.dtype==="complex64")throw new Oe("batchDot is not implemented for complex64-type Tensors yet.");let a=e.shape.length,r=t.shape.length;n==null&&(n=[a-1,r-2]);let s=n;return V(()=>{let i;if(a>r){i=a-r;let u=[];for(let l=0;l<i;++l)u.push(1);t=t.reshape(t.shape.concat(u))}else if(r>a){i=r-a;let u=[];for(let l=0;l<i;++l)u.push(1);e=e.reshape(e.shape.concat(u))}else i=0;let o;if(e.shape.length===2&&t.shape.length===2)s[0]===s[1]?o=e.mul(t).sum(s[0]):o=e.transpose([1,0]).mul(t).sum(s[1]);else{let u=s[0]!==e.shape.length-1,l=s[1]===t.shape.length-1;o=e.matMul(t,u,l)}if(i>0){let u;a>r?u=a+r-3:u=a-1;let l=[];for(let d=u;d<u+i;++d)l.push(d);o=o.squeeze(l)}return o.shape.length===1&&(o=o.expandDims(1)),o})}var wg=class extends Ki{constructor(e){super(e);this.axes=e.axes,this.normalize=e.normalize==null?!1:e.normalize,this.supportsMasking=!0,this.reshapeRequired=!1}build(e){k.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0],n=e[1];if(t.length>3||n.length>3)throw new Oe("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);if(t[a[0]]!==n[a[1]])throw new U(`Dimension incompatibility: ${t[a[0]]} !== ${n[a[1]]}`)}mergeFunction(e){if(e.length!==2)throw new U(`A \`Dot\` layer must be called on exactly 2 inputs, but received ${e.length} input(s).`);let t=e[0],n=e[1],a;return Array.isArray(this.axes)?a=this.axes.map((r,s)=>rp(r,e[s].shape.length)):a=[rp(this.axes,t.shape.length),rp(this.axes,n.shape.length)],this.normalize&&(t=g0(t,a[0]),n=g0(n,a[1])),Kre(t,n,a)}interpretAxes(e,t){let n;return Array.isArray(this.axes)?n=this.axes:n=[rp(this.axes,e.length),rp(this.axes,t.length)],n}computeOutputShape(e){k.assert(Array.isArray(e)&&e.length===2&&Array.isArray(e[0])&&Array.isArray(e[1]),()=>"A `Dot` layer should be called on a list of exactly 2 inputs.");let t=e[0].slice(),n=e[1].slice();if(t.length>3||n.length>3)throw new Oe("Dot layer does not support tensors of 4D or higher rank yet.");let a=this.interpretAxes(t,n);t.splice(a[0],1),n.splice(a[1],1),n.splice(0,1);let r=t.concat(n);return r.length===1&&r.push(1),r}computeMask(e,t){return null}getConfig(){let e={axes:this.axes,normalize:this.normalize},t=super.getConfig();return Object.assign(e,t),e}};wg.className="Dot";re.registerClass(wg);var kg=class extends Xe{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 n=Pe(e);return qd(()=>s0(n.shape,0,this.stddev).add(n),()=>n,t.training||!1)})}};kg.className="GaussianNoise";re.registerClass(kg);var Ig=class extends Xe{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 n=Pe(e);return this.rate>0&&this.rate<1?qd(()=>{let a=Math.sqrt(this.rate/(1-this.rate));return n.mul(s0(n.shape,1,a))},()=>n,t.training||!1):n})}};Ig.className="GaussianDropout";re.registerClass(Ig);var Sg=class extends Xe{constructor(e){super(e);this.supportsMasking=!0,this.rate=e.rate,this.noiseShape=e.noiseShape}_getNoiseShape(e){return this.noiseShape||Pe(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 n=this._getNoiseShape(e);return qd(()=>{let a=Pe(e),r=1.6732632423543772,s=1.0507009873554805,i=-r*s,o=Gr(Fl(n),this.rate);o=Ud(o,"float32");let u=((1-this.rate)*(1+this.rate*i**2))**-.5,l=-u*i*this.rate;return a.mul(o).add(o.add(-1).mul(i)).mul(u).add(l)},()=>Pe(e),t.training||!1)}return e})}};Sg.className="AlphaDropout";re.registerClass(Sg);function sp(e,t,n,a,r,s=.001){let i;if(e.rank===2)i=w3(e,t,n,a,r,s);else if(e.rank===3)i=k3(e,t,n,a,r,s);else if(e.rank===4)i=I3(e,t,n,a,r,s);else throw new Oe(`batchNormalization is not implemented for array of rank ${e.rank} yet`);return i}function Zre(e,t,n,a,r=.001){return V(()=>{let s=th(e,a),i=s.mean,o=s.variance;return[sp(e,i,o,n,t,r),i,o]})}function Yre(e,t,n,a,r=.001){return V(()=>{let s=th(e,a),i=s.mean,o=s.variance,u=[];for(let h of Ra(0,e.rank))a.indexOf(h)!==-1?u.push(1):u.push(e.shape[h]);let l=i.reshape(u),d=o.reshape(u),p=t==null?null:t.reshape(u),c=n==null?null:n.reshape(u);return[sp(e,l,d,c,p,r),i,o]})}function Jre(e,t,n,a,r=.001){return k.arraysEqual(a.slice().sort(),Ra(0,e.rank-1))?Zre(e,t,n,a,r):Yre(e,t,n,a,r)}var Ng=class extends Xe{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=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.movingMeanInitializer=gt(e.movingMeanInitializer||"zeros"),this.movingVarianceInitializer=gt(e.movingVarianceInitializer||"ones"),this.betaConstraint=Ut(e.betaConstraint),this.gammaConstraint=Ut(e.gammaConstraint),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer)}build(e){e=st(e);let t=this.axis>=0?this.axis:this.axis+e.length,n=e[t];if(n==null)throw new U(`Axis ${t} of input tensor should have a defined dimension but the layer received an input with shape ${JSON.stringify(e)}.`);this.inputSpec=[new Ot({ndim:e.length,axes:{[t]:n}})];let a=[n];this.scale&&(this.gamma=this.addWeight("gamma",a,null,this.gammaInitializer,this.gammaRegularizer,!0,this.gammaConstraint)),this.center&&(this.beta=this.addWeight("beta",a,null,this.betaInitializer,this.betaRegularizer,!0,this.betaConstraint)),this.movingMean=this.addWeight("moving_mean",a,null,this.movingMeanInitializer,null,!1),this.movingVariance=this.addWeight("moving_variance",a,null,this.movingVarianceInitializer,null,!1),this.built=!0}call(e,t){return V(()=>{let n=t.training==null?!1:t.training,a=Pe(e),r=a.shape,s=r.length,i=Ra(0,s),o=this.axis>=0?this.axis:this.axis+s;i.splice(o,1);let u=Bi(1,s);u[o]=r[o];let l=i.slice();l.sort();let d=!k.arraysEqual(l,Ra(0,s).slice(0,s-1)),p=()=>{if(d){let A=this.movingMean.read().reshape(u),y=this.movingVariance.read().reshape(u),g=this.center?this.beta.read().reshape(u):null,x=this.scale?this.gamma.read().reshape(u):null;return sp(a,A,y,g,x,this.epsilon)}else return sp(a,this.movingMean.read(),this.movingVariance.read(),this.beta==null?null:this.beta.read(),this.gamma==null?null:this.gamma.read(),this.epsilon)};if(!n)return p();let[c,h,m]=Jre(a,this.gamma.read(),this.beta.read(),i,this.epsilon),f=(A,y,g)=>{V(()=>{let x=1-g,v=A.read(),b=v.sub(y).mul(x);A.write(v.sub(b))})};return(()=>{f(this.movingMean,h,this.momentum),f(this.movingVariance,m,this.momentum)})(),c})}getConfig(){let e={axis:this.axis,momentum:this.momentum,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tt(this.betaInitializer),gammaInitializer:Tt(this.gammaInitializer),movingMeanInitializer:Tt(this.movingMeanInitializer),movingVarianceInitializer:Tt(this.movingVarianceInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer),betaConstraint:jt(this.betaConstraint),gammaConstraint:jt(this.gammaConstraint)},t=super.getConfig();return Object.assign(e,t),e}};Ng.className="BatchNormalization";re.registerClass(Ng);var Tg=class extends Xe{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=gt(e.betaInitializer||"zeros"),this.gammaInitializer=gt(e.gammaInitializer||"ones"),this.betaRegularizer=xt(e.betaRegularizer),this.gammaRegularizer=xt(e.gammaRegularizer),this.supportsMasking=!0}build(e){e=st(e);let t=e.length;typeof this.axis=="number"&&(this.axis=[this.axis]);for(let r=0;r<this.axis.length;++r)this.axis[r]<0&&(this.axis[r]+=t);for(let r of this.axis)if(r<0||r>=t)throw new Error(`Invalid axis: ${r}`);if(this.axis.length!==Qr(this.axis).length)throw new Error(`Found duplicate axes in: ${this.axis}`);let n=this.axis.map(r=>e[r]),a=!0;this.scale?this.gamma=this.addWeight("gamma",n,"float32",this.gammaInitializer,this.gammaRegularizer,a):this.gamma=null,this.center?this.beta=this.addWeight("beta",n,"float32",this.betaInitializer,this.betaRegularizer,a):this.beta=null,this.built=!0}call(e,t){let n=Pe(e),a=n.shape,r=a.length;return V(()=>{let s=!0,{mean:i,variance:o}=th(n,this.axis,s),u=Bi(1,r);for(let m of this.axis)u[m]=a[m];let l=m=>m!=null&&m.shape.length!==r&&this.axis!==[r-1]?m.reshape(u):m,d=l(this.gamma.read()),p=l(this.beta.read()),c=[],h=[];for(let m=0;m<r;++m)this.axis.indexOf(m)!==-1?(c.push(a[m]),h.push(1)):(c.push(1),h.push(a[m]));return i=i.tile(c),o=o.tile(c),d=d.tile(h),p=p.tile(h),sp(n,i,o,p,d,this.epsilon)})}getConfig(){let e={axis:this.axis,epsilon:this.epsilon,center:this.center,scale:this.scale,betaInitializer:Tt(this.betaInitializer),gammaInitializer:Tt(this.gammaInitializer),betaRegularizer:dt(this.betaRegularizer),gammaRegularizer:dt(this.gammaRegularizer)},t=super.getConfig();return Object.assign(e,t),e}};Tg.className="LayerNormalization";re.registerClass(Tg);function Qre(e,t,n){return V(()=>{if(e.rank!==4)throw new U(`temporalPadding expects input tensor to be 4-D, but received a ${e.rank}-D tensor.`);if(t==null&&(t=[[1,1],[1,1]]),t.length!==2||t[0].length!==2||t[1].length!==2)throw new U("spatial2dPadding expects `padding` to be an Array of two Arrays, each of which is an Array of two integers.");if(n==null&&(n=Ca()),n!=="channelsLast"&&n!=="channelsFirst")throw new U(`Unknown data format: ${n}. Supported data formats are 'channelsLast' and 'channelsFirst.`);let a;return n==="channelsFirst"?a=[[0,0],[0,0],t[0],t[1]]:a=[[0,0],t[0],t[1],[0,0]],mr(e,a)})}var Cg=class extends Xe{constructor(e){if(e==null&&(e={}),super(e),this.dataFormat=e.dataFormat==null?Ca():e.dataFormat,e.padding==null)this.padding=[[1,1],[1,1]];else if(typeof e.padding=="number")this.padding=[[e.padding,e.padding],[e.padding,e.padding]];else{if(e.padding=e.padding,e.padding.length!==2)throw new U(`ZeroPadding2D expects padding to be a length-2 array, but received a length-${e.padding.length} array.`);let t,n;if(typeof e.padding[0]=="number")t=[e.padding[0],e.padding[0]],n=[e.padding[1],e.padding[1]];else{if(e.padding=e.padding,e.padding[0].length!==2)throw new U(`ZeroPadding2D expects height padding to be a length-2 array, but received a length-${e.padding[0].length} array.`);if(t=e.padding[0],e.padding[1].length!==2)throw new U(`ZeroPadding2D expects width padding to be a length-2 array, but received a length-${e.padding[1].length} array.`);n=e.padding[1]}this.padding=[t,n]}this.inputSpec=[new Ot({ndim:4})]}computeOutputShape(e){e=st(e);let t,n;return this.dataFormat==="channelsFirst"?(e[2]!=null&&e[2]>=0?t=e[2]+this.padding[0][0]+this.padding[0][1]:t=null,e[3]!=null&&e[3]>=0?n=e[3]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],e[1],t,n]):(e[1]!=null&&e[1]>=0?t=e[1]+this.padding[0][0]+this.padding[0][1]:t=null,e[2]!=null&&e[2]>=0?n=e[2]+this.padding[1][0]+this.padding[1][1]:n=null,[e[0],t,n,e[3]])}call(e,t){return V(()=>Qre(Pe(e),this.padding,this.dataFormat))}getConfig(){let e={padding:this.padding,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}};Cg.className="ZeroPadding2D";re.registerClass(Cg);function F0(e,t,n,a,r,s){return V(()=>{Ft(r),M4(s),ua(a),n==null&&(n=[1,1]),a==null&&(a="valid"),r==null&&(r=Ca()),s==null&&(s="max"),e=Ky(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=cd(e,t,n,o):i=od(e,t,n,o),r==="channelsFirst"&&(i=Qe(i,[0,3,1,2])),i})}function j8(e,t,n,a,r,s){return V(()=>{Ft(r),M4(s),ua(a),n==null&&(n=[1,1,1]),a==null&&(a="valid"),r==null&&(r=Ca()),s==null&&(s="max"),e=z8(e,r);let i,o=a==="same"?"same":"valid";return s==="max"?i=Y1(e,t,n,o):i=_1(e,t,n,o),r==="channelsFirst"&&(i=Qe(i,[0,4,1,2,3])),i})}var U8=class extends Xe{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 U(`poolSize for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.poolSize)}`);if(Yt(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 U(`strides for 1D convolutional layer must be a number or an Array of a single number, but received ${JSON.stringify(e.strides)}`);Yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,ua(this.padding),this.inputSpec=[new Ot({ndim:3})]}computeOutputShape(e){e=st(e);let t=Da(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=Hd(Pe(e),2);let n=this.poolingFunction(Pe(e),[this.poolSize[0],1],[this.strides[0],1],this.padding,"channelsLast");return Aa(n,[2])})}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides},t=super.getConfig();return Object.assign(e,t),e}},Eg=class extends U8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ua(a),F0(e,t,n,a,r,"max")}};Eg.className="MaxPooling1D";re.registerClass(Eg);var Rg=class extends U8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ua(a),F0(e,t,n,a,r,"avg")}};Rg.className="AveragePooling1D";re.registerClass(Rg);var H8=class extends Xe{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 U(`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];Yt(this.poolSize,"poolSize"),Yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),ua(this.padding),this.inputSpec=[new Ot({ndim:4})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2];return t=Da(t,this.poolSize[0],this.padding,this.strides[0]),n=Da(n,this.poolSize[1],this.padding,this.strides[1]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n]:[e[0],t,n,e[3]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(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}},Mg=class extends H8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ua(a),F0(e,t,n,a,r,"max")}};Mg.className="MaxPooling2D";re.registerClass(Mg);var Fg=class extends H8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ua(a),F0(e,t,n,a,r,"avg")}};Fg.className="AveragePooling2D";re.registerClass(Fg);var G8=class extends Xe{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 U(`If the strides property of a 3D pooling layer is an Array, it is expected to have a length of 3, but received length ${e.strides.length}.`);this.strides=e.strides}else this.strides=[e.strides,e.strides,e.strides];Yt(this.poolSize,"poolSize"),Yt(this.strides,"strides"),this.padding=e.padding==null?"valid":e.padding,this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),ua(this.padding),this.inputSpec=[new Ot({ndim:5})]}computeOutputShape(e){e=st(e);let t=this.dataFormat==="channelsFirst"?e[2]:e[1],n=this.dataFormat==="channelsFirst"?e[3]:e[2],a=this.dataFormat==="channelsFirst"?e[4]:e[3];return t=Da(t,this.poolSize[0],this.padding,this.strides[0]),n=Da(n,this.poolSize[1],this.padding,this.strides[1]),a=Da(a,this.poolSize[2],this.padding,this.strides[2]),this.dataFormat==="channelsFirst"?[e[0],e[1],t,n,a]:[e[0],t,n,a,e[4]]}call(e,t){return V(()=>(this.invokeCallHook(e,t),this.poolingFunction(Pe(e),this.poolSize,this.strides,this.padding,this.dataFormat)))}getConfig(){let e={poolSize:this.poolSize,padding:this.padding,strides:this.strides,dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},$g=class extends G8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ua(a),j8(e,t,n,a,r,"max")}};$g.className="MaxPooling3D";re.registerClass($g);var Dg=class extends G8{constructor(e){super(e)}poolingFunction(e,t,n,a,r){return Ft(r),ua(a),j8(e,t,n,a,r,"avg")}};Dg.className="AveragePooling3D";re.registerClass(Dg);var q8=class extends Xe{constructor(e){super(e);this.inputSpec=[new Ot({ndim:3})]}computeOutputShape(e){return[e[0],e[2]]}call(e,t){throw new Oe}},_g=class extends q8{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Pe(e);return Nt(n,1)})}};_g.className="GlobalAveragePooling1D";re.registerClass(_g);var Og=class extends q8{constructor(e){super(e||{})}call(e,t){return V(()=>{let n=Pe(e);return Bn(n,1)})}};Og.className="GlobalMaxPooling1D";re.registerClass(Og);var X8=class extends Xe{constructor(e){super(e);this.dataFormat=e.dataFormat==null?"channelsLast":e.dataFormat,Ft(this.dataFormat),this.inputSpec=[new Ot({ndim:4})]}computeOutputShape(e){return e=e,this.dataFormat==="channelsLast"?[e[0],e[3]]:[e[0],e[1]]}call(e,t){throw new Oe}getConfig(){let e={dataFormat:this.dataFormat},t=super.getConfig();return Object.assign(e,t),e}},zg=class extends X8{call(e,t){return V(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?Nt(n,[1,2]):Nt(n,[2,3])})}};zg.className="GlobalAveragePooling2D";re.registerClass(zg);var Pg=class extends X8{call(e,t){return V(()=>{let n=Pe(e);return this.dataFormat==="channelsLast"?Bn(n,[1,2]):Bn(n,[2,3])})}};Pg.className="GlobalMaxPooling2D";re.registerClass(Pg);var K8=class extends Xe{constructor(e){super(e);this.layer=e.layer}build(e){this.built=!0}get trainable(){return this.layer!=null?this.layer.trainable:!1}set trainable(e){this.layer!=null&&(this.layer.trainable=e)}get trainableWeights(){return this.layer.trainableWeights}get nonTrainableWeights(){return this.layer.nonTrainableWeights}get updates(){return this.layer._updates}get losses(){return this.layer.losses}getWeights(){return this.layer.getWeights()}setWeights(e){this.layer.setWeights(e)}getConfig(){let e={layer:{className:this.layer.getClassName(),config:this.layer.getConfig()}},t=super.getConfig();return Object.assign(e,t),e}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.layer!=null&&this.layer.setFastWeightInitDuringBuild(e)}static fromConfig(e,t,n={}){let a=t.layer,r=$a(a,n);delete t.layer;let s={layer:r};return Object.assign(s,t),new e(s)}},Lg=class extends K8{constructor(e){super(e);this.supportsMasking=!0}build(e){if(e=st(e),e.length<3)throw new U(`TimeDistributed layer expects an input shape >= 3D, but received input shape ${JSON.stringify(e)}`);this.inputSpec=[{shape:e}];let t=[e[0]].concat(e.slice(2));this.layer.built||(this.layer.build(t),this.layer.built=!0),super.build(e)}computeOutputShape(e){e=st(e);let t=[e[0]].concat(e.slice(2)),n=this.layer.computeOutputShape(t),a=e[1];return[n[0],a].concat(n.slice(1))}call(e,t){return V(()=>(e=Pe(e),B8((n,a)=>[Pe(this.layer.call(n,t)),[]],e,[],!1,null,null,!1,!0)[1]))}};Lg.className="TimeDistributed";re.registerClass(Lg);function ese(e){ji(iae,"BidirectionalMergeMode",e)}var tse="concat",Wg=class extends K8{constructor(e){super(e);let t=e.layer.getConfig(),n={};n.className=e.layer.getClassName(),n.config=t,this.forwardLayer=$a(n),t.goBackwards=t.goBackwards!==!0;let a={};if(a.className=e.layer.getClassName(),a.config=t,this.backwardLayer=$a(a),this.forwardLayer.name="forward_"+this.forwardLayer.name,this.backwardLayer.name="backward_"+this.backwardLayer.name,this.mergeMode=e.mergeMode===void 0?tse:e.mergeMode,ese(this.mergeMode),e.weights)throw new Oe("weights support is not implemented for Bidirectional layer yet.");this._stateful=e.layer.stateful,this.returnSequences=e.layer.returnSequences,this.returnState=e.layer.returnState,this.supportsMasking=!0,this._trainable=!0,this.inputSpec=e.layer.inputSpec,this.numConstants=null}get trainable(){return this._trainable}set trainable(e){this._trainable=e,this.forwardLayer!=null&&(this.forwardLayer.trainable=e),this.backwardLayer!=null&&(this.backwardLayer.trainable=e)}getWeights(){return this.forwardLayer.getWeights().concat(this.backwardLayer.getWeights())}setWeights(e){let t=e.length,n=Math.floor(t/2);this.forwardLayer.setWeights(e.slice(0,n)),this.backwardLayer.setWeights(e.slice(n))}computeOutputShape(e){let t=this.forwardLayer.computeOutputShape(e);Array.isArray(t)&&Array.isArray(t[0])||(t=[t]),t=t;let n,a,r;return this.returnState&&(r=t.slice(1)),n=t[0],n=n,this.mergeMode==="concat"?(n[n.length-1]*=2,a=[n]):this.mergeMode==null?a=[n,n.slice()]:a=[n],this.returnState?this.mergeMode==null?a.concat(r).concat(r.slice()):[n].concat(r).concat(r.slice()):Fn(a)}apply(e,t){let n=t==null?null:t.initialState,a=t==null?null:t.constants;t==null&&(t={});let r=W8(e,n,a,this.numConstants);if(e=r.inputs,n=r.initialState,a=r.constants,Array.isArray(e)&&(n=e.slice(1),e=e[0]),(n==null||n.length===0)&&a==null)return super.apply(e,t);let s=[],i=[];if(n!=null){let u=n.length;if(u%2>0)throw new U("When passing `initialState` to a Bidrectional RNN, the state should be an Array containing the states of the underlying RNNs.");t.initialState=n,s.push(...n);let l=n.map(d=>new Ot({shape:d.shape}));this.forwardLayer.stateSpec=l.slice(0,u/2),this.backwardLayer.stateSpec=l.slice(u/2),i.push(...l)}if(a!=null)throw new Oe("Support for constants in Bidirectional layers is not implemented yet.");let o=s[0]instanceof Fa;for(let u of s)if(u instanceof Fa!==o)throw new U("The initial state of a Bidirectional layer cannot be specified as a mix of symbolic and non-symbolic tensors");if(o){let u=[e].concat(s),l=this.inputSpec.concat(i),d=this.inputSpec;this.inputSpec=l;let p=super.apply(u,t);return this.inputSpec=d,p}else return super.apply(e,t)}call(e,t){return V(()=>{let n=t.initialState,a,r;if(n==null)a=this.forwardLayer.call(e,t),r=this.backwardLayer.call(e,t);else{let o=n.slice(0,n.length/2),u=n.slice(n.length/2);a=this.forwardLayer.call(e,Object.assign(t,{initialState:o})),r=this.backwardLayer.call(e,Object.assign(t,{initialState:u}))}let s;this.returnState&&(Array.isArray(a)&&(s=a.slice(1).concat(r.slice(1))),a=a[0],r=r[0]),this.returnSequences&&(r=Un(r,1));let i;return this.mergeMode==="concat"?i=fy([a,r]):this.mergeMode==="sum"?i=ie(a,r):this.mergeMode==="ave"?i=B(.5,ie(a,r)):this.mergeMode==="mul"?i=B(a,r):this.mergeMode==null&&(i=[a,r]),this.returnState?this.mergeMode==null?i.concat(s):[i].concat(s):i})}resetStates(e){this.forwardLayer.resetStates(),this.backwardLayer.resetStates()}build(e){Ui(this.forwardLayer.name,()=>{this.forwardLayer.build(e)}),Ui(this.backwardLayer.name,()=>{this.backwardLayer.build(e)}),this.built=!0}computeMask(e,t){Array.isArray(t)&&(t=t[0]);let n;if(this.returnSequences?this.mergeMode==null?n=[t,t]:n=t:this.mergeMode==null?n=[null,null]:n=null,this.returnState){let a=this.forwardLayer.states.map(r=>null);return Array.isArray(n)?n.concat(a).concat(a):[n].concat(a).concat(a)}else return n}get trainableWeights(){return this.forwardLayer.trainableWeights.concat(this.backwardLayer.trainableWeights)}get nonTrainableWeights(){return this.forwardLayer.nonTrainableWeights.concat(this.backwardLayer.nonTrainableWeights)}setFastWeightInitDuringBuild(e){super.setFastWeightInitDuringBuild(e),this.forwardLayer!=null&&this.forwardLayer.setFastWeightInitDuringBuild(e),this.backwardLayer!=null&&this.backwardLayer.setFastWeightInitDuringBuild(e)}getConfig(){let e={mergeMode:this.mergeMode},t=super.getConfig();return Object.assign(e,t),e}static fromConfig(e,t){let n=$a(t.layer);if(delete t.layer,t.numConstants!=null)throw new Oe("Deserialization of a Bidirectional layer with numConstants present is not supported yet.");let a=t;return a.layer=n,new e(a)}};Wg.className="Bidirectional";re.registerClass(Wg);function nse(e){return new eu(e)}function ase(e){return new Gy(e)}function rse(e){return new jy(e)}function sse(e){return new Uy(e)}function ise(e){return new Hy(e)}function ose(e){return new Xy(e)}function lse(e){return new qy(e)}function use(e){return new N0(e)}function dse(e){return new ep(e)}function pse(e){return new 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TypeError(`Node type ${e.op} is not implemented`)}};function va(e,t,n=""){if(!(typeof e=="number"||typeof t=="number")){k.assert(e.length===t.length,()=>n+` Shapes ${e} and ${t} must match`);for(let a=0;a<e.length;a++){let r=e[a],s=t[a];k.assert(r<0||s<0||r===s,()=>n+` Shapes ${e} and ${t} must match`)}}}function Mk(e){return!(typeof e=="number"||e.some(t=>t<0))}function ip(e,t,n){let a=n2(e,n),r=!Mk(a);if(r&&t.length===0)throw new Error(`Tried to calculate elements of an empty list with non-fully-defined elementShape: ${a}`);if(r&&t.forEach(s=>{a=n2(s.shape,a)}),!Mk(a))throw new Error(`Non-fully-defined elementShape: ${a}`);return a}function n2(e,t){if(typeof e=="number")return t;if(typeof t=="number")return e;if(e.length!==t.length)throw new Error(`Incompatible ranks during merge: ${e} vs. ${t}`);let n=[];for(let a=0;a<e.length;++a){let r=e[a],s=t[a];if(r>=0&&s>=0&&r!==s)throw new Error(`Incompatible shape during merge: ${e} vs. ${t}`);n[a]=r>=0?r:s}return n}var roe=class{constructor(e,t,n,a,r,s,i){this.name=e,this.dtype=t,this.maxSize=n,this.elementShape=a,this.identicalElementShapes=r,this.dynamicSize=s,this.clearAfterRead=i,this.tensors=[],this.closed_=!1,this.idTensor=we(0),Xt(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 n=this.tensors[e]||{};if(t.dtype!==this.dtype)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e},
|
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because the value dtype is ${t.dtype}, but TensorArray dtype is ${this.dtype}.`);if(this.size()===0&&(this.elementShape==null||this.elementShape.length===0)&&(this.elementShape=t.shape),va(this.elementShape,t.shape,`TensorArray ${this.name}: Could not write to TensorArray index ${e}.`),n.read)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been read.`);if(n.written)throw new Error(`TensorArray ${this.name}: Could not write to TensorArray index ${e}, because it has already been written.`);n.tensor=t,Xt(t),n.written=!0,this.tensors[e]=n}writeMany(e,t){if(e.length!==t.length)throw new Error(`TensorArray ${this.name}: could not write multiple tensors,because the index size: ${e.length} is not the same as tensors size: ${t.length}.`);e.forEach((n,a)=>this.write(n,t[a]))}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 a=0;a<this.size();a++)e.push(a)}if(e.length===0)return on([],[0].concat(this.elementShape));let n=this.readMany(e);return va(this.elementShape,n[0].shape,"TensorArray shape mismatch: "),mn(n,0)}concat(e){if(!!e&&e!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but concat requested dtype ${e}`);if(this.size()===0)return on([],[0].concat(this.elementShape));let t=[];for(let a=0;a<this.size();a++)t.push(a);let n=this.readMany(t);return va(this.elementShape,n[0].shape,`TensorArray shape mismatch: tensor array shape (${this.elementShape}) vs first tensor shape (${n[0].shape})`),lt(n,0)}scatter(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);if(e.length!==t.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${e.length} vs. ${t.shape[0]}`);let n=Math.max(...e);if(!this.dynamicSize&&n>=this.maxSize)throw new Error(`Max index must be < array size (${n} vs. ${this.maxSize})`);this.writeMany(e,ya(t,0))}split(e,t){if(t.dtype!==this.dtype)throw new Error(`TensorArray dtype is ${this.dtype} but tensor has dtype ${t.dtype}`);let n=0,a=e.map(o=>(n+=o,n));if(n!==t.shape[0])throw new Error(`Expected sum of lengths to be equal to
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tensor.shape[0], but sum of lengths is
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${n}, and tensor's shape is: ${t.shape}`);if(!this.dynamicSize&&e.length!==this.maxSize)throw new Error(`TensorArray's size is not equal to the size of lengths (${this.maxSize} vs. ${e.length}), and the TensorArray is not marked as dynamically resizeable`);let r=n===0?0:t.size/n,s=[];V(()=>{t=q(t,[1,n,r]);for(let o=0;o<e.length;++o){let u=o===0?0:a[o-1],l=[0,u,0],d=[1,e[o],r];s[o]=q(Re(t,l,d),this.elementShape)}return s});let i=[];for(let o=0;o<e.length;o++)i[o]=o;this.writeMany(i,s)}},op=class{constructor(e,t,n,a=-1){this.tensors=e,this.elementShape=t,this.elementDtype=n,e!=null&&e.forEach(r=>{if(n!==r.dtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${r.dtype}`);va(t,r.shape,"TensorList shape mismatch: "),Xt(r)}),this.idTensor=we(0),this.maxNumElements=a,Xt(this.idTensor)}get id(){return this.idTensor.id}copy(){return new op([...this.tensors],this.elementShape,this.elementDtype)}clearAndClose(e){this.tensors.forEach(t=>{(e==null||!e.has(t.id))&&t.dispose()}),this.tensors.length=0,this.idTensor.dispose()}size(){return this.tensors.length}stack(e,t,n=-1){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(n!==-1&&this.tensors.length!==n)throw new Error(`Operation expected a list with ${n} elements but got a list with ${this.tensors.length} elements.`);va(e,this.elementShape,"TensorList shape mismatch: ");let a=ip(this.elementShape,this.tensors,e);return V(()=>{let r=this.tensors.map(s=>q(s,a));return mn(r,0)})}popBack(e,t){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);if(this.size()===0)throw new Error("Trying to pop from an empty list.");let n=ip(this.elementShape,this.tensors,e),a=this.tensors.pop();return va(a.shape,e,"TensorList shape mismatch: "),q(a,n)}pushBack(e){if(e.dtype!==this.elementDtype)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${this.elementDtype}`);if(va(e.shape,this.elementShape,"TensorList shape mismatch: "),this.maxNumElements===this.size())throw new Error("Trying to push element into a full list.");Xt(e),this.tensors.push(e)}resize(e){if(e<0)throw new Error(`TensorListResize expects size to be non-negative. Got: ${e}`);if(this.maxNumElements!==-1&&e>this.maxNumElements)throw new Error(`TensorListResize input size ${e} is greater maxNumElement ${this.maxNumElements}.`);this.tensors.length=e}getItem(e,t,n){if(n!==this.elementDtype)throw new Error(`Invalid data types; op elements ${n}, but list elements ${this.elementDtype}`);if(e<0||e>this.tensors.length)throw new Error(`Trying to access element ${e} in a list with ${this.tensors.length} elements.`);if(this.tensors[e]==null)throw new Error(`element at index ${e} is null.`);va(this.tensors[e].shape,t,"TensorList shape mismatch: ");let a=ip(this.elementShape,this.tensors,t);return q(this.tensors[e],a)}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.`);va(this.elementShape,t.shape,"TensorList shape mismatch: "),Xt(t),this.tensors[e]=t}gather(e,t,n){if(t!==this.elementDtype)throw new Error(`Invalid data types; op elements ${t}, but list elements ${this.elementDtype}`);va(this.elementShape,n,"TensorList shape mismatch: "),e=e.slice(0,this.size());let a=ip(this.elementShape,this.tensors,n);return e.length===0?on([],[0].concat(a)):V(()=>{let r=e.map(s=>q(this.tensors[s],a));return mn(r,0)})}concat(e,t){if(!!e&&e!==this.elementDtype)throw new Error(`TensorList dtype is ${this.elementDtype} but concat requested dtype ${e}`);va(this.elementShape,t,"TensorList shape mismatch: ");let n=ip(this.elementShape,this.tensors,t);return this.size()===0?on([],[0].concat(n)):V(()=>{let a=this.tensors.map(r=>q(r,n));return lt(a,0)})}};function soe(e,t,n){let a=e.dtype;if(e.shape.length<1)throw new Error(`Tensor must be at least a vector, but saw shape: ${e.shape}`);if(e.dtype!==n)throw new Error(`Invalid data types; op elements ${e.dtype}, but list elements ${n}`);let r=e.shape.slice(1);va(r,t,"TensorList shape mismatch: ");let s=ya(e);return new op(s,t,a)}function ioe(e,t,n){return new op([],e,t,n)}function ooe(e,t,n,a){if(t.length!==e.shape[0])throw new Error(`Expected len(indices) == tensor.shape[0], but saw: ${t.length} vs. ${e.shape[0]}`);let r=Math.max(...t);if(a!=null&&a!==-1&&r>=a)throw new Error(`Max index must be < array size (${r} vs. ${a})`);let s=new op([],n,e.dtype,a),i=ya(e,0);return t.forEach((o,u)=>{s.setItem(o,i[u])}),s}function loe(e,t,n){let a=0,r=t.map(d=>(a+=d,a));if(a!==e.shape[0])throw new Error(`Expected sum of lengths to be equal to
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tensor.shape[0], but sum of lengths is
|
|
${a}, and tensor's shape is: ${e.shape}`);let s=e.shape.slice(1),i=n2(s,n),o=a===0?0:e.size/a,u=V(()=>{let d=[];e=q(e,[1,a,o]);for(let p=0;p<t.length;++p){let c=p===0?0:r[p-1],h=[0,c,0],m=[1,t[p],o];d[p]=q(Re(e,h,m),i)}return e.dispose(),d}),l=new op([],n,e.dtype,t.length);for(let d=0;d<u.length;d++)l.setItem(d,u[d]);return l}var uoe=async(e,t,n)=>{switch(e.op){case"If":case"StatelessIf":{let a=I("thenBranch",e,t,n),r=I("elseBranch",e,t,n),s=I("cond",e,t,n),i=I("args",e,t,n);return(await s.data())[0]?n.functionMap[a].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap):n.functionMap[r].executeFunctionAsync(i,n.tensorArrayMap,n.tensorListMap)}case"While":case"StatelessWhile":{let a=I("body",e,t,n),r=I("cond",e,t,n),s=I("args",e,t,n),i=await n.functionMap[r].executeFunctionAsync(s,n.tensorArrayMap,n.tensorListMap),o=s.map(d=>d.id),u=await i[0].data();i.forEach(d=>{!d.kept&&o.indexOf(d.id)===-1&&d.dispose()});let l=s;for(;u[0];){let d=l;l=await n.functionMap[a].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);let p=l.map(h=>h.id);d.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()});let c=await n.functionMap[r].executeFunctionAsync(l,n.tensorArrayMap,n.tensorListMap);u=await c[0].data(),c.forEach(h=>{!h.kept&&o.indexOf(h.id)===-1&&p.indexOf(h.id)===-1&&h.dispose()})}return l}case"LoopCond":{let a=I("pred",e,t,n);return[Ir(a)]}case"Switch":{let a=I("pred",e,t,n),r=I("data",e,t,n);return r.kept||(r=Ir(r)),(await a.data())[0]?[void 0,r]:[r,void 0]}case"Merge":{let a=e.inputNames.find(r=>wn(r,t,n)!==void 0);if(a){let r=wn(a,t,n);return[Ir(r)]}return}case"Enter":{let a=I("frameName",e,t,n),r=I("tensor",e,t,n);return n.enterFrame(a),[Ir(r)]}case"Exit":{let a=I("tensor",e,t,n);return n.exitFrame(),[Ir(a)]}case"NextIteration":{let a=I("tensor",e,t,n);return n.nextIteration(),[Ir(a)]}case"TensorArrayV3":{let a=I("size",e,t,n),r=I("dtype",e,t,n),s=I("elementShape",e,t,n),i=I("dynamicSize",e,t,n),o=I("clearAfterRead",e,t,n),u=I("identicalElementShapes",e,t,n),l=I("name",e,t,n),d=new roe(l,r,a,s,u,i,o);return n.addTensorArray(d),[d.idTensor,we(1)]}case"TensorArrayWriteV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.write(r,s),[i.idTensor]}case"TensorArrayReadV3":{let a=I("tensorArrayId",e,t,n),r=I("index",e,t,n);return[n.getTensorArray(a.id).read(r)]}case"TensorArrayGatherV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("dtype",e,t,n);return[n.getTensorArray(a.id).gather(r,s)]}case"TensorArrayScatterV3":{let a=I("tensorArrayId",e,t,n),r=I("indices",e,t,n),s=I("tensor",e,t,n),i=n.getTensorArray(a.id);return i.scatter(r,s),[i.idTensor]}case"TensorArrayConcatV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id),s=I("dtype",e,t,n);return[r.concat(s)]}case"TensorArraySplitV3":{let a=I("tensorArrayId",e,t,n),r=I("tensor",e,t,n),s=I("lengths",e,t,n),i=n.getTensorArray(a.id);return i.split(s,r),[i.idTensor]}case"TensorArraySizeV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return[we(r.size(),"int32")]}case"TensorArrayCloseV3":{let a=I("tensorArrayId",e,t,n),r=n.getTensorArray(a.id);return r.clearAndClose(),[r.idTensor]}case"TensorListSetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("tensor",e,t,n),i=n.getTensorList(a.id);return i.setItem(r,s),[i.idTensor]}case"TensorListGetItem":{let a=I("tensorListId",e,t,n),r=I("index",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).getItem(r,s,i)]}case"TensorListScatterV2":case"TensorListScatter":{let a=I("indices",e,t,n),r=I("tensor",e,t,n),s=I("elementShape",e,t,n),i=I("numElements",e,t,n),o=ooe(r,a,s,i);return n.addTensorList(o),[o.idTensor]}case"TensorListReserve":case"EmptyTensorList":{let a=I("elementShape",e,t,n),r=I("elementDType",e,t,n),s;e.op==="TensorListReserve"?s="numElements":s="maxNumElements";let i=I(s,e,t,n),o=ioe(a,r,i);return n.addTensorList(o),[o.idTensor]}case"TensorListGather":{let a=I("tensorListId",e,t,n),r=I("indices",e,t,n),s=I("elementShape",e,t,n),i=I("elementDType",e,t,n);return[n.getTensorList(a.id).gather(r,i,s)]}case"TensorListStack":{let a=I("tensorListId",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=I("numElements",e,t,n);return[n.getTensorList(a.id).stack(r,s,i)]}case"TensorListFromTensor":{let a=I("tensor",e,t,n),r=I("elementShape",e,t,n),s=I("elementDType",e,t,n),i=soe(a,r,s);return n.addTensorList(i),[i.idTensor]}case"TensorListConcat":{let a=I("tensorListId",e,t,n),r=n.getTensorList(a.id),s=I("dtype",e,t,n),i=I("elementShape",e,t,n);return[r.concat(s,i)]}case"TensorListPushBack":{let a=I("tensorListId",e,t,n),r=I("tensor",e,t,n),s=n.getTensorList(a.id);return s.pushBack(r),[s.idTensor]}case"TensorListPopBack":{let 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implemented`)}},koe=(e,t,n)=>{switch(e.op){case"SparseFillEmptyRows":{let{outputIndices:a,outputValues:r,emptyRowIndicator:s,reverseIndexMap:i}=vd.sparseFillEmptyRows(I("indices",e,t,n),I("values",e,t,n),I("denseShape",e,t,n),I("defaultValue",e,t,n));return[a,r,s,i]}case"SparseReshape":{let{outputIndices:a,outputShape:r}=vd.sparseReshape(I("inputIndices",e,t,n),I("inputShape",e,t,n),I("newShape",e,t,n));return[a,r]}case"SparseSegmentMean":return[vd.sparseSegmentMean(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];case"SparseSegmentSum":return[vd.sparseSegmentSum(I("data",e,t,n),I("indices",e,t,n),I("segmentIds",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Ioe=(e,t,n)=>{switch(e.op){case"FFT":return[xd(I("x",e,t,n))];case"IFFT":return[Dl(I("x",e,t,n))];case"RFFT":return[bd(I("x",e,t,n))];case"IRFFT":return[ph(I("x",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Soe=(e,t,n)=>{switch(e.op){case"StringNGrams":{let{nGrams:a,nGramsSplits:r}=bh.stringNGrams(I("data",e,t,n),I("dataSplits",e,t,n),I("separator",e,t,n),I("nGramWidths",e,t,n),I("leftPad",e,t,n),I("rightPad",e,t,n),I("padWidth",e,t,n),I("preserveShortSequences",e,t,n));return[a,r]}case"StringSplit":{let{indices:a,values:r,shape:s}=bh.stringSplit(I("input",e,t,n),I("delimiter",e,t,n),I("skipEmpty",e,t,n));return[a,r,s]}case"StringToHashBucketFast":return[bh.stringToHashBucketFast(I("input",e,t,n),I("numBuckets",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}},Noe=(e,t,n)=>{switch(e.op){case"Cast":return[me(I("x",e,t,n),I("dtype",e,t,n))];case"ExpandDims":{let a=I("axis",e,t,n);return[fn(I("x",e,t,n),a)]}case"Squeeze":{let a=I("axis",e,t,n);return[Aa(I("x",e,t,n),a)]}case"Reshape":return[q(I("x",e,t,n),I("shape",e,t,n))];case"MirrorPad":return[J1(I("x",e,t,n),I("padding",e,t,n),I("mode",e,t,n))];case"PadV2":case"Pad":return[mr(I("x",e,t,n),I("padding",e,t,n),I("constantValue",e,t,n))];case"SpaceToBatchND":{let a=I("blockShape",e,t,n),r=I("paddings",e,t,n);return[fd(I("x",e,t,n),a,r)]}case"BatchToSpaceND":{let a=I("blockShape",e,t,n),r=I("crops",e,t,n);return[ld(I("x",e,t,n),a,r)]}case"DepthToSpace":{let a=I("blockSize",e,t,n),r=I("dataFormat",e,t,n).toUpperCase();return[W1(I("x",e,t,n),a,r)]}case"BroadcastTo":return[Sl(I("x",e,t,n),I("shape",e,t,n))];default:throw TypeError(`Node type ${e.op} is not implemented`)}};function $k(e,t,n,a){let r=((s,i,o)=>{switch(s.category){case"arithmetic":return V(()=>noe(s,i,o));case"basic_math":return V(()=>aoe(s,i,o));case"control":return uoe(s,i,o);case"convolution":return V(()=>doe(s,i,o));case"creation":return V(()=>poe(s,i,o));case"dynamic":return coe(s,i,o);case"evaluation":return V(()=>hoe(s,i,o));case"image":return V(()=>yoe(s,i,o));case"graph":return V(()=>foe(s,i,o));case"logical":return V(()=>goe(s,i,o));case"matrices":return V(()=>xoe(s,i,o));case"normalization":return V(()=>boe(s,i,o));case"reduction":return V(()=>voe(s,i,o));case"slice_join":return V(()=>woe(s,i,o));case"sparse":return V(()=>koe(s,i,o));case"spectral":return V(()=>Ioe(s,i,o));case"string":return V(()=>Soe(s,i,o));case"transformation":return V(()=>Noe(s,i,o));case"hash_table":return Aoe(s,i,o,a);case"custom":let u=ok(s.op);if(u&&u.customExecutor)return u.customExecutor(new toe(s,i,o));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()`)}})(e,t,n);return k.isPromise(r)?r.then(s=>[].concat(s)):[].concat(r)}var Dk=class{constructor(e={},t={},n={},a={}){this.weightMap=e,this.tensorArrayMap=t,this.tensorListMap=n,this.functionMap=a,this.rootContext={id:0,frameName:"",iterationId:0},this.contexts=[this.rootContext],this.lastId=0,this.generateCurrentContextIds()}newFrame(e,t){return{id:e,frameName:t,iterationId:0}}set currentContext(e){this.contexts!==e&&(this.contexts=e,this.generateCurrentContextIds())}get currentContext(){return this.contexts}get currentContextId(){return this._currentContextIds[0]}get currentContextIds(){return this._currentContextIds}generateCurrentContextIds(){let e=[];for(let t=0;t<this.contexts.length-1;t++){let n=this.contexts.slice(0,this.contexts.length-t);e.push(this.contextIdforContexts(n))}e.push(""),this._currentContextIds=e}contextIdforContexts(e){return e?e.map(t=>t.id===0&&t.iterationId===0?"":`${t.frameName}-${t.iterationId}`).join("/"):""}enterFrame(e){this.contexts&&(this.lastId++,this.contexts=this.contexts.slice(),this.contexts.push(this.newFrame(this.lastId,e)),this._currentContextIds.unshift(this.contextIdforContexts(this.contexts)))}exitFrame(){if(this.contexts&&this.contexts.length>1)this.contexts=this.contexts.slice(),this.contexts.splice(-1),this.currentContextIds.shift();else throw new Error("Cannot exit frame, the context is empty")}nextIteration(){if(this.contexts&&this.contexts.length>0){this.contexts=this.contexts.slice(),this.lastId++;let e=Object.assign({},this.contexts[this.contexts.length-1]);e.iterationId+=1,e.id=this.lastId,this.contexts.splice(-1,1,e),this._currentContextIds.splice(0,1,this.contextIdforContexts(this.contexts))}else throw new Error("Cannot increase frame iteration, the context is empty")}getWeight(e){return this.weightMap[e]}addTensorArray(e){this.tensorArrayMap[e.id]=e}getTensorArray(e){return this.tensorArrayMap[e]}addTensorList(e){this.tensorListMap[e.id]=e}getTensorList(e){return this.tensorListMap[e]}dispose(e){for(let t in this.tensorArrayMap)this.tensorArrayMap[t].clearAndClose(e);for(let t in this.tensorListMap)this.tensorListMap[t].clearAndClose(e)}};function _k(e,t,n,a){let r=new Set,s=[],i=null,o=null,u=new Set,l=Object.keys(e).map(c=>qn(c)[0]),d=[];a!=null&&(d=a.map(c=>qn(c.name)[0]));let p=[...t];for(;p.length>0;){let c=p.pop();if((Ok(c)||Moe(c)||Foe(c))&&i==null&&(i=c,o=i.children.map(h=>h.name).filter(h=>r.has(h))),r.add(c.name),n[c.name]==null&&l.indexOf(c.name)===-1&&d.indexOf(c.name)===-1){if(c.inputs.length===0){s.push(c.name);continue}c.inputs.forEach(h=>{u.has(h.name)||(u.add(h.name),p.push(h))})}}return{inputs:e,outputs:t,usedNodes:r,missingInputs:s,dynamicNode:i,syncInputs:o}}function Toe(e,t,n){let{usedNodes:a,inputs:r}=n,s=[],i=Object.keys(r).map(d=>qn(d)[0]).map(d=>e.nodes[d]),o=e.initNodes;i.forEach(d=>{a.has(d.name)&&s.push(d)}),e.weights.forEach(d=>{a.has(d.name)&&s.push(d)}),o!=null&&o.forEach(d=>{a.has(d.name)&&s.push(d)});let u=new Set,l=[];for(;s.length>0;){let d=s.pop();u.add(d.name),t[d.name]||l.push(d),d.children.forEach(p=>{!u.has(p.name)&&a.has(p.name)&&p.inputs.every(c=>u.has(c.name))&&s.push(p)})}return l}var Coe=["Switch","Merge","Enter","Exit","NextIteration","StatelessIf","StatelessWhile","if","While"],Eoe=["NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV5","Where"],Roe=["HashTable","HashTableV2","LookupTableImport","LookupTableImportV2","LookupTableFind","LookupTableFindV2","LookupTableSize","LookupTableSizeV2"];function Ok(e){return Coe.indexOf(e.op)>=0}function Moe(e){return Eoe.indexOf(e.op)>=0}function Foe(e){return Roe.indexOf(e.op)>=0}var r2=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(n=>{this._functionExecutorMap[n]=new r2(e.functions[n],this)})}get weightIds(){return this.parent?this.parent.weightIds:this._weightIds}get functionExecutorMap(){return this.parent?this.parent.functionExecutorMap:this._functionExecutorMap}get weightMap(){return this.parent?this.parent.weightMap:this._weightMap}set weightMap(e){let t=Object.keys(e).map(n=>e[n].map(a=>a.id));this._weightIds=[].concat(...t),this._weightMap=e}set resourceManager(e){this._resourceManager=e}get inputs(){return this._inputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get outputs(){return this._outputs.map(e=>({name:e.name,shape:e.attrParams.shape?e.attrParams.shape.value:void 0,dtype:e.attrParams.dtype?e.attrParams.dtype.value:void 0}))}get inputNodes(){return this._inputs.map(e=>e.signatureKey||e.name)}get outputNodes(){return this._outputs.map(e=>{let t=e.signatureKey||e.name;return e.defaultOutput?`${t}:${e.defaultOutput}`:t})}get functions(){return Object.keys(this._functions).reduce((e,t)=>(e[t]=this._functions[t].signature,e),{})}getCompilationKey(e,t){let n=e.map(r=>r.name).sort(),a=t.map(r=>r.name).sort();return n.join(this.SEPERATOR)+"--"+a.join(this.SEPERATOR)}compile(e,t){let n=_k(e,t,this.weightMap,this._initNodes),{missingInputs:a,dynamicNode:r,syncInputs:s}=n;if(r!=null)throw new Error(`This execution contains the node '${r.name}', which has the dynamic op '${r.op}'. Please use model.executeAsync() instead. Alternatively, to avoid the dynamic ops, specify the inputs [${s}]`);if(a.length>0){let i=t.map(u=>u.name),o=Object.keys(e);throw new Error(`Cannot compute the outputs [${i}] from the provided inputs [${o}]. Missing the following inputs: [${a}]`)}return Toe(this.graph,this.weightMap,n)}execute(e,t){e=this.mapInputs(e);let n=Object.keys(e).sort();this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t);let a=n.map(d=>this.graph.nodes[qn(d)[0]]),r=t.map(d=>qn(d)[0]),s=r.map(d=>this.graph.nodes[d]);s.length===0&&(s=this._outputs);let i=this.getCompilationKey(a,s),o=this.compiledMap.get(i);o==null&&(o=this.compile(e,s),this.compiledMap.set(i,o));let u={},l={};return V(()=>{let d=new Dk(this.weightMap,u,l,this.functionExecutorMap),p=Object.assign({},this.weightMap);Object.keys(e).forEach(m=>{let[f,A]=qn(m),y=[];y[A]=e[m],p[f]=y});let c=this.getFrozenTensorIds(p),h={};for(let m=0;m<o.length;m++){let f=o[m];if(!p[f.name]){let A=$k(f,p,d,this._resourceManager);if(k.isPromise(A))throw new Error(`The execution of the op '${f.op}' returned a promise. Please use model.executeAsync() instead.`);p[f.name]=A,this.checkTensorForDisposal(f.name,f,p,d,c,r,h)}}return this.parent==null&&d.dispose(c),t.map(m=>wn(m,p,d))})}getFrozenTensorIds(e){let t=[].concat.apply([],Object.keys(e).map(n=>e[n]).map(n=>n.map(a=>a.id)));return new Set(t)}checkTensorForDisposal(e,t,n,a,r,s,i){t.category==="control"||s.indexOf(e)!==-1||(n[e].forEach(o=>{o!=null&&(i[o.id]=(i[o.id]||0)+t.children.length)}),t.inputs.forEach(o=>{if(o.category!=="control"){let u=Die(o.name,n,a);u!=null&&u.forEach(l=>{if(l&&!l.kept&&!r.has(l.id)){let d=i[l.id];d===1?(l.dispose(),delete i[l.id]):d!=null&&i[l.id]--}})}}))}async executeAsync(e,t){return this._executeAsync(e,t)}async _executeAsync(e,t,n=!1,a={},r={}){n||(e=this.mapInputs(e),this.checkInputs(e),this.checkInputShapeAndType(e),t=this.mapOutputs(t),this.checkOutputs(t));let s=new Dk(this.weightMap,a,r,this.functionExecutorMap),i=await this.executeWithControlFlow(e,s,t,n),o=t.map(p=>wn(p,i,s)),u=o.map(p=>p.id),l=Object.keys(e).map(p=>e[p].id),d=new Set([...u,...l,...this.weightIds]);return Object.keys(i).forEach(p=>{i[p].forEach(c=>{c&&!c.kept&&!c.isDisposed&&!d.has(c.id)&&c.dispose()})}),this.parent==null&&s.dispose(d),o}async executeFunctionAsync(e,t,n){let a=e.reduce((r,s,i)=>(r[this.inputs[i].name]=s,r),{});return this._executeAsync(a,this.outputNodes,!0,t,n)}async executeWithControlFlow(e,t,n,a){let r=Object.keys(e),s=r.map(g=>this.graph.nodes[qn(g)[0]]),i=n.map(g=>qn(g)[0]),o=i.map(g=>this.graph.nodes[g]);o.length===0&&(o=this._outputs);let{usedNodes:u,missingInputs:l,dynamicNode:d,syncInputs:p}=_k(e,o,this.weightMap,this._initNodes),c=[...s,...this.graph.weights,...this._initNodes||[]].map(g=>({node:g,contexts:t.currentContext})),h=Object.assign({},this.weightMap);Object.keys(e).forEach(g=>{let[x,v]=qn(g),b=[];b[v]=e[g],h[x]=b});let m={},f=this.getFrozenTensorIds(h),A={};for(;c.length>0;){let g=this.processStack(s,c,t,h,A,f,i,m,u);await Promise.all(g)}d==null&&!a&&console.warn("This model execution did not contain any nodes with control flow or dynamic output shapes. You can use model.execute() instead.");let y=o.filter(g=>!Ok(g)&&!wn(g.name,h,t)).map(g=>g.name);if(y.length>0){let g="";throw d!=null&&(g=`Alternatively, to avoid the dynamic ops, use model.execute() and specify the inputs [${p}]`),new Error(`Cannot compute the outputs [${y}] from the provided inputs [${r}]. Consider providing the following inputs: [${l}]. ${g}`)}return h}processStack(e,t,n,a,r,s,i,o,u){let l=[];for(;t.length>0;){let d=t.pop();n.currentContext=d.contexts;let p="";if(d.node.op==="Enter"&&I("isConstant",d.node,a,n)&&([p]=kr(d.node.name,n)),a[d.node.name]==null){let c=$k(d.node,a,n,this._resourceManager);p||([p]=kr(d.node.name,n));let h=n.currentContext;k.isPromise(c)?l.push(c.then(m=>(a[p]=m,n.currentContext=h,this.checkTensorForDisposal(p,d.node,a,n,s,i,o),this.processChildNodes(d.node,t,n,a,r,u),m))):(a[p]=c,this.checkTensorForDisposal(p,d.node,a,n,s,i,o),this.processChildNodes(d.node,t,n,a,r,u))}else this.processChildNodes(d.node,t,n,a,r,u)}return l}processChildNodes(e,t,n,a,r,s){e.children.forEach(i=>{let[o]=kr(i.name,n);r[o]||!s.has(i.name)||(i.op==="Merge"?i.inputNames.some(u=>!!wn(u,a,n))&&(r[o]=!0,t.push({contexts:n.currentContext,node:i})):i.inputNames.every(u=>!!wn(u,a,n))&&(r[o]=!0,t.push({contexts:n.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 n=e[t],[a]=qn(t),r=this.graph.nodes[a];if(r.attrParams.shape&&r.attrParams.shape.value){let s=r.attrParams.shape.value,i=s.length===n.shape.length&&n.shape.every((o,u)=>s[u]===-1||s[u]===o);k.assert(i,()=>`The shape of dict['${r.name}'] provided in model.execute(dict) must be [${s}], but was [${n.shape}]`)}r.attrParams.dtype&&r.attrParams.dtype.value&&k.assert(n.dtype===r.attrParams.dtype.value,()=>`The dtype of dict['${r.name}'] provided in model.execute(dict) must be ${r.attrParams.dtype.value}, but was ${n.dtype}`)})}mapInputs(e){let t={};for(let n in e)if(this._signature!=null&&this._signature.inputs!=null&&this._signature.inputs[n]!=null){let a=this._signature.inputs[n];t[a.name]=e[n]}else t[n]=e[n];return t}checkInputs(e){let t=Object.keys(e).filter(n=>{let[a]=qn(n);return this.graph.nodes[a]==null});if(t.length>0)throw new Error(`The dict provided in model.execute(dict) has keys: [${t}] that are not part of graph`)}mapOutputs(e){return e.map(t=>this._signature!=null&&this._signature.outputs!=null&&this._signature.outputs[t]!=null?this._signature.outputs[t].name:t,{})}checkOutputs(e){e.forEach(t=>{let[n]=qn(t);if(!this.graph.nodes[n])throw new Error(`The output '${t}' is not found in the graph`)})}},$oe=class{constructor(e={},t={}){this.hashTableNameToHandle=e,this.hashTableMap=t}addHashTable(e,t){this.hashTableNameToHandle[e]=t.handle,this.hashTableMap[t.id]=t}getHashTableHandleByName(e){return this.hashTableNameToHandle[e]}getHashTableById(e){return this.hashTableMap[e]}dispose(){for(let e in this.hashTableMap)this.hashTableMap[e].clearAndClose(),delete this.hashTableMap[e];for(let e in this.hashTableNameToHandle)this.hashTableNameToHandle[e].dispose(),delete this.hashTableNameToHandle[e]}},Doe="?tfjs-format=file",_oe="model.json",zk=class{constructor(e,t={}){this.modelUrl=e,this.loadOptions=t,this.version="n/a",t==null&&(this.loadOptions={}),this.resourceManager=new $oe}get modelVersion(){return this.version}get inputNodes(){return this.executor.inputNodes}get outputNodes(){return this.executor.outputNodes}get inputs(){return this.executor.inputs}get outputs(){return this.executor.outputs}get weights(){return this.executor.weightMap}get metadata(){return this.artifacts.userDefinedMetadata}get modelSignature(){return this.signature}findIOHandler(){let e=this.modelUrl;if(e.load!=null)this.handler=e;else if(this.loadOptions.requestInit!=null)this.handler=Cn.browserHTTPRequest(e,this.loadOptions);else{let t=Cn.getLoadHandlers(e,this.loadOptions);if(t.length===0)t.push(Cn.browserHTTPRequest(e,this.loadOptions));else if(t.length>1)throw new Error(`Found more than one (${t.length}) load handlers for URL '${[e]}'`);this.handler=t[0]}}async load(){if(this.findIOHandler(),this.handler.load==null)throw new Error("Cannot proceed with model loading because the IOHandler provided does not have the `load` method implemented.");let e=await this.handler.load();return this.loadSync(e)}loadSync(e){this.artifacts=e;let t=this.artifacts.modelTopology,n;this.artifacts.userDefinedMetadata!=null&&this.artifacts.userDefinedMetadata.signature!=null?n=this.artifacts.userDefinedMetadata.signature:n=this.artifacts.signature,this.signature=n,this.version=`${t.versions.producer}.${t.versions.minConsumer}`;let a=Cn.decodeWeights(this.artifacts.weightData,this.artifacts.weightSpecs);if(this.executor=new r2(Tk.Instance.transformGraph(t,this.signature)),this.executor.weightMap=this.convertTensorMapToTensorsMap(a),this.executor.resourceManager=this.resourceManager,e.modelInitializer!=null&&e.modelInitializer.node!=null){let r=Tk.Instance.transformGraph(e.modelInitializer);this.initializer=new r2(r),this.initializer.weightMap=this.executor.weightMap,this.initializer.resourceManager=this.resourceManager,this.initializer.executeAsync({},[])}return!0}async save(e,t){if(typeof e=="string"){let n=Cn.getSaveHandlers(e);if(n.length===0)throw new Error(`Cannot find any save handlers for URL '${e}'`);if(n.length>1)throw new Error(`Found more than one (${n.length}) save handlers for URL '${e}'`);e=n[0]}if(e.save==null)throw new Error("GraphModel.save() cannot proceed because the IOHandler provided does not have the `save` attribute defined.");return e.save(this.artifacts)}predict(e,t){return this.execute(e,this.outputNodes)}normalizeInputs(e){if(!(e instanceof We)&&!Array.isArray(e))return e;if(e=Array.isArray(e)?e:[e],e.length!==this.inputNodes.length)throw new Error(`Input tensor count mismatch,the graph model has ${this.inputNodes.length} placeholders, while there are ${e.length} input tensors.`);return this.inputNodes.reduce((t,n,a)=>(t[n]=e[a],t),{})}normalizeOutputs(e){return e=e||this.outputNodes,Array.isArray(e)?e:[e]}execute(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=this.executor.execute(e,t);return n.length>1?n:n[0]}async executeAsync(e,t){e=this.normalizeInputs(e),t=this.normalizeOutputs(t);let n=await this.executor.executeAsync(e,t);return n.length>1?n:n[0]}convertTensorMapToTensorsMap(e){return Object.keys(e).reduce((t,n)=>(t[n]=[e[n]],t),{})}dispose(){this.executor.dispose(),this.initializer&&this.initializer.dispose(),this.resourceManager.dispose()}};async function bt(e,t={}){if(e==null)throw new Error("modelUrl in loadGraphModel() cannot be null. Please provide a url or an IOHandler that loads the model");t==null&&(t={}),t.fromTFHub&&e.load==null&&(e.endsWith("/")||(e=e+"/"),e=`${e}${_oe}${Doe}`);let n=new zk(e,t);return await n.load(),n}var Ooe="3.7.0",Pk={};Fe(Pk,{CSVDataset:()=>Zk,Dataset:()=>iu,FileDataSource:()=>a9,TextLineDataset:()=>qk,URLDataSource:()=>r9,array:()=>sle,csv:()=>Ale,func:()=>yle,generator:()=>gle,microphone:()=>ble,version_data:()=>vle,webcam:()=>xle,zip:()=>ile});var zoe=ms(F5()),Poe=ms(F5());function Loe(e,t){return O0(e,t)}function O0(e,t,n=new Map,a=new Set){if(e==null)return null;if(a.has(e))throw new Error("Circular references are not supported.");if(n.has(e))return n.get(e);let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep map function may not return both a value and recurse=true.");if(r.recurse)if(su(e)){let s=Array.isArray(e)?[]:{};a.add(e);for(let i in e){let o=e[i],u=O0(o,t,n,a);s[i]=u}return a.delete(e),s}else throw new Error(`Can't recurse into non-iterable type: ${e}`);else return n.set(e,r.value),r.value}function Woe(e,t=Wk){return Lk(e,t)}function Lk(e,t,n=new Set){let a=e[0];if(n.has(a))throw new Error("Circular references are not supported.");let r=t(e);if(r.recurse&&r.value!==null)throw new Error("A deep zip function may not return both a value and recurse=true.");if(r.recurse)if(su(a)){let s=Array.isArray(a)?[]:{};n.add(a);for(let i in a){let o=e.map(l=>l[i]),u=Lk(o,t,n);s[i]=u}return n.delete(a),s}else throw new Error(`Can't recurse into non-iterable type: ${a}`);else return r.value}function Wk(e){return e===null?null:su(e[0])?{value:null,recurse:!0}:{value:e,recurse:!1}}async function Bk(e,t){let n=new Map;O0(e,t,n);for(let a of Array.from(n.keys())){let r=n.get(a);if(k.isPromise(r)){let s=await r;n.set(a,s)}}return O0(e,t,n)}function su(e){return e!=null&&!ArrayBuffer.isView(e)&&(Array.isArray(e)||typeof e=="object"&&!(e instanceof We))}function Boe(e){return e==null||Voe(e)||Array.isArray(e)||typeof e=="object"&&e instanceof We||k.isTypedArray(e)}function Voe(e){return e===null||typeof e!="object"&&typeof e!="function"}function joe(e){return Loe(e,Uoe)}function Uoe(e){return e instanceof We?{value:e.clone(),recurse:!1}:su(e)?{value:null,recurse:!0}:{value:e,recurse:!1}}var Vk=class{constructor(e){if(this.capacity=e,this.begin=0,this.end=0,e==null)throw new RangeError("Can't create a ring buffer of unknown capacity.");if(e<1)throw new RangeError("Can't create ring buffer of capacity < 1.");this.data=new Array(e),this.doubledCapacity=2*e}wrap(e){for(;e<0;)e+=this.doubledCapacity;return e%this.doubledCapacity}get(e){if(e<0)throw new RangeError("Can't get item at a negative index.");return this.data[e%this.capacity]}set(e,t){if(e<0)throw new RangeError("Can't set item at a negative index.");this.data[e%this.capacity]=t}length(){let e=this.end-this.begin;return e<0&&(e=this.doubledCapacity+e),e}isFull(){return this.length()===this.capacity}isEmpty(){return this.length()===0}push(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.set(this.end,e),this.end=this.wrap(this.end+1)}pushAll(e){for(let t of e)this.push(t)}pop(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");this.end=this.wrap(this.end-1);let e=this.get(this.end);return this.set(this.end,void 0),e}unshift(e){if(this.isFull())throw new RangeError("Ring buffer is full.");this.begin=this.wrap(this.begin-1),this.set(this.begin,e)}shift(){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let e=this.get(this.begin);return this.set(this.begin,void 0),this.begin=this.wrap(this.begin+1),e}shuffleExcise(e){if(this.isEmpty())throw new RangeError("Ring buffer is empty.");let t=this.wrap(this.begin+e),n=this.get(t);return this.set(t,this.pop()),n}},s2=class extends Vk{constructor(){super(s2.INITIAL_CAPACITY)}isFull(){return!1}push(e){super.isFull()&&this.expand(),super.push(e)}unshift(e){super.isFull()&&this.expand(),super.unshift(e)}expand(){let e=this.capacity*2,t=new Array(e),n=this.length();for(let a=0;a<n;a++)t[a]=this.get(this.wrap(this.begin+a));this.data=t,this.capacity=e,this.doubledCapacity=2*this.capacity,this.begin=0,this.end=n}};s2.INITIAL_CAPACITY=32;function jk(e){return new qoe(e)}function i2(e){return new Xoe(e)}function Hoe(e,t){return new Hk(e,t)}function Goe(e,t=is.FAIL){return new ale(e,t)}var Jt=class{async toArray(){let e=[],t=await this.next();for(;!t.done;)e.push(t.value),t=await this.next();return e}async toArrayForTest(){let e=this.prefetch(100),t=[],n=await e.next();for(;!n.done;)t.push(n.value),n=await e.next();return t}async resolveFully(){let e=await this.next();for(;!e.done;)e=await this.next()}async resolveWhile(e){let t=await this.next(),n=e(t.value);for(;!t.done&&n;)t=await this.next(),n=e(t.value)}handleErrors(e){return new tle(this,e)}filter(e){return new Qoe(this,e)}map(e){return new ele(this,e)}mapAsync(e){return new Uk(this,e)}serialMapAsync(e){return new Uk(this,e).serial()}flatmap(e){return new nle(this,e)}async forEachAsync(e){return this.map(e).resolveFully()}async serialForEach(e){return this.serialMapAsync(e).resolveWhile(t=>t===!0)}rowMajorBatch(e,t=!0){return new Joe(this,e,t)}columnMajorBatch(e,t=!0,n=Wk){return this.rowMajorBatch(e,t).map(a=>Woe(a,n))}concatenate(e,t){return new Hk(jk([this,e]),t)}take(e){return e<0||e==null?this:new Yoe(this,e)}skip(e){return e<0||e==null?this:new Zoe(this,e)}prefetch(e){return new Gk(this,e)}shuffle(e,t){return new rle(this,e,t)}serial(){return new Koe(this)}},qoe=class extends Jt{constructor(e){super();this.items=e,this.trav=0}summary(){return`Array of ${this.items.length} items`}async next(){if(this.trav>=this.items.length)return{value:null,done:!0};let e=this.items[this.trav];return this.trav++,{value:joe(e),done:!1}}},Xoe=class extends Jt{constructor(e){super();this.nextFn=e}summary(){return"Function call"}async next(){try{return this.nextFn()}catch(e){throw e.message=`Error thrown while iterating through a dataset: 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this.count++>=this.maxCount?{value:null,done:!0}:this.upstream.next()}},Joe=class extends Jt{constructor(e,t,n=!0){super();this.upstream=e,this.batchSize=t,this.enableSmallLastBatch=n,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> RowMajorBatch`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){let e=[];for(;e.length<this.batchSize;){let t=await this.upstream.next();if(t.done)return this.enableSmallLastBatch&&e.length>0?{value:e,done:!1}:{value:null,done:!0};e.push(t.value)}return{value:e,done:!1}}},Qoe=class extends Jt{constructor(e,t){super();this.upstream=e,this.predicate=t,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> Filter`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;){let e=await this.upstream.next();if(e.done||this.predicate(e.value))return e;Ie(e.value)}}},ele=class extends Jt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Map`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ka.getTensorsInContainer(e.value),n=this.transform(e.value),a=ka.getTensorsInContainer(n);for(let r of t)ka.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},tle=class extends Jt{constructor(e,t){super();this.upstream=e,this.handler=t,this.count=0,this.lastRead=Promise.resolve({value:null,done:!1})}summary(){return`${this.upstream.summary()} -> handleErrors`}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;;)try{return await this.upstream.next()}catch(e){if(!this.handler(e))return{value:null,done:!0}}}},Uk=class extends Jt{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> AsyncMap`}async next(){let e=await this.upstream.next();if(e.done)return{value:null,done:!0};let t=ka.getTensorsInContainer(e.value),n=await this.transform(e.value),a=ka.getTensorsInContainer(n);for(let r of t)ka.isTensorInList(r,a)||r.dispose();return{value:n,done:!1}}},o2=class extends Jt{constructor(){super();this.outputQueue=new s2,this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}async serialNext(){for(;this.outputQueue.length()===0;)if(!await this.pump())return{value:null,done:!0};return{value:this.outputQueue.shift(),done:!1}}},nle=class extends o2{constructor(e,t){super();this.upstream=e,this.transform=t}summary(){return`${this.upstream.summary()} -> Flatmap`}async pump(){let e=await this.upstream.next();if(e.done)return!1;let t=ka.getTensorsInContainer(e.value),n=this.transform(e.value),a=ka.getTensorsInContainer(n);this.outputQueue.pushAll(n);for(let r of t)ka.isTensorInList(r,a)||r.dispose();return!0}},Hk=class extends Jt{constructor(e,t){super();this.baseErrorHandler=t,this.lastRead=null,this.iterator=null,this.moreIterators=e}summary(){return"TODO: fill in upstream of chained summaries -> Chained"}async next(){return this.lastRead=this.readFromChain(this.lastRead),this.lastRead}async readFromChain(e){if(await e,this.iterator==null){let n=await this.moreIterators.next();if(n.done)return{value:null,done:!0};this.iterator=n.value,this.baseErrorHandler!=null&&(this.iterator=this.iterator.handleErrors(this.baseErrorHandler))}let t=await this.iterator.next();return t.done?(this.iterator=null,this.readFromChain(e)):t}},is;(function(e){e[e.FAIL=0]="FAIL",e[e.SHORTEST=1]="SHORTEST",e[e.LONGEST=2]="LONGEST"})(is||(is={}));var ale=class extends Jt{constructor(e,t=is.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,n=0;function a(s){return s instanceof Jt?{value:s.next().then(i=>(t++,i.done&&n++,i.value)),recurse:!1}:{value:null,recurse:!0}}let r=await Bk(this.iterators,a);if(t===n)return{value:null,done:!0};if(n>0)switch(this.mismatchMode){case is.FAIL:throw new Error(`Zipped streams should have the same length. Mismatched at element ${this.count}.`);case is.SHORTEST:return{value:null,done:!0};case is.LONGEST:default:}return this.count++,{value:r,done:!1}}async next(){return this.currentPromise=this.nextState(this.currentPromise),this.currentPromise}},Gk=class extends Jt{constructor(e,t){super();this.upstream=e,this.bufferSize=t,this.buffer=new Vk(t)}summary(){return`${this.upstream.summary()} -> Prefetch`}refill(){for(;!this.buffer.isFull();){let e=this.upstream.next();this.buffer.push(e)}}next(){return this.refill(),this.buffer.shift()}},rle=class extends Gk{constructor(e,t,n){super(e,t);this.upstream=e,this.windowSize=t,this.upstreamExhausted=!1,this.random=Poe.alea(n||k.now().toString()),this.lastRead=Promise.resolve({value:null,done:!1})}async next(){return this.lastRead=this.lastRead.then(()=>this.serialNext()),this.lastRead}randomInt(e){return Math.floor(this.random()*e)}chooseIndex(){return this.randomInt(this.buffer.length())}async serialNext(){for(this.upstreamExhausted||this.refill();!this.buffer.isEmpty();){let e=this.chooseIndex(),t=await this.buffer.shuffleExcise(e);if(t.done)this.upstreamExhausted=!0;else return this.refill(),t}return{value:null,done:!0}}},iu=class{constructor(){this.size=null}batch(e,t=!0){let n=this;k.assert(e>0,()=>`batchSize needs to be positive, but it is
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${e}`);let a;return this.size===Infinity||this.size==null?a=this.size:t?a=Math.ceil(this.size/e):a=Math.floor(this.size/e),Xn(async()=>(await n.iterator()).columnMajorBatch(e,t,ole),a)}concatenate(e){let t=this,n;return this.size===Infinity||e.size===Infinity?n=Infinity:this.size!=null&&e.size!=null?n=this.size+e.size:n=null,Xn(async()=>(await t.iterator()).concatenate(await e.iterator()),n)}filter(e){let t=this,n;return this.size===Infinity?n=Infinity:n=null,Xn(async()=>(await t.iterator()).filter(a=>V(()=>e(a))),n)}async forEachAsync(e){return(await this.iterator()).forEachAsync(e)}map(e){let t=this;return Xn(async()=>(await t.iterator()).map(n=>V(()=>e(n))),this.size)}mapAsync(e){let t=this;return Xn(async()=>(await t.iterator()).mapAsync(e),this.size)}prefetch(e){if(e==null)throw new RangeError("`Dataset.prefetch()` requires bufferSize to be specified.");let t=this;return Xn(async()=>(await t.iterator()).prefetch(e),this.size)}repeat(e){let t=this,n;return this.size!=null&&e>0?n=this.size*e:e===0?n=0:this.size!=null&&(e===void 0||e<0)?n=Infinity:n=null,Xn(async()=>{let a=i2(async()=>({value:await t.iterator(),done:!1}));return Hoe(a.take(e))},n)}skip(e){let t=this,n;return this.size!=null&&e>=0&&this.size>=e?n=this.size-e:this.size!=null&&(this.size<e||e===void 0||e<0)?n=0:n=null,Xn(async()=>(await t.iterator()).skip(e),n)}shuffle(e,t,n=!0){if(e==null||e<0)throw this.size==null?new RangeError("`Dataset.shuffle()` requires bufferSize to be specified."):new RangeError(`\`Dataset.shuffle()\` requires bufferSize to be specified. If your data fits in main memory (for regular JS objects), and/or GPU memory (for \`tf.Tensor\`s), consider setting bufferSize to the dataset size (${this.size} elements)`);let a=this,r=zoe.alea(t||k.now().toString());return Xn(async()=>{let s=r.int32();return n&&(s+=r.int32()),(await a.iterator()).shuffle(e,s.toString())},this.size)}take(e){let t=this,n;return this.size!=null&&this.size>e?n=e:this.size!=null&&this.size<=e?n=this.size:n=null,Xn(async()=>(await t.iterator()).take(e),n)}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()}};iu.MAX_BUFFER_SIZE=1e4;function Xn(e,t=null){return new class extends iu{constructor(){super(...arguments);this.size=t}async iterator(){return e()}}}function sle(e){return Xn(async()=>jk(e),e.length)}function ile(e){if(!su(e))throw new Error("The argument to zip() must be an object or array.");let t;if(Array.isArray(e))for(let n=0;n<e.length;n++)t=t==null?e[n].size:Math.min(t,e[n].size);else if(e instanceof Object)for(let n in e)t=t==null?e[n].size:Math.min(t,e[n].size);return Xn(async()=>{let n=await Bk(e,a=>{if(a instanceof iu)return{value:a.iterator(),recurse:!1};if(su(a))return{value:null,recurse:!0};throw new Error("Leaves of the structure passed to zip() must be Datasets, not primitives.")});return Goe(n,is.SHORTEST)},t)}function ole(e){if(e===null)return null;let t=e[0];return Boe(t)?{value:lle(e),recurse:!1}:{value:null,recurse:!0}}function lle(e){if(e.length===0)throw new Error("Can't make a batch of zero elements.");return e[0]instanceof We?mn(e):on(e)}var qk=class extends iu{constructor(e){super();this.input=e}async iterator(){return(await this.input.iterator()).decodeUTF8().split(`
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|
`).map(e=>(e.endsWith("\r")&&(e=e.slice(0,-1)),e))}},z0='"',lp=Symbol("out"),Xk=Symbol("field"),P0=Symbol("quote"),l2=Symbol("quoteafterquote"),Kk=Symbol("quoteinquote"),Zk=class extends iu{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 qk(e),t||(t={}),this.hasHeader=t.hasHeader!==!1,this.fullColumnNames=t.columnNames,this.columnConfigs=t.columnConfigs,this.configuredColumnsOnly=t.configuredColumnsOnly,t.delimWhitespace?(k.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&&k.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((a,r)=>(a[r]=a[r]+1||1,a),{}),n=Object.keys(t).filter(a=>t[a]>1);if(k.assert(n.length===0,()=>"Duplicate column names found: "+n.toString()),this.columnConfigs){for(let a of Object.keys(this.columnConfigs))if(this.fullColumnNames.indexOf(a)===-1)throw new Error('The key "'+a+'" provided in columnConfigs does not match any of the column names ('+this.fullColumnNames.toString()+").")}this.columnNamesValidated=!0}async maybeReadHeaderLine(){if(this.hasHeader){let e=await(await this.base.iterator()).next();if(e.done)throw new Error("No data was found for CSV parsing.");let t=e.value;return this.parseRow(t,!1)}else return null}async iterator(){this.columnNamesValidated||await this.setColumnNames();let e=await this.base.iterator();return this.hasHeader&&(e=e.skip(1)),e.map(t=>this.makeDataElement(t))}makeDataElement(e){let t=this.parseRow(e),n={},a={};for(let r=0;r<this.fullColumnNames.length;r++){let s=this.fullColumnNames[r],i=this.columnConfigs?this.columnConfigs[s]:null;if(!(this.configuredColumnsOnly&&!i)){let o=t[r],u=null;if(o==="")if(i&&i.default!==void 0)u=i.default;else{if(i&&(i.required||i.isLabel))throw new Error(`Required column ${s} is empty in this line: ${e}`);u=void 0}else{let l=Number(o);if(isNaN(l))i&&i.dtype==="bool"?u=this.getBoolean(o):u=o;else if(!i||!i.dtype)u=l;else switch(i.dtype){case"float32":u=l;break;case"int32":u=Math.floor(l);break;case"bool":u=this.getBoolean(o);break;default:u=l}}i&&i.isLabel?a[s]=u:n[s]=u}}return Object.keys(a).length===0?n:{xs:n,ys:a}}getBoolean(e){return e==="1"||e.toLowerCase()==="true"?1:0}parseRow(e,t=!0){let n=[],a=0,r=e.length,s=lp;for(let i=0;i<r;i++)switch(s){case lp:switch(e.charAt(i)){case z0:a=i+1,s=P0;break;case this.delimiter:if(a=i+1,this.delimiter===" "&&this.delimWhitespace)break;n.push(""),s=lp;break;default:s=Xk,a=i;break}break;case Xk:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i)),s=lp,a=i+1;break;default:}break;case P0:switch(e.charAt(i)){case z0:s=l2;break;default:}break;case l2:switch(e.charAt(i)){case this.delimiter:n.push(e.substring(a,i-1)),s=lp,a=i+1;break;case z0:s=P0;break;default:s=Kk;break}break;case Kk:switch(e.charAt(i)){case z0:s=P0;break;default:}break;default:}if(s===l2?n.push(e.substring(a,r-1)):n.push(e.substring(a)),t&&n.length!==this.fullColumnNames.length)throw new Error(`Invalid row in csv file. Should have ${this.fullColumnNames.length} elements in a row, but got ${n}`);return n}},Yk=class extends Jt{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(te().get("IS_NODE"))throw new Error("microphone API is only supported in browser environment.");let t=new Yk(e);return await t.start(),t}async start(){try{this.stream=await navigator.mediaDevices.getUserMedia({audio:this.audioTrackConstraints==null?!0:this.audioTrackConstraints,video:!1})}catch(n){throw new Error(`Error thrown while initializing video stream: ${n.message}`)}if(!this.stream)throw new Error("Could not obtain audio from microphone.");let e=window.AudioContext||window.webkitAudioContext;if(this.audioContext=new e,!this.sampleRateHz)this.sampleRateHz=this.audioContext.sampleRate;else if(this.audioContext.sampleRate!==this.sampleRateHz)throw new Error(`Mismatch in sampling rate: Expected: ${this.sampleRateHz}; Actual: ${this.audioContext.sampleRate}`);let t=this.audioContext.createMediaStreamSource(this.stream);this.analyser=this.audioContext.createAnalyser(),this.analyser.fftSize=this.fftSize*2,this.analyser.smoothingTimeConstant=this.smoothingTimeConstant,t.connect(this.analyser),this.freqData=new Float32Array(this.fftSize),this.timeData=new Float32Array(this.fftSize)}async next(){if(this.isClosed)return{value:null,done:!0};let e,t,n=await this.getAudioData();if(this.includeSpectrogram){let a=this.flattenQueue(n.freqDataQueue);e=this.getTensorFromAudioDataArray(a,[this.numFrames,this.columnTruncateLength,1])}if(this.includeWaveform){let a=this.flattenQueue(n.timeDataQueue);t=this.getTensorFromAudioDataArray(a,[this.numFrames*this.fftSize,1])}return{value:{spectrogram:e,waveform:t},done:!1}}async capture(){return(await this.next()).value}async getAudioData(){let e=[],t=[],n=0;return new Promise(a=>{let r=setInterval(()=>{this.includeSpectrogram&&(this.analyser.getFloatFrequencyData(this.freqData),this.freqData[0]===-Infinity&&a({freqDataQueue:e,timeDataQueue:t}),e.push(this.freqData.slice(0,this.columnTruncateLength))),this.includeWaveform&&(this.analyser.getFloatTimeDomainData(this.timeData),t.push(this.timeData.slice())),++n===this.numFrames&&(clearInterval(r),a({freqDataQueue:e,timeDataQueue:t}))},this.fftSize/this.sampleRateHz*1e3)})}stop(){this.isClosed||(this.isClosed=!0,this.analyser.disconnect(),this.audioContext.close(),this.stream!=null&&this.stream.getTracks().length>0&&this.stream.getTracks()[0].stop())}toArray(){throw new Error("Can not convert infinite audio stream to array.")}getSampleRate(){return this.sampleRateHz}flattenQueue(e){let t=e[0].length,n=new Float32Array(e.length*t);return e.forEach((a,r)=>n.set(a,r*t)),n}getTensorFromAudioDataArray(e,t){let n=new Float32Array(k.sizeFromShape(t));return n.set(e,n.length-e.length),on(n,t)}},Jk=class extends Jt{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=Dt([0],"int32"),this.webcamConfig.centerCrop){let n=this.webcamConfig.resizeWidth*1/this.webcamVideoElement.width,a=this.webcamConfig.resizeHeight*1/this.webcamVideoElement.height,r=(1-n)/2,s=(1-a)/2,i=r+n,o=a+s;this.cropBox=Sa([s,r,o,i],[1,4])}else this.cropBox=Sa([0,0,1,1],[1,4])}summary(){return"webcam"}static async create(e,t={}){if(te().get("IS_NODE"))throw new Error("tf.data.webcam is only supported in browser environment.");if(!e){if(e=document.createElement("video"),!t.resizeWidth||!t.resizeHeight)throw new Error("Please provide webcam video element, or resizeWidth and resizeHeight to create a hidden video element.");e.width=t.resizeWidth,e.height=t.resizeHeight}let n=new Jk(e,t);return await n.start(),n}async start(){this.webcamConfig.facingMode&&k.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=vi.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: ${t.message}`)}finally{e.dispose()}else return{value:e,done:!1}}needToResize(){return!!(this.webcamConfig.resizeWidth&&this.webcamConfig.resizeHeight&&(this.webcamVideoElement.width!==this.webcamConfig.resizeWidth||this.webcamVideoElement.height!==this.webcamConfig.resizeHeight))}cropAndResizeFrame(e){return V(()=>{let t=fn(me(e,"float32"),0),n;n=je.cropAndResize(t,this.cropBox,this.cropBoxInd,this.cropSize,"bilinear");let a=n.shape;return q(n,a.slice(1))})}async capture(){return(await this.next()).value}stop(){this.stream.getTracks().forEach(e=>e.stop());try{this.webcamVideoElement.srcObject=null}catch(e){console.log(e),this.webcamVideoElement.src=null}this.isClosed=!0}toArray(){throw new Error("Can not convert infinite video stream to array.")}},Qk=class{},e9=class extends Jt{split(e){return new ule(this,e)}},ule=class extends e9{constructor(e,t){super();this.upstream=e,this.impl=new dle(e,t)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},dle=class extends o2{constructor(e,t){super();this.upstream=e,this.separator=t,this.carryover=""}summary(){return`${this.upstream.summary()} -> Split('${this.separator}')`}async pump(){let e=await this.upstream.next();if(e.done)return this.carryover===""?!1:(this.outputQueue.push(this.carryover),this.carryover="",!0);let t=e.value.split(this.separator);t[0]=this.carryover+t[0];for(let n of t.slice(0,-1))this.outputQueue.push(n);return this.carryover=t[t.length-1],!0}},ple=class extends Jt{decodeUTF8(){return new cle(this)}},cle=class extends e9{constructor(e){super();this.upstream=e,this.impl=new hle(e)}summary(){return this.impl.summary()}async next(){return this.impl.next()}},hle=class extends o2{constructor(e){super();if(this.upstream=e,te().get("IS_BROWSER"))this.decoder=new TextDecoder("utf-8");else{let{StringDecoder:t}=xS();this.decoder=new t("utf8")}}summary(){return`${this.upstream.summary()} -> Utf8`}async pump(){let e=await this.upstream.next(),t;if(e.done)return!1;t=e.value;let n;return te().get("IS_BROWSER")?n=this.decoder.decode(t,{stream:!0}):n=this.decoder.write(Buffer.from(t.buffer)),this.outputQueue.push(n),!0}},t9=class extends ple{constructor(e,t={}){super();this.file=e,this.options=t,k.assert(e instanceof Uint8Array||(te().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((e,t)=>{let n=this.offset+this.chunkSize;if(this.file instanceof Uint8Array)e(new Uint8Array(this.file.slice(this.offset,n)));else{let a=new FileReader;a.onload=s=>{let i=a.result;if(i instanceof ArrayBuffer&&(i=new Uint8Array(i)),!(i instanceof Uint8Array))return t(new TypeError("FileReader returned unknown type."));e(i)},a.onabort=s=>t(new Error("Aborted")),a.onerror=s=>t(new Error(s.type));let r=this.file.slice(this.offset,n);a.readAsArrayBuffer(r)}this.offset=n}),done:!1}}};async function fle(e,t={}){let n,a;typeof e=="string"?n=e:(n=e.url,a=mle(e));let r=await k.fetch(n,a);if(r.ok){let s=new Uint8Array(await r.arrayBuffer());return new t9(s,t)}else throw new Error(r.statusText)}var mle=e=>({method:e.method,headers:e.headers,body:e.body,mode:e.mode,credentials:e.credentials,cache:e.cache,redirect:e.redirect,referrer:e.referrer,integrity:e.integrity});function n9(e){return typeof e=="string"&&e.substr(0,7)==="file://"}var a9=class extends Qk{constructor(e,t={}){super();this.input=e,this.options=t}async iterator(){if(n9(this.input)&&te().get("IS_NODE")){let e=po("fs");this.input=e.readFileSync(this.input.substr(7))}return new t9(this.input,this.options)}},r9=class extends Qk{constructor(e,t={}){super();this.url=e,this.fileOptions=t}async iterator(){return n9(this.url)?new a9(this.url,this.fileOptions).iterator():fle(this.url,this.fileOptions)}};function Ale(e,t={}){return new Zk(new r9(e),t)}function yle(e){let t=i2(e);return Xn(async()=>t)}function gle(e){return Xn(async()=>{let t=await e();return i2(()=>t.next())})}async function xle(e,t){return Jk.create(e,t)}async function ble(e){return Yk.create(e)}var vle="3.7.0",wle={tfjs:(zm==null?void 0:zm.version)||void 0,"tfjs-core":(Pm==null?void 0:Pm.version)||void 0,"tfjs-data":(Lm==null?void 0:Lm.version)||void 0,"tfjs-layers":(Wm==null?void 0:Wm.version)||void 0,"tfjs-converter":(Bm==null?void 0:Bm.version)||void 0,"tfjs-backend-cpu":Q7||void 0,"tfjs-backend-webgl":ww||void 0,"tfjs-backend-wasm":f4||void 0};var Kn={name:"humangl",priority:99,canvas:null,gl:null,width:1024,height:1024,webGLattr:{alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!1,desynchronized:!0}};function s9(){if(!I1(Kn.name)){pe("backend registration:",Kn.name);try{Kn.canvas=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Kn.width,Kn.height):document.createElement("canvas")}catch(e){pe("error: cannot create canvas:",e);return}try{Kn.gl=Kn.canvas.getContext("webgl2",Kn.webGLattr)}catch(e){pe("error: cannot get WebGL2 context:",e);return}try{Dh(2,Kn.gl)}catch(e){pe("error: cannot set WebGL2 context:",e);return}try{let e=new Bh(Kn.gl);kl(Kn.name,()=>new Xl(e),Kn.priority)}catch(e){pe("error: cannot register WebGL backend:",e);return}try{Al("webgl").forEach(t=>{let n={...t,backendName:Kn.name};mi(n)})}catch(e){pe("error: cannot update WebGL backend registration:",e);return}try{aa.set("WEBGL_VERSION",2)}catch(e){pe("error: cannot set WebGL backend flags:",e);return}pe("backend registered:",Kn.name)}}var g2={};Ba(g2,{load:()=>y2,predict:()=>A2,triangulation:()=>y9,uvmap:()=>g9});function i9(e,t){let n=[e.startPoint[0]*t[0],e.startPoint[1]*t[1]],a=[e.endPoint[0]*t[0],e.endPoint[1]*t[1]];return{startPoint:n,endPoint:a}}function dp(e){return[Math.abs(e.endPoint[0]-e.startPoint[0]),Math.abs(e.endPoint[1]-e.startPoint[1])]}function ou(e){return[e.startPoint[0]+(e.endPoint[0]-e.startPoint[0])/2,e.startPoint[1]+(e.endPoint[1]-e.startPoint[1])/2]}function lu(e,t,n){let a=t.shape[1],r=t.shape[2],s=[[e.startPoint[1]/a,e.startPoint[0]/r,e.endPoint[1]/a,e.endPoint[0]/r]];return je.cropAndResize(t,s,[0],n)}function L0(e,t=1.5){let n=ou(e),a=dp(e),r=[t*a[0]/2,t*a[1]/2],s=[n[0]-r[0],n[1]-r[1]],i=[n[0]+r[0],n[1]+r[1]];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function W0(e){let t=ou(e),n=dp(e),r=Math.max(...n)/2,s=[Math.round(t[0]-r),Math.round(t[1]-r)],i=[Math.round(t[0]+r),Math.round(t[1]+r)];return{startPoint:s,endPoint:i,landmarks:e.landmarks}}function u2(e){let t=e.map(s=>s[0]),n=e.map(s=>s[1]),a=[Math.min(...t),Math.min(...n)],r=[Math.max(...t),Math.max(...n)];return{startPoint:a,endPoint:r,landmarks:e}}var o9=e=>({startPoint:Re(e,[0,0],[-1,2]),endPoint:Re(e,[0,2],[-1,2])});var B0=[[1,0,0],[0,1,0],[0,0,1]];function kle(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function d2(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return kle(n)}function l9(e,t){return[[1,0,e],[0,1,t],[0,0,1]]}function os(e,t){let n=0;for(let a=0;a<e.length;a++)n+=e[a]*t[a];return n}function Ile(e,t){let n=[];for(let a=0;a<e.length;a++)n.push(e[a][t]);return n}function u9(e,t){let n=[],a=e.length;for(let r=0;r<a;r++){n.push([]);for(let s=0;s<a;s++)n[r].push(os(e[r],Ile(t,s)))}return n}function V0(e,t){let n=Math.cos(e),a=Math.sin(e),r=[[n,-a,0],[a,n,0],[0,0,1]],s=l9(t[0],t[1]),i=u9(s,r),o=l9(-t[0],-t[1]);return u9(i,o)}function d9(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],a=[-os(t[0],n),-os(t[1],n)];return[t[0].concat(a[0]),t[1].concat(a[1]),[0,0,1]]}function p9(e,t){return[os(e,t[0]),os(e,t[1])]}function c9(e){let t={strides:[e/16,e/8],anchors:[2,6]},n=[];for(let a=0;a<t.strides.length;a++){let r=t.strides[a],s=Math.floor((e+r-1)/r),i=Math.floor((e+r-1)/r),o=t.anchors[a];for(let u=0;u<s;u++){let l=r*(u+.5);for(let d=0;d<i;d++){let p=r*(d+.5);for(let c=0;c<o;c++)n.push([p,l])}}}return n}var h9=6;function Sle(e,t,n){let a=Re(e,[0,1],[-1,2]),r=ie(a,t),s=Re(e,[0,3],[-1,2]),i=fe(s,n),o=fe(r,n),u=fe(i,2),l=Ae(o,u),d=ie(o,u),p=B(l,n),c=B(d,n);return Nl([p,c],1)}var f9=class{constructor(t,n){this.model=t,this.anchorsData=c9(t.inputs[0].shape[1]),this.anchors=Sa(this.anchorsData),this.inputSize=t.inputs[0].shape[2],this.config=n}async getBoundingBoxes(t){if(!t||t.isDisposedInternal||t.shape.length!==4||t.shape[1]<1||t.shape[2]<1)return null;let[n,a,r]=V(()=>{let l=t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(.5),d=this.model.execute(l),p;if(Array.isArray(d)){let f=d.sort((x,v)=>x.size-v.size),A=lt([f[0],f[2]],2),y=lt([f[1],f[3]],2);p=lt([y,A],1).squeeze(0)}else p=d.squeeze();let c=Sle(p,this.anchors,[this.inputSize,this.inputSize]),h=Re(p,[0,0],[-1,1]),m=En(h).squeeze().dataSync();return[p,c,m]}),s=await je.nonMaxSuppressionAsync(a,r,this.config.face.detector.maxDetected,this.config.face.detector.iouThreshold,this.config.face.detector.minConfidence),i=s.arraySync();s.dispose();let o=[];for(let u=0;u<i.length;u++){let l=r[i[u]];if(l>this.config.face.detector.minConfidence){let d=Re(a,[i[u],0],[1,-1]),p=o9(d);d.dispose();let c=this.anchorsData[i[u]],h=V(()=>Re(n,[i[u],h9-1],[1,-1]).squeeze().reshape([h9,-1]));o.push({box:p,landmarks:h,anchor:c,confidence:l})}}return 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Nle=[127,234,132,58,172,150,149,148,152,377,378,379,397,288,361,454,356,70,63,105,66,107,336,296,334,293,300,168,6,195,4,98,97,2,326,327,33,160,158,133,153,144,362,385,387,263,373,380,57,40,37,0,267,270,287,321,314,17,84,91,78,81,13,311,308,402,14,178],Tle=[33,133,362,263,1,62,308,159,145,386,374,6,102,331,2,13,14,70,105,107,336,334,300,54,10,284,50,280,234,454,58,288,152],Cle=[33,133,362,263,1,78,308],yue=Nle.map(e=>pp[e]),gue=Tle.map(e=>pp[e]),xue=Cle.map(e=>pp[e]);var c2=ar.leftEyeLower0,h2=ar.rightEyeLower0,uu={leftBounds:[c2[0],c2[c2.length-1]],rightBounds:[h2[0],h2[h2.length-1]]},j0={count:468,mouth:13,symmetryLine:[13,ar.midwayBetweenEyes[0]]},A9={leftEye:0,rightEye:1,nose:2,mouth:3,leftEar:4,rightEar:5,symmetryLine:[3,2]},du={upperCenter:3,lowerCenter:4,index:71,numCoordinates:76};function U0(e,t,n,a){for(let r=0;r<p2.length;r++){let{key:s,indices:i}=p2[r],o=ar[`${n}${s}`];if(!a||a.includes(s))for(let u=0;u<i.length;u++){let l=i[u];e[o[u]]=[t[l][0],t[l][1],(t[l][2]+e[o[u]][2])/2]}}}var f2=class{constructor(t,n,a){var r,s;this.storedBoxes=[],this.boundingBoxDetector=t,this.meshDetector=n,this.irisModel=a,this.boxSize=((r=t==null?void 0:t.model)==null?void 0:r.inputs[0].shape[2])||0,this.meshSize=(n==null?void 0:n.inputs[0].shape[2])||((s=t==null?void 0:t.model)==null?void 0:s.inputs[0].shape[2]),this.irisSize=(a==null?void 0:a.inputs[0].shape[1])||0,this.irisEnlarge=2.3,this.skipped=0,this.detectedFaces=0}transformRawCoords(t,n,a,r){let s=dp({startPoint:n.startPoint,endPoint:n.endPoint}),i=t.map(p=>[s[0]/this.meshSize*(p[0]-this.meshSize/2),s[1]/this.meshSize*(p[1]-this.meshSize/2),p[2]]),o=a!==0?V0(a,[0,0]):B0,u=a!==0?i.map(p=>[...p9(p,o),p[2]]):i,l=a!==0?d9(r):B0,d=[...ou({startPoint:n.startPoint,endPoint:n.endPoint}),1];return u.map(p=>[Math.round(p[0]+os(d,l[0])),Math.round(p[1]+os(d,l[1])),Math.round(p[2])])}getLeftToRightEyeDepthDifference(t){let n=t[uu.leftBounds[0]][2],a=t[uu.rightBounds[0]][2];return n-a}getEyeBox(t,n,a,r,s=!1){let i=W0(L0(u2([t[a],t[r]]),this.irisEnlarge)),o=dp(i),u=je.cropAndResize(n,[[i.startPoint[1]/this.meshSize,i.startPoint[0]/this.meshSize,i.endPoint[1]/this.meshSize,i.endPoint[0]/this.meshSize]],[0],[this.irisSize,this.irisSize]);return s&&aa.flags.IS_BROWSER&&(u=je.flipLeftRight(u)),{box:i,boxSize:o,crop:u}}getEyeCoords(t,n,a,r=!1){let s=[];for(let i=0;i<du.numCoordinates;i++){let o=t[i*3],u=t[i*3+1],l=t[i*3+2];s.push([(r?1-o/this.irisSize:o/this.irisSize)*a[0]+n.startPoint[0],u/this.irisSize*a[1]+n.startPoint[1],l])}return{rawCoords:s,iris:s.slice(du.index)}}getAdjustedIrisCoords(t,n,a){let r=t[ar[`${a}EyeUpper0`][du.upperCenter]][2],s=t[ar[`${a}EyeLower0`][du.lowerCenter]][2],i=(r+s)/2;return n.map((o,u)=>{let l=i;return u===2?l=r:u===4&&(l=s),[o[0],o[1],l]})}async predict(t,n){let a=!1,r;if((this.skipped===0||this.skipped>n.face.detector.skipFrames||!n.face.mesh.enabled||!n.skipFrame)&&(r=await this.boundingBoxDetector.getBoundingBoxes(t),this.skipped=0),n.skipFrame&&this.skipped++,!n.skipFrame||r&&r.boxes&&(!n.face.mesh.enabled||r.boxes.length!==this.detectedFaces&&this.detectedFaces!==n.face.detector.maxDetected)){this.storedBoxes=[],this.detectedFaces=0;for(let i of r.boxes)this.storedBoxes.push({startPoint:i.box.startPoint.dataSync(),endPoint:i.box.endPoint.dataSync(),landmarks:i.landmarks.arraySync(),confidence:i.confidence});this.storedBoxes.length>0&&(a=!0)}if(a){if(!r||!r.boxes||r.boxes.length===0)return this.storedBoxes=[],this.detectedFaces=0,null;for(let i=0;i<this.storedBoxes.length;i++){let o=i9({startPoint:this.storedBoxes[i].startPoint,endPoint:this.storedBoxes[i].endPoint},r.scaleFactor),u=L0(o),l=W0(u),d=this.storedBoxes[i].landmarks,p=this.storedBoxes[i].confidence;this.storedBoxes[i]={...l,confidence:p,landmarks:d}}}r&&r.boxes&&r.boxes.forEach(i=>{i.box.startPoint.dispose(),i.box.endPoint.dispose(),i.landmarks.dispose()});let s=V(()=>this.storedBoxes.map((i,o)=>{let u,l=0,d;if(n.face.detector.rotation&&n.face.mesh.enabled&&aa.flags.IS_BROWSER){let[x,v]=i.landmarks.length>=j0.count?j0.symmetryLine:A9.symmetryLine;l=d2(i.landmarks[x],i.landmarks[v]);let b=ou({startPoint:i.startPoint,endPoint:i.endPoint}),w=[b[0]/t.shape[2],b[1]/t.shape[1]],N=je.rotateWithOffset(t,l,0,w);d=V0(-l,b),n.face.mesh.enabled?u=lu({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.meshSize,this.meshSize]).div(255):u=lu({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.boxSize,this.boxSize]).div(255)}else{d=B0;let x=t.clone();n.face.mesh.enabled?u=lu({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.meshSize,this.meshSize]).div(255):u=lu({startPoint:i.startPoint,endPoint:i.endPoint},x,[this.boxSize,this.boxSize]).div(255)}if(!n.face.mesh.enabled)return{mesh:[],box:i,faceConfidence:null,boxConfidence:i.confidence,confidence:i.confidence,image:u};let[,p,c]=this.meshDetector.execute(u),h=p.dataSync()[0];if(h<n.face.detector.minConfidence)return this.storedBoxes[o].confidence=h,null;let f=q(c,[-1,3]).arraySync();if(n.face.iris.enabled){let{box:x,boxSize:v,crop:b}=this.getEyeBox(f,u,uu.leftBounds[0],uu.leftBounds[1],!0),{box:w,boxSize:N,crop:C}=this.getEyeBox(f,u,uu.rightBounds[0],uu.rightBounds[1]),z=this.irisModel.predict(lt([b,C])).dataSync(),$=z.slice(0,du.numCoordinates*3),{rawCoords:S,iris:O}=this.getEyeCoords($,x,v,!0),_=z.slice(du.numCoordinates*3),{rawCoords:W,iris:G}=this.getEyeCoords(_,w,N),H=this.getLeftToRightEyeDepthDifference(f);Math.abs(H)<30?(U0(f,S,"left",null),U0(f,W,"right",null)):H<1?U0(f,S,"left",["EyeUpper0","EyeLower0"]):U0(f,W,"right",["EyeUpper0","EyeLower0"]);let J=this.getAdjustedIrisCoords(f,O,"left"),K=this.getAdjustedIrisCoords(f,G,"right");f=f.concat(J).concat(K)}let A=this.transformRawCoords(f,i,l,d),y=i.confidence;if(i=L0(u2(A),1.5),i.confidence=y,n.face.detector.rotation&&n.face.mesh.enabled&&n.face.description.enabled&&aa.flags.IS_BROWSER){let[x,v]=i.landmarks.length>=j0.count?j0.symmetryLine:A9.symmetryLine;l=d2(i.landmarks[x],i.landmarks[v]);let b=ou({startPoint:i.startPoint,endPoint:i.endPoint}),w=[b[0]/t.shape[2],b[1]/t.shape[1]],N=je.rotateWithOffset(t.toFloat(),l,0,w);d=V0(-l,b),u=lu({startPoint:i.startPoint,endPoint:i.endPoint},N,[this.meshSize,this.meshSize]).div(255)}let g={mesh:A,box:i,faceConfidence:h,boxConfidence:i.confidence,image:u};return this.storedBoxes[o]={...W0(i),confidence:i.confidence,faceConfidence:h},g}));return n.face.mesh.enabled&&(this.storedBoxes=this.storedBoxes.filter(i=>i.confidence>n.face.detector.minConfidence)),this.detectedFaces=s.length,s}};var Rt=[null,null,null],m2;async function A2(e,t){let n=await m2.predict(e,t),a=[],r=0;for(let s of n||[]){if(!s||s.isDisposedInternal)continue;let i=s.mesh.map(d=>[d[0]/(e.shape[2]||0),d[1]/(e.shape[1]||0),d[2]/m2.meshSize]),o={};if(s.mesh&&s.mesh.length>0)for(let d of Object.keys(ar))o[d]=ar[d].map(p=>s.mesh[p]);let u=s.box?[Math.trunc(Math.max(0,s.box.startPoint[0])),Math.trunc(Math.max(0,s.box.startPoint[1])),Math.trunc(Math.min(e.shape[2]||0,s.box.endPoint[0])-Math.max(0,s.box.startPoint[0])),Math.trunc(Math.min(e.shape[1]||0,s.box.endPoint[1])-Math.max(0,s.box.startPoint[1]))]:[0,0,0,0],l=s.box?[s.box.startPoint[0]/(e.shape[2]||0),s.box.startPoint[1]/(e.shape[1]||0),(s.box.endPoint[0]-s.box.startPoint[0])/(e.shape[2]||0),(s.box.endPoint[1]-s.box.startPoint[1])/(e.shape[1]||0)]:[0,0,0,0];a.push({id:r++,score:Math.round(100*s.faceConfidence||100*s.boxConfidence||0)/100,boxScore:Math.round(100*s.boxConfidence)/100,faceScore:Math.round(100*s.faceConfidence)/100,box:u,boxRaw:l,mesh:s.mesh,meshRaw:i,annotations:o,image:s.image,tensor:s.image}),s.coords&&s.coords.dispose()}return a}async function y2(e){return!Rt[0]&&e.face.enabled||!Rt[1]&&e.face.mesh.enabled||!Rt[2]&&e.face.iris.enabled?(Rt=await Promise.all([!Rt[0]&&e.face.enabled?m9(e):null,!Rt[1]&&e.face.mesh.enabled?bt(kt(e.modelBasePath,e.face.mesh.modelPath),{fromTFHub:e.face.mesh.modelPath.includes("tfhub.dev")}):null,!Rt[2]&&e.face.iris.enabled?bt(kt(e.modelBasePath,e.face.iris.modelPath),{fromTFHub:e.face.iris.modelPath.includes("tfhub.dev")}):null]),e.face.mesh.enabled&&(!Rt[1]||!Rt[1].modelUrl?pe("load model failed:",e.face.mesh.modelPath):e.debug&&pe("load model:",Rt[1].modelUrl)),e.face.iris.enabled&&(!Rt[2]||!Rt[2].modelUrl?pe("load model failed:",e.face.iris.modelPath):e.debug&&pe("load model:",Rt[2].modelUrl))):e.debug&&(Rt[0]&&pe("cached model:",Rt[0].model.modelUrl),Rt[1]&&pe("cached model:",Rt[1].modelUrl),Rt[2]&&pe("cached model:",Rt[2].modelUrl)),m2=new f2(Rt[0],Rt[1],Rt[2]),Rt}var y9=Zi,g9=pp;var w2={};Ba(w2,{load:()=>v2,predict:()=>G0});var Ele=["angry","disgust","fear","happy","sad","surprise","neutral"],Oa,H0=[],x9=0,x2=Number.MAX_SAFE_INTEGER,b2=[.2989,.587,.114];async function v2(e){return Oa?e.debug&&pe("cached model:",Oa.modelUrl):(Oa=await bt(kt(e.modelBasePath,e.face.emotion.modelPath)),!Oa||!Oa.modelUrl?pe("load model failed:",e.face.emotion.modelPath):e.debug&&pe("load model:",Oa.modelUrl)),Oa}async function G0(e,t,n,a){return Oa?x2<t.face.emotion.skipFrames&&t.skipFrame&&x9===a&&H0[n]&&H0[n].length>0?(x2++,H0[n]):(x2=0,new Promise(async r=>{let s=je.resizeBilinear(e,[Oa.inputs[0].shape[2],Oa.inputs[0].shape[1]],!1),[i,o,u]=Kt(s,3,3);s.dispose();let l=B(i,b2[0]),d=B(o,b2[1]),p=B(u,b2[2]);i.dispose(),o.dispose(),u.dispose();let c=Vc([l,d,p]);l.dispose(),d.dispose(),p.dispose();let h=V(()=>c.sub(.5).mul(2));c.dispose();let m=[];if(t.face.emotion.enabled){let f=await Oa.predict(h),A=f.dataSync();Ie(f);for(let y=0;y<A.length;y++)A[y]>t.face.emotion.minConfidence&&m.push({score:Math.min(.99,Math.trunc(100*A[y])/100),emotion:Ele[y]});m.sort((y,g)=>g.score-y.score)}h.dispose(),H0[n]=m,x9=a,r(m)})):null}var T2={};Ba(T2,{enhance:()=>N2,load:()=>I2,match:()=>v9,predict:()=>X0,similarity:()=>S2});var za,q0=[],b9=0,k2=Number.MAX_SAFE_INTEGER;async function I2(e){let t=kt(e.modelBasePath,e.face.description.modelPath);return za?e.debug&&pe("cached model:",t):(za=await bt(t),za?e.debug&&pe("load model:",t):pe("load model failed:",e.face.description.modelPath)),za}function S2(e,t,n=2){if(!e||!t||(e==null?void 0:e.length)===0||(t==null?void 0:t.length)===0||(e==null?void 0:e.length)!==(t==null?void 0:t.length))return 0;let a=5*e.map((s,i)=>Math.abs(e[i]-t[i])**n).reduce((s,i)=>s+i,0)**(1/n);return Math.max(0,100-a)/100}function v9(e,t,n=0){let a={similarity:0,name:"",source:"",embedding:[]};if(!e||!t||!Array.isArray(e)||!Array.isArray(t))return a;for(let r of t)if(r.embedding&&r.name){let s=S2(e,r.embedding);s>n&&s>a.similarity&&(a={...r,similarity:s})}return a}function N2(e){return V(()=>{let n=e.image||e.tensor||e;if(!(n instanceof We))return null;let a=[[.05,.15,.85,.85]];return za.inputs[0].shape?(n.shape.length===3?je.cropAndResize(fn(n,0),a,[0],[za.inputs[0].shape[2],za.inputs[0].shape[1]]):je.cropAndResize(n,a,[0],[za.inputs[0].shape[2],za.inputs[0].shape[1]])).mul(255):null})}async function X0(e,t,n,a){var r,s;return za?k2<t.face.description.skipFrames&&t.skipFrame&&b9===a&&((r=q0[n])==null?void 0:r.age)&&((s=q0[n])==null?void 0:s.age)>0?(k2++,q0[n]):(k2=0,new Promise(async i=>{let o=N2(e),u,l={age:0,gender:"unknown",genderScore:0,descriptor:[]};t.face.description.enabled&&(u=await za.predict(o)),Ie(o),u&&(V(()=>{let d=u.find(f=>f.shape[1]===1).dataSync(),p=Math.trunc(200*Math.abs(d[0]-.5))/100;p>t.face.description.minConfidence&&(l.gender=d[0]<=.5?"female":"male",l.genderScore=Math.min(.99,p));let c=u.find(f=>f.shape[1]===100).argMax(1).dataSync()[0],h=u.find(f=>f.shape[1]===100).dataSync();l.age=Math.round(h[c-1]>h[c+1]?10*c-100*h[c-1]:10*c+100*h[c+1])/10;let m=u.find(f=>f.shape[1]===1024);l.descriptor=[...m.dataSync()]}),u.forEach(d=>Ie(d))),q0[n]=l,b9=a,i(l)})):null}var Rle=e=>{let t=(p,c)=>Math.atan2(p[1]-c[1],p[0]-c[0]),n=[0,-.1],a=1,r=e.mesh[33][2]>e.mesh[263][2],s=r?e.mesh[473]:e.mesh[468],i=r?[(e.mesh[133][0]+e.mesh[33][0])/2,(e.mesh[133][1]+e.mesh[33][1])/2]:[(e.mesh[263][0]+e.mesh[362][0])/2,(e.mesh[263][1]+e.mesh[362][1])/2],o=r?[e.mesh[133][0]-e.mesh[33][0],e.mesh[23][1]-e.mesh[27][1]]:[e.mesh[263][0]-e.mesh[362][0],e.mesh[253][1]-e.mesh[257][1]],u=[(i[0]-s[0])/o[0]-n[0],a*(s[1]-i[1])/o[1]-n[1]],l=Math.sqrt(u[0]**2+u[1]**2);return l=Math.min(l,e.boxRaw[2]/2,e.boxRaw[3]/2),{bearing:(t([0,0],u)+Math.PI/2)%Math.PI,strength:l}},Mle=(e,t)=>{let n=A=>{let y=Math.sqrt(A[0]*A[0]+A[1]*A[1]+A[2]*A[2]);return A[0]/=y,A[1]/=y,A[2]/=y,A},a=(A,y)=>{let g=A[0]-y[0],x=A[1]-y[1],v=A[2]-y[2];return[g,x,v]},r=(A,y)=>{let g=A[1]*y[2]-A[2]*y[1],x=A[2]*y[0]-A[0]*y[2],v=A[0]*y[1]-A[1]*y[0];return[g,x,v]},s=A=>{let[y,g,x,v,b,w,N,C,E]=A,z,$,S;return v<1?v>-1?(S=Math.asin(v),$=Math.atan2(-N,y),z=Math.atan2(-w,b)):(S=-Math.PI/2,$=-Math.atan2(C,E),z=0):(S=Math.PI/2,$=Math.atan2(C,E),z=0),{pitch:2*-z,yaw:2*-$,roll:2*-S}},i=A=>{let y=(x,v,b,w)=>Math.atan2(w-v,b-x);return{pitch:y(A[10][1],A[10][2],A[152][1],A[152][2]),yaw:y(A[33][0],A[33][2],A[263][0],A[263][2]),roll:y(A[33][0],A[33][1],A[263][0],A[263][1])}},o=e.meshRaw;if(!o||o.length<300)return{angle:{pitch:0,yaw:0,roll:0},matrix:[1,0,0,0,1,0,0,0,1],gaze:{bearing:0,strength:0}};let u=Math.max(e.boxRaw[2]*t[0],e.boxRaw[3]*t[1])/1.5,l=[o[10],o[152],o[234],o[454]].map(A=>[A[0]*t[0]/u,A[1]*t[1]/u,A[2]]),d=n(a(l[1],l[0])),p=n(a(l[3],l[2])),c=n(r(p,d));p=r(d,c);let h=[p[0],p[1],p[2],d[0],d[1],d[2],c[0],c[1],c[2]],m=s(h),f=o.length===478?Rle(e):{bearing:0,strength:0};return{angle:m,matrix:h,gaze:f}},C2=async(e,t)=>{var d,p,c,h,m,f;let n,a,r,s,i,o,u=[];e.state="run:face",n=Je();let l=await A2(t,e.config);if(e.performance.face=Math.trunc(Je()-n),!t.shape||t.shape.length!==4)return[];if(!l)return[];for(let A=0;A<l.length;A++){if(e.analyze("Get Face"),!l[A].image||l[A].image.isDisposedInternal){pe("Face object is disposed:",l[A].image);continue}let y=Mle(l[A],[t.shape[2],t.shape[1]]);e.analyze("Start Emotion:"),e.config.async?s=e.config.face.emotion.enabled?G0(l[A].image||on([]),e.config,A,l.length):{}:(e.state="run:emotion",n=Je(),s=e.config.face.emotion.enabled?await G0(l[A].image||on([]),e.config,A,l.length):{},e.performance.emotion=Math.trunc(Je()-n)),e.analyze("End Emotion:"),e.analyze("Start Description:"),e.config.async?o=e.config.face.description.enabled?X0(l[A].image||on([]),e.config,A,l.length):[]:(e.state="run:description",n=Je(),o=e.config.face.description.enabled?await X0(l[A].image||on([]),e.config,A,l.length):[],e.performance.embedding=Math.trunc(Je()-n)),e.analyze("End Description:"),e.config.async&&([a,r,s,i,o]=await Promise.all([a,r,s,i,o])),e.analyze("Finish Face:"),!e.config.face.iris.enabled&&((p=(d=l[A])==null?void 0:d.annotations)==null?void 0:p.leftEyeIris)&&((h=(c=l[A])==null?void 0:c.annotations)==null?void 0:h.rightEyeIris)&&(delete l[A].annotations.leftEyeIris,delete l[A].annotations.rightEyeIris);let g=((m=l[A].annotations)==null?void 0:m.leftEyeIris)&&((f=l[A].annotations)==null?void 0:f.rightEyeIris)?Math.max(Math.abs(l[A].annotations.leftEyeIris[3][0]-l[A].annotations.leftEyeIris[1][0]),Math.abs(l[A].annotations.rightEyeIris[4][1]-l[A].annotations.rightEyeIris[2][1]))/t.shape[2]:0;u.push({...l[A],id:A,age:o.age,gender:o.gender,genderScore:o.genderScore,embedding:o.descriptor,emotion:s,iris:g!==0?Math.trunc(500/g/11.7)/100:0,rotation:y,tensor:e.config.face.detector.return?Aa(l[A].image):null}),Ie(l[A].image),l[A].image&&delete l[A].image,e.analyze("End Face")}return e.analyze("End FaceMesh:"),e.config.async&&(e.performance.face&&delete e.performance.face,e.performance.age&&delete e.performance.age,e.performance.gender&&delete e.performance.gender,e.performance.emotion&&delete e.performance.emotion),u};var O2={};Ba(O2,{load:()=>_2,predict:()=>D2});var cp=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"],w9=cp.length,hp=cp.reduce((e,t,n)=>(e[t]=n,e),{}),Fle=[["leftHip","leftShoulder"],["leftElbow","leftShoulder"],["leftElbow","leftWrist"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["rightHip","rightShoulder"],["rightElbow","rightShoulder"],["rightElbow","rightWrist"],["rightHip","rightKnee"],["rightKnee","rightAnkle"],["leftShoulder","rightShoulder"],["leftHip","rightHip"]],$le=Fle.map(([e,t])=>[hp[e],hp[t]]),k9=[["nose","leftEye"],["leftEye","leftEar"],["nose","rightEye"],["rightEye","rightEar"],["nose","leftShoulder"],["leftShoulder","leftElbow"],["leftElbow","leftWrist"],["leftShoulder","leftHip"],["leftHip","leftKnee"],["leftKnee","leftAnkle"],["nose","rightShoulder"],["rightShoulder","rightElbow"],["rightElbow","rightWrist"],["rightShoulder","rightHip"],["rightHip","rightKnee"],["rightKnee","rightAnkle"]];function 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je.nonMaxSuppressionAsync(u,i,n.hand.maxDetected,n.hand.iouThreshold,n.hand.minConfidence),d=l.arraySync();s.dispose(),l.dispose();let p=[];for(let c of d)if(i[c]>=n.hand.minConfidence){let h=Re(u,[c,0],[1,-1]),m=Re(r,[c,5],[1,14]),f=V(()=>this.normalizeLandmarks(m,c).reshape([-1,2]));m.dispose(),p.push({box:h,palmLandmarks:f,confidence:i[c]})}return r.dispose(),u.dispose(),p}async estimateHandBounds(t,n){let a=t.shape[1],r=t.shape[2],s=V(()=>t.resizeBilinear([this.inputSize,this.inputSize]).div(127.5).sub(1)),i=await this.getBoxes(s,n);s.dispose();let o=[];if(!i||i.length===0)return o;for(let u of i){let l=u.box.dataSync(),d=l.slice(0,2),p=l.slice(2,4),c=u.palmLandmarks.arraySync();u.box.dispose(),u.palmLandmarks.dispose(),o.push(M9({startPoint:d,endPoint:p,palmLandmarks:c,confidence:u.confidence},[r/this.inputSize,a/this.inputSize]))}return o}};function Wle(e){return e-2*Math.PI*Math.floor((e+Math.PI)/(2*Math.PI))}function $9(e,t){let n=Math.PI/2-Math.atan2(-(t[1]-e[1]),t[0]-e[0]);return Wle(n)}var D9=(e,t)=>[[1,0,e],[0,1,t],[0,0,1]];function ls(e,t){let n=0;for(let a=0;a<e.length;a++)n+=e[a]*t[a];return n}function Ble(e,t){let n=[];for(let a=0;a<e.length;a++)n.push(e[a][t]);return n}function _9(e,t){let n=[],a=e.length;for(let r=0;r<a;r++){n.push([]);for(let s=0;s<a;s++)n[r].push(ls(e[r],Ble(t,s)))}return n}function P2(e,t){let n=Math.cos(e),a=Math.sin(e),r=[[n,-a,0],[a,n,0],[0,0,1]],s=D9(t[0],t[1]),i=_9(s,r),o=D9(-t[0],-t[1]);return _9(i,o)}function O9(e){let t=[[e[0][0],e[1][0]],[e[0][1],e[1][1]]],n=[e[0][2],e[1][2]],a=[-ls(t[0],n),-ls(t[1],n)];return[t[0].concat(a[0]),t[1].concat(a[1]),[0,0,1]]}function L2(e,t){return[ls(e,t[0]),ls(e,t[1])]}var Vle=5,z9=1.65,P9=[0,5,9,13,17,1,2],jle=0,Ule=2,W2=class{constructor(t,n){var a;this.handDetector=t,this.handPoseModel=n,this.inputSize=(a=this.handPoseModel)==null?void 0:a.inputs[0].shape[2],this.storedBoxes=[],this.skipped=0,this.detectedHands=0}calculateLandmarksBoundingBox(t){let n=t.map(i=>i[0]),a=t.map(i=>i[1]),r=[Math.min(...n),Math.min(...a)],s=[Math.max(...n),Math.max(...a)];return{startPoint:r,endPoint:s}}getBoxForPalmLandmarks(t,n){let a=t.map(s=>L2([...s,1],n)),r=this.calculateLandmarksBoundingBox(a);return Y0(J0(r),Vle)}getBoxForHandLandmarks(t){let n=this.calculateLandmarksBoundingBox(t),a=Y0(J0(n),z9);a.palmLandmarks=[];for(let r=0;r<P9.length;r++)a.palmLandmarks.push(t[P9[r]].slice(0,2));return a}transformRawCoords(t,n,a,r){let s=Z0(n),i=[s[0]/this.inputSize,s[1]/this.inputSize,(s[0]+s[1])/this.inputSize/2],o=t.map(h=>[i[0]*(h[0]-this.inputSize/2),i[1]*(h[1]-this.inputSize/2),i[2]*h[2]]),u=P2(a,[0,0]),l=o.map(h=>[...L2(h,u),h[2]]),d=O9(r),p=[...fp(n),1],c=[ls(p,d[0]),ls(p,d[1])];return l.map(h=>[Math.trunc(h[0]+c[0]),Math.trunc(h[1]+c[1]),Math.trunc(h[2])])}async estimateHands(t,n){let a=!1,r;(this.skipped===0||this.skipped>n.hand.skipFrames||!n.hand.landmarks||!n.skipFrame)&&(r=await this.handDetector.estimateHandBounds(t,n),this.skipped=0),n.skipFrame&&this.skipped++,r&&r.length>0&&(r.length!==this.detectedHands&&this.detectedHands!==n.hand.maxDetected||!n.hand.landmarks)&&(this.detectedHands=0,this.storedBoxes=[...r],this.storedBoxes.length>0&&(a=!0));let s=[];for(let i=0;i<this.storedBoxes.length;i++){let o=this.storedBoxes[i];if(!!o)if(n.hand.landmarks){let u=n.hand.rotation?$9(o.palmLandmarks[jle],o.palmLandmarks[Ule]):0,l=fp(o),d=[l[0]/t.shape[2],l[1]/t.shape[1]],p=n.hand.rotation&&aa.flags.IS_BROWSER?je.rotateWithOffset(t,u,0,d):t.clone(),c=P2(-u,l),h=a?this.getBoxForPalmLandmarks(o.palmLandmarks,c):o,m=R9(h,p,[this.inputSize,this.inputSize]),f=m.div(255);m.dispose(),p.dispose();let[A,y]=await this.handPoseModel.predict(f);f.dispose();let g=A.dataSync()[0];if(A.dispose(),g>=n.hand.minConfidence){let x=q(y,[-1,3]),v=x.arraySync();y.dispose(),x.dispose();let b=this.transformRawCoords(v,h,u,c),w=this.getBoxForHandLandmarks(b);this.storedBoxes[i]={...w,confidence:g};let N={landmarks:b,confidence:g,box:{topLeft:w.startPoint,bottomRight:w.endPoint}};s.push(N)}else this.storedBoxes[i]=null;y.dispose()}else{let u=Y0(J0(o),z9),l={confidence:o.confidence,box:{topLeft:u.startPoint,bottomRight:u.endPoint}};s.push(l)}}return this.storedBoxes=this.storedBoxes.filter(i=>i!==null),this.detectedHands=s.length,s}};var L9={thumb:[1,2,3,4],indexFinger:[5,6,7,8],middleFinger:[9,10,11,12],ringFinger:[13,14,15,16],pinky:[17,18,19,20],palmBase:[0]},us,ds,W9;async function B2(e,t){let n=await W9.estimateHands(e,t);if(!n)return[];let a=[];for(let r=0;r<n.length;r++){let s={};if(n[r].landmarks)for(let l of Object.keys(L9))s[l]=L9[l].map(d=>n[r].landmarks[d]);let i=n[r].landmarks,o=[Number.MAX_SAFE_INTEGER,Number.MAX_SAFE_INTEGER,0,0],u=[0,0,0,0];if(i&&i.length>0){for(let l of i)l[0]<o[0]&&(o[0]=l[0]),l[1]<o[1]&&(o[1]=l[1]),l[0]>o[2]&&(o[2]=l[0]),l[1]>o[3]&&(o[3]=l[1]);o[2]-=o[0],o[3]-=o[1],u=[o[0]/(e.shape[2]||0),o[1]/(e.shape[1]||0),o[2]/(e.shape[2]||0),o[3]/(e.shape[1]||0)]}else o=n[r].box?[Math.trunc(Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.max(0,n[r].box.topLeft[1])),Math.trunc(Math.min(e.shape[2]||0,n[r].box.bottomRight[0])-Math.max(0,n[r].box.topLeft[0])),Math.trunc(Math.min(e.shape[1]||0,n[r].box.bottomRight[1])-Math.max(0,n[r].box.topLeft[1]))]:[0,0,0,0],u=[n[r].box.topLeft[0]/(e.shape[2]||0),n[r].box.topLeft[1]/(e.shape[1]||0),(n[r].box.bottomRight[0]-n[r].box.topLeft[0])/(e.shape[2]||0),(n[r].box.bottomRight[1]-n[r].box.topLeft[1])/(e.shape[1]||0)];a.push({id:r,score:Math.round(100*n[r].confidence)/100,box:o,boxRaw:u,keypoints:i,annotations:s})}return a}async function V2(e){!us||!ds?([us,ds]=await Promise.all([e.hand.enabled?bt(kt(e.modelBasePath,e.hand.detector.modelPath),{fromTFHub:e.hand.detector.modelPath.includes("tfhub.dev")}):null,e.hand.landmarks?bt(kt(e.modelBasePath,e.hand.skeleton.modelPath),{fromTFHub:e.hand.skeleton.modelPath.includes("tfhub.dev")}):null]),e.hand.enabled&&(!us||!us.modelUrl?pe("load model failed:",e.hand.detector.modelPath):e.debug&&pe("load model:",us.modelUrl),!ds||!ds.modelUrl?pe("load model failed:",e.hand.skeleton.modelPath):e.debug&&pe("load model:",ds.modelUrl))):(e.debug&&pe("cached model:",us.modelUrl),e.debug&&pe("cached model:",ds.modelUrl));let t=new z2(us);return W9=new W2(t,ds),[us,ds]}var H2={};Ba(H2,{load:()=>Q0,predict:()=>U2});var B9=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftPalm","rightPalm","leftIndex","rightIndex","leftPinky","rightPinky","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle","leftHeel","rightHeel","leftFoot","rightFoot","midHip","forehead","leftThumb","leftHand","rightThumb","rightHand"],V9=["nose","leftEyeInside","leftEye","leftEyeOutside","rightEyeInside","rightEye","rightEyeOutside","leftEar","rightEar","leftMouth","rightMouth","leftShoulder","rightShoulder","leftElbow","rightElbow","left:15","right:16","left:17","right:18","left:19","right:20","left:21","right:22","leftChest","rightChest","neck","forehead","left:27","right:28","left:29","right:30"];var _n;async function Q0(e){return _n?e.debug&&pe("cached model:",_n.modelUrl):(_n=await bt(kt(e.modelBasePath,e.body.modelPath)),_n.width=parseInt(_n.signature.inputs["input_1:0"].tensorShape.dim[2].size),_n.height=parseInt(_n.signature.inputs["input_1:0"].tensorShape.dim[1].size),!_n||!_n.modelUrl?pe("load model failed:",e.body.modelPath):e.debug&&pe("load model:",_n.modelUrl)),_n}async function U2(e,t){var f;if(!_n)return[];if(!t.body.enabled)return[];let n={width:e.shape[2]||0,height:e.shape[1]||0},a=je.resizeBilinear(e,[_n.width,_n.height],!1),r=fe(a,[255]);a.dispose();let s=await _n.predict(r),i=((f=s.find(A=>A.size===195||A.size===155))==null?void 0:f.dataSync())||[];s.forEach(A=>A.dispose()),r.dispose();let o=[],u=(i==null?void 0:i.length)===195?B9:V9,l=5;for(let A=0;A<i.length/l;A++)o.push({id:A,part:u[A],position:[Math.trunc(n.width*i[l*A+0]/255),Math.trunc(n.height*i[l*A+1]/255),Math.trunc(i[l*A+2])+0],positionRaw:[i[l*A+0]/255,i[l*A+1]/255,i[l*A+2]+0],score:(100-Math.trunc(100/(1+Math.exp(i[l*A+3]))))/100,presence:(100-Math.trunc(100/(1+Math.exp(i[l*A+4]))))/100});let d=o.map(A=>A.position[0]),p=o.map(A=>A.position[1]),c=[Math.min(...d),Math.min(...p),Math.max(...d)-Math.min(...d),Math.max(...p)-Math.min(...d)],h=[0,0,0,0],m=o.reduce((A,y)=>y.score>A?y.score:A,0);return[{id:0,score:m,box:c,boxRaw:h,keypoints:o}]}var On,rr=[],G2=[0,0,0,0],q2=[0,0,0,0],ef=0,X2=Number.MAX_SAFE_INTEGER,Hle=["head","neck","rightShoulder","rightElbow","rightWrist","chest","leftShoulder","leftElbow","leftWrist","pelvis","rightHip","rightKnee","rightAnkle","leftHip","leftKnee","leftAnkle"];async function j9(e){return On?e.debug&&pe("cached model:",On.modelUrl):(On=await bt(kt(e.modelBasePath,e.body.modelPath)),!On||!On.modelUrl?pe("load model failed:",e.body.modelPath):e.debug&&pe("load model:",On.modelUrl)),On}function Gle(e,t){let[n,a]=e.shape;return V(()=>{let r=(o,u)=>Ae(o,B(fe(o,we(u,"int32")),we(u,"int32"))),s=q(e,[a*n]),i=Bn(s,0).dataSync()[0];if(i>t){let o=ki(s,0),u=r(o,n).dataSync()[0],l=fe(o,we(n,"int32")).dataSync()[0];return[u,l,i]}return[0,0,i]})}async function K2(e,t){return X2<t.body.skipFrames&&t.skipFrame&&Object.keys(rr).length>0?(X2++,[{id:0,score:ef,box:G2,boxRaw:q2,keypoints:rr}]):(X2=0,new Promise(async n=>{let a=V(()=>{if(!On.inputs[0].shape)return null;let l=je.resizeBilinear(e,[On.inputs[0].shape[2],On.inputs[0].shape[1]],!1);return B(l,2).sub(1)}),r;if(t.body.enabled&&(r=await On.predict(a)),a.dispose(),r){rr.length=0;let l=r.squeeze();Ie(r);let d=l.unstack(2);Ie(l);for(let p=0;p<d.length;p++){let[c,h,m]=Gle(d[p],t.body.minConfidence);ef>t.body.minConfidence&&rr.push({score:Math.round(100*m)/100,part:Hle[p],positionRaw:[c/On.inputs[0].shape[2],h/On.inputs[0].shape[1]],position:[Math.round(e.shape[2]*c/On.inputs[0].shape[2]),Math.round(e.shape[1]*h/On.inputs[0].shape[1])]})}d.forEach(p=>Ie(p))}ef=rr.reduce((l,d)=>d.score>l?d.score:l,0);let s=rr.map(l=>l.position[0]),i=rr.map(l=>l.position[1]);G2=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=rr.map(l=>l.positionRaw[0]),u=rr.map(l=>l.positionRaw[1]);q2=[Math.min(...o),Math.min(...u),Math.max(...o)-Math.min(...o),Math.max(...u)-Math.min(...u)],n([{id:0,score:ef,box:G2,boxRaw:q2,keypoints:rr}])}))}var Pa,sr=[],Z2=[0,0,0,0],Y2=[0,0,0,0],cu=0,J2=Number.MAX_SAFE_INTEGER,qle=["nose","leftEye","rightEye","leftEar","rightEar","leftShoulder","rightShoulder","leftElbow","rightElbow","leftWrist","rightWrist","leftHip","rightHip","leftKnee","rightKnee","leftAnkle","rightAnkle"];async function Q2(e){return Pa?e.debug&&pe("cached model:",Pa.modelUrl):(Pa=await bt(kt(e.modelBasePath,e.body.modelPath)),!Pa||!Pa.modelUrl?pe("load model failed:",e.body.modelPath):e.debug&&pe("load model:",Pa.modelUrl)),Pa}async function e5(e,t){return J2<t.body.skipFrames&&t.skipFrame&&Object.keys(sr).length>0?(J2++,[{id:0,score:cu,box:Z2,boxRaw:Y2,keypoints:sr}]):(J2=0,new Promise(async n=>{let a=V(()=>{if(!Pa.inputs[0].shape)return null;let l=je.resizeBilinear(e,[Pa.inputs[0].shape[2],Pa.inputs[0].shape[1]],!1);return me(l,"int32")}),r;if(t.body.enabled&&(r=await Pa.predict(a)),a.dispose(),r){sr.length=0;let l=r.arraySync();Ie(r);let d=l[0][0];for(let p=0;p<d.length;p++)cu=d[p][2],cu>t.body.minConfidence&&sr.push({score:Math.round(100*cu)/100,part:qle[p],positionRaw:[d[p][1],d[p][0]],position:[Math.round((e.shape[2]||0)*d[p][1]),Math.round((e.shape[1]||0)*d[p][0])]})}cu=sr.reduce((l,d)=>d.score>l?d.score:l,0);let s=sr.map(l=>l.position[0]),i=sr.map(l=>l.position[1]);Z2=[Math.min(...s),Math.min(...i),Math.max(...s)-Math.min(...s),Math.max(...i)-Math.min(...i)];let o=sr.map(l=>l.positionRaw[0]),u=sr.map(l=>l.positionRaw[1]);Y2=[Math.min(...o),Math.min(...u),Math.max(...o)-Math.min(...o),Math.max(...u)-Math.min(...u)],n([{id:0,score:cu,box:Z2,boxRaw:Y2,keypoints:sr}])}))}var s5={};Ba(s5,{load:()=>a5,predict:()=>r5});var hu=[{class:1,label:"person"},{class:2,label:"bicycle"},{class:3,label:"car"},{class:4,label:"motorcycle"},{class:5,label:"airplane"},{class:6,label:"bus"},{class:7,label:"train"},{class:8,label:"truck"},{class:9,label:"boat"},{class:10,label:"traffic light"},{class:11,label:"fire hydrant"},{class:12,label:"stop sign"},{class:13,label:"parking meter"},{class:14,label:"bench"},{class:15,label:"bird"},{class:16,label:"cat"},{class:17,label:"dog"},{class:18,label:"horse"},{class:19,label:"sheep"},{class:20,label:"cow"},{class:21,label:"elephant"},{class:22,label:"bear"},{class:23,label:"zebra"},{class:24,label:"giraffe"},{class:25,label:"backpack"},{class:26,label:"umbrella"},{class:27,label:"handbag"},{class:28,label:"tie"},{class:29,label:"suitcase"},{class:30,label:"frisbee"},{class:31,label:"skis"},{class:32,label:"snowboard"},{class:33,label:"sports ball"},{class:34,label:"kite"},{class:35,label:"baseball bat"},{class:36,label:"baseball glove"},{class:37,label:"skateboard"},{class:38,label:"surfboard"},{class:39,label:"tennis racket"},{class:40,label:"bottle"},{class:41,label:"wine glass"},{class:42,label:"cup"},{class:43,label:"fork"},{class:44,label:"knife"},{class:45,label:"spoon"},{class:46,label:"bowl"},{class:47,label:"banana"},{class:48,label:"apple"},{class:49,label:"sandwich"},{class:50,label:"orange"},{class:51,label:"broccoli"},{class:52,label:"carrot"},{class:53,label:"hot dog"},{class:54,label:"pizza"},{class:55,label:"donut"},{class:56,label:"cake"},{class:57,label:"chair"},{class:58,label:"couch"},{class:59,label:"potted plant"},{class:60,label:"bed"},{class:61,label:"dining table"},{class:62,label:"toilet"},{class:63,label:"tv"},{class:64,label:"laptop"},{class:65,label:"mouse"},{class:66,label:"remote"},{class:67,label:"keyboard"},{class:68,label:"cell phone"},{class:69,label:"microwave"},{class:70,label:"oven"},{class:71,label:"toaster"},{class:72,label:"sink"},{class:73,label:"refrigerator"},{class:74,label:"book"},{class:75,label:"clock"},{class:76,label:"vase"},{class:77,label:"scissors"},{class:78,label:"teddy bear"},{class:79,label:"hair drier"},{class:80,label:"toothbrush"}];var Yn,t5=[],n5=Number.MAX_SAFE_INTEGER,tf=2.5;async function a5(e){if(Yn)e.debug&&pe("cached model:",Yn.modelUrl);else{Yn=await bt(kt(e.modelBasePath,e.object.modelPath));let t=Object.values(Yn.modelSignature.inputs);if(Yn.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Yn.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!Yn||!Yn.modelUrl?pe("load model failed:",e.object.modelPath):e.debug&&pe("load model:",Yn.modelUrl)}return Yn}async function Xle(e,t,n,a){let r=0,s=[];for(let l of[1,2,4])V(()=>{var A,y;let d=l*13,p=(A=e.find(g=>g.shape[1]===d**2&&g.shape[2]===hu.length))==null?void 0:A.squeeze(),c=(y=e.find(g=>g.shape[1]===d**2&&g.shape[2]<hu.length))==null?void 0:y.squeeze(),m=c.reshape([-1,4,c.shape[1]/4]).argMax(2).arraySync(),f=p.arraySync();for(let g=0;g<p.shape[0];g++)for(let x=0;x<p.shape[1];x++){let v=f[g][x];if(v>a.object.minConfidence&&x!==61){let b=(.5+Math.trunc(g%d))/d,w=(.5+Math.trunc(g/d))/d,N=m[g].map(W=>W*(d/l/t)),[C,E]=[b-tf/l*N[0],w-tf/l*N[1]],[z,$]=[b+tf/l*N[2]-C,w+tf/l*N[3]-E],S=[C,E,z,$];S=S.map(W=>Math.max(0,Math.min(W,1)));let O=[S[0]*n[0],S[1]*n[1],S[2]*n[0],S[3]*n[1]],_={id:r++,score:Math.round(100*v)/100,class:x+1,label:hu[x].label,box:O.map(W=>Math.trunc(W)),boxRaw:S};s.push(_)}}});e.forEach(l=>Ie(l));let i=s.map(l=>[l.boxRaw[1],l.boxRaw[0],l.boxRaw[3],l.boxRaw[2]]),o=s.map(l=>l.score),u=[];if(i&&i.length>0){let l=await je.nonMaxSuppressionAsync(i,o,a.object.maxDetected,a.object.iouThreshold,a.object.minConfidence);u=l.dataSync(),Ie(l)}return s=s.filter((l,d)=>u.includes(d)).sort((l,d)=>d.score-l.score),s}async function r5(e,t){return n5<t.object.skipFrames&&t.skipFrame&&t5.length>0?(n5++,t5):(n5=0,new Promise(async n=>{let a=[e.shape[2],e.shape[1]],r=je.resizeBilinear(e,[Yn.inputSize,Yn.inputSize],!1),s=r.div(255),i=s.transpose([0,3,1,2]);s.dispose(),r.dispose();let o;t.object.enabled&&(o=await Yn.predict(i)),i.dispose();let u=await Xle(o,Yn.inputSize,a,t);t5=u,n(u)}))}var d5={};Ba(d5,{load:()=>l5,predict:()=>u5});var Jn,i5=[],o5=Number.MAX_SAFE_INTEGER;async function l5(e){if(Jn)e.debug&&pe("cached model:",Jn.modelUrl);else{Jn=await bt(kt(e.modelBasePath,e.object.modelPath));let t=Object.values(Jn.modelSignature.inputs);if(Jn.inputSize=Array.isArray(t)?parseInt(t[0].tensorShape.dim[2].size):null,!Jn.inputSize)throw new Error(`Human: Cannot determine model inputSize: ${e.object.modelPath}`);!Jn||!Jn.modelUrl?pe("load model failed:",e.object.modelPath):e.debug&&pe("load model:",Jn.modelUrl)}return Jn}async function Kle(e,t,n,a){if(!e)return[];let r=[],s=e.arraySync(),i=Aa(e);e.dispose();let o=Kt(i,6,1);i.dispose();let l=mn([o[1],o[0],o[3],o[2]],1).squeeze(),d=o[4].squeeze(),p=o[5].squeeze();o.forEach(f=>f.dispose());let c=await je.nonMaxSuppressionAsync(l,d,a.object.maxDetected,a.object.iouThreshold,a.object.minConfidence);l.dispose(),d.dispose(),p.dispose();let 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|
|
`)}var nf=2048,Ee,vt,zt;function p5(e,t){let n;if(!e)throw new Error("Human: Input is missing");if(!(e instanceof We)&&!(typeof Image!="undefined"&&e instanceof Image)&&!(typeof ImageData!="undefined"&&e instanceof ImageData)&&!(typeof ImageBitmap!="undefined"&&e instanceof ImageBitmap)&&!(typeof HTMLImageElement!="undefined"&&e instanceof HTMLImageElement)&&!(typeof HTMLMediaElement!="undefined"&&e instanceof HTMLMediaElement)&&!(typeof HTMLVideoElement!="undefined"&&e instanceof HTMLVideoElement)&&!(typeof HTMLCanvasElement!="undefined"&&e instanceof HTMLCanvasElement)&&!(typeof OffscreenCanvas!="undefined"&&e instanceof OffscreenCanvas))throw new Error("Human: Input type is not recognized");if(e instanceof We)if(e.shape&&e.shape.length===4&&e.shape[0]===1&&e.shape[3]===3)n=Ua(e);else throw new Error(`Human: Input tensor shape must be [1, height, width, 3] and instead was ${e.shape}`);else{let r=e.naturalWidth||e.videoWidth||e.width||e.shape&&e.shape[1]>0,s=e.naturalHeight||e.videoHeight||e.height||e.shape&&e.shape[2]>0;if(!r||!s)return{tensor:null,canvas:Ee};let i=r,o=s;if(i>nf&&(i=nf,o=i*s/r),o>nf&&(o=nf,i=o*r/s),t.filter.width>0?i=t.filter.width:t.filter.height>0&&(i=r*(t.filter.height/s)),t.filter.height>0?o=t.filter.height:t.filter.width>0&&(o=s*(t.filter.width/r)),!i||!o)throw new Error("Human: Input cannot determine dimension");(!Ee||(Ee==null?void 0:Ee.width)!==i||(Ee==null?void 0:Ee.height)!==o)&&(Ee=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas"),(Ee==null?void 0:Ee.width)!==i&&(Ee.width=i),(Ee==null?void 0:Ee.height)!==o&&(Ee.height=o));let u=Ee.getContext("2d");if(e instanceof ImageData?u.putImageData(e,0,0):t.filter.flip&&typeof u.translate!="undefined"?(u.translate(r,0),u.scale(-1,1),u.drawImage(e,0,0,r,s,0,0,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),u.setTransform(1,0,0,1,0,0)):u.drawImage(e,0,0,r,s,0,0,Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height),t.filter.enabled){if((!zt||!vt||Ee.width!==vt.width||(Ee==null?void 0:Ee.height)!==(vt==null?void 0:vt.height))&&(vt=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(Ee==null?void 0:Ee.width,Ee==null?void 0:Ee.height):document.createElement("canvas"),(vt==null?void 0:vt.width)!==(Ee==null?void 0:Ee.width)&&(vt.width=Ee==null?void 0:Ee.width),(vt==null?void 0:vt.height)!==(Ee==null?void 0:Ee.height)&&(vt.height=Ee==null?void 0:Ee.height),zt=aa.flags.IS_BROWSER?new X9({canvas:vt}):null),!zt)return{tensor:null,canvas:Ee};zt.reset(),zt.addFilter("brightness",t.filter.brightness),t.filter.contrast!==0&&zt.addFilter("contrast",t.filter.contrast),t.filter.sharpness!==0&&zt.addFilter("sharpen",t.filter.sharpness),t.filter.blur!==0&&zt.addFilter("blur",t.filter.blur),t.filter.saturation!==0&&zt.addFilter("saturation",t.filter.saturation),t.filter.hue!==0&&zt.addFilter("hue",t.filter.hue),t.filter.negative&&zt.addFilter("negative"),t.filter.sepia&&zt.addFilter("sepia"),t.filter.vintage&&zt.addFilter("brownie"),t.filter.sepia&&zt.addFilter("sepia"),t.filter.kodachrome&&zt.addFilter("kodachrome"),t.filter.technicolor&&zt.addFilter("technicolor"),t.filter.polaroid&&zt.addFilter("polaroid"),t.filter.pixelate!==0&&zt.addFilter("pixelate",t.filter.pixelate),zt.apply(Ee)}else vt=Ee,zt&&(zt=null);let l;if(vt.data){let p=[vt.height,vt.width,3];l=Pc(vt.data,p,"int32")}else if(vt instanceof ImageData)l=vi.fromPixels(vt);else if(t.backend==="webgl"||t.backend==="humangl"){let p=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");p.width=i,p.height=o;let c=p.getContext("2d");c==null||c.drawImage(vt,0,0),l=vi.fromPixels(p)}else{let p=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(i,o):document.createElement("canvas");p.width=i,p.height=o;let c=p.getContext("2d");c==null||c.drawImage(vt,0,0);let h=c==null?void 0:c.getImageData(0,0,i,o);l=vi.fromPixels(h)}let d=l.toFloat();n=d.expandDims(0),l.dispose(),d.dispose()}let a=t.filter.return?vt:null;return{tensor:n,canvas:a}}var f5={};Ba(f5,{all:()=>Qle,body:()=>Y9,canvas:()=>Jle,face:()=>Z9,gesture:()=>K9,hand:()=>J9,object:()=>Q9,options:()=>ps,person:()=>Yle});var ps={color:"rgba(173, 216, 230, 0.6)",labelColor:"rgba(173, 216, 230, 1)",shadowColor:"black",font:'small-caps 14px "Segoe UI"',lineHeight:24,lineWidth:6,pointSize:2,roundRect:28,drawPoints:!1,drawLabels:!0,drawBoxes:!0,drawPolygons:!0,drawGaze:!0,fillPolygons:!1,useDepth:!0,useCurves:!1,bufferedOutput:!0},af=e=>Math.round(e*180/Math.PI);function c5(e,t,n,a=0,r){e.fillStyle=r.useDepth&&a?`rgba(${127.5+2*a}, ${127.5-2*a}, 255, 0.3)`:r.color,e.beginPath(),e.arc(t,n,r.pointSize,0,2*Math.PI),e.fill()}function mp(e,t,n,a,r,s){if(e.beginPath(),s.useCurves){let i=(t+t+a)/2,o=(n+n+r)/2;e.ellipse(i,o,a/2,r/2,0,0,2*Math.PI)}else e.lineWidth=s.lineWidth,e.moveTo(t+s.roundRect,n),e.lineTo(t+a-s.roundRect,n),e.quadraticCurveTo(t+a,n,t+a,n+s.roundRect),e.lineTo(t+a,n+r-s.roundRect),e.quadraticCurveTo(t+a,n+r,t+a-s.roundRect,n+r),e.lineTo(t+s.roundRect,n+r),e.quadraticCurveTo(t,n+r,t,n+r-s.roundRect),e.lineTo(t,n+s.roundRect),e.quadraticCurveTo(t,n,t+s.roundRect,n),e.closePath();e.stroke()}function h5(e,t=[],n){if(!(t===void 0||t.length===0)){e.beginPath(),e.moveTo(t[0][0],t[0][1]);for(let a of t){let r=a[2]||0;e.strokeStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.fillStyle=n.useDepth&&r?`rgba(${127.5+2*r}, ${127.5-2*r}, 255, 0.3)`:n.color,e.lineTo(a[0],Math.round(a[1]))}e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}function Ap(e,t=[],n){if(!(t===void 0||t.length===0)){if(!n.useCurves||t.length<=2){h5(e,t,n);return}e.moveTo(t[0][0],t[0][1]);for(let a=0;a<t.length-2;a++){let r=(t[a][0]+t[a+1][0])/2,s=(t[a][1]+t[a+1][1])/2;e.quadraticCurveTo(t[a][0],t[a][1],r,s)}e.quadraticCurveTo(t[t.length-2][0],t[t.length-2][1],t[t.length-1][0],t[t.length-1][1]),e.stroke(),n.fillPolygons&&(e.closePath(),e.fill())}}async function K9(e,t,n){let a=Pn(ps,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!r)return;r.font=a.font,r.fillStyle=a.color;let s=1;for(let i=0;i<t.length;i++){let o=[],u=[];if([o,u]=Object.entries(t[i]),u.length>1&&u[1].length>0){let l=o[1]>0?`#${o[1]}`:"",d=`${o[0]} ${l}: ${u[1]}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(d,8,2+s*a.lineHeight)),r.fillStyle=a.labelColor,r.fillText(d,6,0+s*a.lineHeight),s+=1}}}async function Z9(e,t,n){var s,i,o,u;let a=Pn(ps,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r)for(let l of t){r.font=a.font,r.strokeStyle=a.color,r.fillStyle=a.color,a.drawBoxes&&mp(r,l.box[0],l.box[1],l.box[2],l.box[3],a);let d=[];if(d.push(`face: ${Math.trunc(100*l.score)}%`),l.genderScore&&d.push(`${l.gender||""} ${Math.trunc(100*l.genderScore)}%`),l.age&&d.push(`age: ${l.age||""}`),l.iris&&d.push(`distance: ${l.iris}`),l.emotion&&l.emotion.length>0){let p=l.emotion.map(c=>`${Math.trunc(100*c.score)}% ${c.emotion}`);p.length>3&&(p.length=3),d.push(p.join(" "))}l.rotation&&l.rotation.angle&&l.rotation.gaze&&(l.rotation.angle.roll&&d.push(`roll: ${af(l.rotation.angle.roll)}\xB0 yaw:${af(l.rotation.angle.yaw)}\xB0 pitch:${af(l.rotation.angle.pitch)}\xB0`),l.rotation.gaze.bearing&&d.push(`gaze: ${af(l.rotation.gaze.bearing)}\xB0`)),d.length===0&&d.push("face"),r.fillStyle=a.color;for(let p=d.length-1;p>=0;p--){let c=Math.max(l.box[0],0),h=p*a.lineHeight+l.box[1];a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(d[p],c+5,h+16)),r.fillStyle=a.labelColor,r.fillText(d[p],c+4,h+15)}if(r.lineWidth=1,l.mesh&&l.mesh.length>0){if(a.drawPoints)for(let p of l.mesh)c5(r,p[0],p[1],p[2],a);if(a.drawPolygons){r.lineWidth=1;for(let p=0;p<Zi.length/3;p++){let c=[Zi[p*3+0],Zi[p*3+1],Zi[p*3+2]].map(h=>l.mesh[h]);h5(r,c,a)}if(l.annotations&&l.annotations.leftEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(l.annotations.leftEyeIris[3][0]-l.annotations.leftEyeIris[1][0])/2,c=Math.abs(l.annotations.leftEyeIris[4][1]-l.annotations.leftEyeIris[2][1])/2;r.ellipse(l.annotations.leftEyeIris[0][0],l.annotations.leftEyeIris[0][1],p,c,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(l.annotations&&l.annotations.rightEyeIris){r.strokeStyle=a.useDepth?"rgba(255, 200, 255, 0.3)":a.color,r.beginPath();let p=Math.abs(l.annotations.rightEyeIris[3][0]-l.annotations.rightEyeIris[1][0])/2,c=Math.abs(l.annotations.rightEyeIris[4][1]-l.annotations.rightEyeIris[2][1])/2;r.ellipse(l.annotations.rightEyeIris[0][0],l.annotations.rightEyeIris[0][1],p,c,0,0,2*Math.PI),r.stroke(),a.fillPolygons&&(r.fillStyle=a.useDepth?"rgba(255, 255, 200, 0.3)":a.color,r.fill())}if(a.drawGaze&&((i=(s=l.rotation)==null?void 0:s.gaze)==null?void 0:i.strength)&&((u=(o=l.rotation)==null?void 0:o.gaze)==null?void 0:u.bearing)){r.strokeStyle="pink",r.beginPath();let p=[l.annotations.leftEyeIris[0][0]+Math.sin(l.rotation.gaze.bearing)*l.rotation.gaze.strength*l.box[3],l.annotations.leftEyeIris[0][1]+Math.cos(l.rotation.gaze.bearing)*l.rotation.gaze.strength*l.box[2]];r.moveTo(l.annotations.leftEyeIris[0][0],l.annotations.leftEyeIris[0][1]),r.lineTo(p[0],p[1]);let c=[l.annotations.rightEyeIris[0][0]+Math.sin(l.rotation.gaze.bearing)*l.rotation.gaze.strength*l.box[3],l.annotations.rightEyeIris[0][1]+Math.cos(l.rotation.gaze.bearing)*l.rotation.gaze.strength*l.box[2]];r.moveTo(l.annotations.rightEyeIris[0][0],l.annotations.rightEyeIris[0][1]),r.lineTo(c[0],c[1]),r.stroke()}}}}}async function Y9(e,t,n){var s;let a=Pn(ps,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round";for(let i=0;i<t.length;i++){if(r.strokeStyle=a.color,r.fillStyle=a.color,r.lineWidth=a.lineWidth,r.font=a.font,a.drawBoxes&&t[i].box&&((s=t[i].box)==null?void 0:s.length)===4&&(mp(r,t[i].box[0],t[i].box[1],t[i].box[2],t[i].box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(`body ${100*t[i].score}%`,t[i].box[0]+3,1+t[i].box[1]+a.lineHeight,t[i].box[2])),r.fillStyle=a.labelColor,r.fillText(`body ${100*t[i].score}%`,t[i].box[0]+2,0+t[i].box[1]+a.lineHeight,t[i].box[2]))),a.drawPoints)for(let o=0;o<t[i].keypoints.length;o++)r.fillStyle=a.useDepth&&t[i].keypoints[o].position[2]?`rgba(${127.5+2*(t[i].keypoints[o].position[2]||0)}, ${127.5-2*(t[i].keypoints[o].position[2]||0)}, 255, 0.5)`:a.color,c5(r,t[i].keypoints[o].position[0],t[i].keypoints[o].position[1],0,a);if(a.drawLabels&&(r.font=a.font,t[i].keypoints))for(let o of t[i].keypoints)r.fillStyle=a.useDepth&&o.position[2]?`rgba(${127.5+2*o.position[2]}, ${127.5-2*o.position[2]}, 255, 0.5)`:a.color,r.fillText(`${o.part} ${Math.trunc(100*o.score)}%`,o.position[0]+4,o.position[1]+4);if(a.drawPolygons&&t[i].keypoints){let o,u=[];u.length=0,o=t[i].keypoints.find(l=>l.part==="leftShoulder"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightShoulder"),o&&u.push([o.position[0],o.position[1]]),Ap(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="rightShoulder"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightHip"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftHip"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftShoulder"),o&&u.push([o.position[0],o.position[1]]),u.length===4&&h5(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="leftHip"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftKnee"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftAnkle"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftHeel"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftFoot"),o&&u.push([o.position[0],o.position[1]]),Ap(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="rightHip"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightKnee"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightAnkle"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightHeel"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightFoot"),o&&u.push([o.position[0],o.position[1]]),Ap(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="leftShoulder"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftElbow"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftWrist"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="leftPalm"),o&&u.push([o.position[0],o.position[1]]),Ap(r,u,a),u.length=0,o=t[i].keypoints.find(l=>l.part==="rightShoulder"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightElbow"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightWrist"),o&&u.push([o.position[0],o.position[1]]),o=t[i].keypoints.find(l=>l.part==="rightPalm"),o&&u.push([o.position[0],o.position[1]]),Ap(r,u,a)}}}}async function J9(e,t,n){let a=Pn(ps,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s of t){if(a.drawBoxes&&(r.strokeStyle=a.color,r.fillStyle=a.color,mp(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels&&(a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText("hand",s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText("hand",s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])),r.stroke()),a.drawPoints&&s.keypoints&&s.keypoints.length>0)for(let i of s.keypoints)r.fillStyle=a.useDepth?`rgba(${127.5+2*i[2]}, ${127.5-2*i[2]}, 255, 0.5)`:a.color,c5(r,i[0],i[1],0,a);if(a.drawLabels){let i=(o,u)=>{r.fillStyle=a.useDepth?`rgba(${127.5+2*o[o.length-1][2]}, ${127.5-2*o[o.length-1][2]}, 255, 0.5)`:a.color,r.fillText(u,o[o.length-1][0]+4,o[o.length-1][1]+4)};r.font=a.font,i(s.annotations.indexFinger,"index"),i(s.annotations.middleFinger,"middle"),i(s.annotations.ringFinger,"ring"),i(s.annotations.pinky,"pinky"),i(s.annotations.thumb,"thumb"),i(s.annotations.palmBase,"palm")}if(a.drawPolygons){let i=o=>{if(!!o)for(let u=0;u<o.length;u++)r.beginPath(),r.strokeStyle=a.useDepth?`rgba(${127.5+2*o[u][2]}, ${127.5-2*o[u][2]}, 255, 0.5)`:a.color,r.moveTo(o[u>0?u-1:0][0],o[u>0?u-1:0][1]),r.lineTo(o[u][0],o[u][1]),r.stroke()};r.lineWidth=a.lineWidth,i(s.annotations.indexFinger),i(s.annotations.middleFinger),i(s.annotations.ringFinger),i(s.annotations.pinky),i(s.annotations.thumb)}}}}async function Q9(e,t,n){let a=Pn(ps,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s of t)if(a.drawBoxes){if(r.strokeStyle=a.color,r.fillStyle=a.color,mp(r,s.box[0],s.box[1],s.box[2],s.box[3],a),a.drawLabels){let i=`${Math.round(100*s.score)}% ${s.label}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(i,s.box[0]+3,1+s.box[1]+a.lineHeight,s.box[2])),r.fillStyle=a.labelColor,r.fillText(i,s.box[0]+2,0+s.box[1]+a.lineHeight,s.box[2])}r.stroke()}}}async function Yle(e,t,n){let a=Pn(ps,n);if(!t||!e||!(e instanceof HTMLCanvasElement))return;let r=e.getContext("2d");if(!!r){r.lineJoin="round",r.font=a.font;for(let s=0;s<t.length;s++)if(a.drawBoxes){if(r.strokeStyle=a.color,r.fillStyle=a.color,mp(r,t[s].box[0],t[s].box[1],t[s].box[2],t[s].box[3],a),a.drawLabels){let i=`person #${s}`;a.shadowColor&&a.shadowColor!==""&&(r.fillStyle=a.shadowColor,r.fillText(i,t[s].box[0]+3,1+t[s].box[1]+a.lineHeight,t[s].box[2])),r.fillStyle=a.labelColor,r.fillText(i,t[s].box[0]+2,0+t[s].box[1]+a.lineHeight,t[s].box[2])}r.stroke()}}}async function Jle(e,t){if(!e||!t||!(e instanceof HTMLCanvasElement)||!(t instanceof HTMLCanvasElement))return;let n=e.getContext("2d");n==null||n.drawImage(e,0,0)}async function Qle(e,t,n){let a=Je(),r=Pn(ps,n);!t||!e||e instanceof HTMLCanvasElement&&(Z9(e,t.face,r),Y9(e,t.body,r),J9(e,t.hand,r),Q9(e,t.object,r),K9(e,t.gesture,r),t.performance.draw=Math.trunc(Je()-a))}function eI(e,t,n,a,r){var o,u,l,d,p,c,h,m,f,A,y,g,x,v,b,w;let s=0,i=[];for(let N of e){let C={id:s++,face:N,body:null,hands:{left:null,right:null},gestures:[],box:[0,0,0,0]};for(let _ of t)N.box[0]>_.box[0]&&N.box[0]<_.box[0]+_.box[2]&&N.box[1]+N.box[3]>_.box[1]&&N.box[1]+N.box[3]<_.box[1]+_.box[3]&&(C.body=_);if(C.body)for(let _ of n)_.box[0]+_.box[2]>C.body.box[0]&&_.box[0]+_.box[2]<C.body.box[0]+C.body.box[2]&&_.box[1]+_.box[3]>C.body.box[1]&&_.box[1]+_.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.left=_),_.box[0]<C.body.box[0]+C.body.box[2]&&_.box[0]>C.body.box[0]&&_.box[1]+_.box[3]>C.body.box[1]&&_.box[1]+_.box[3]<C.body.box[1]+C.body.box[3]&&C.hands&&(C.hands.right=_);for(let _ of a)_.face!==void 0&&_.face===N.id?(o=C.gestures)==null||o.push(_):_.iris!==void 0&&_.iris===N.id?(u=C.gestures)==null||u.push(_):_.body!==void 0&&_.body===((l=C.body)==null?void 0:l.id)?(d=C.gestures)==null||d.push(_):_.hand!==void 0&&_.hand===((c=(p=C.hands)==null?void 0:p.left)==null?void 0:c.id)?(h=C.gestures)==null||h.push(_):_.hand!==void 0&&_.hand===((f=(m=C.hands)==null?void 0:m.right)==null?void 0:f.id)&&((A=C.gestures)==null||A.push(_));let E=[],z=[],$=_=>{_&&_.length===4&&(E.push(_[0],_[0]+_[2]),z.push(_[1],_[1]+_[3]))};$((y=C.face)==null?void 0:y.box),$((g=C.body)==null?void 0:g.box),$((v=(x=C.hands)==null?void 0:x.left)==null?void 0:v.box),$((w=(b=C.hands)==null?void 0:b.right)==null?void 0:w.box);let S=Math.min(...E),O=Math.min(...z);C.box=[S,O,Math.max(...E)-S,Math.max(...z)-O],r&&r.length===4&&(C.boxRaw=[C.box[0]/r[2],C.box[1]/r[1],C.box[2]/r[2],C.box[3]/r[1]]),i.push(C)}return i}var $e={face:[],body:[],hand:[],gesture:[],object:[],persons:[],performance:{},timestamp:0};function tI(e){var r,s,i,o,u,l,d,p,c,h,m,f,A,y,g,x,v,b,w,N,C;let t=Date.now()-e.timestamp,n=t<1e3?8-Math.log(t):1;if(!$e.body||e.body.length!==$e.body.length)$e.body=JSON.parse(JSON.stringify(e.body));else for(let E=0;E<e.body.length;E++){let z=e.body[E].box.map((O,_)=>((n-1)*$e.body[E].box[_]+O)/n),$=e.body[E].boxRaw.map((O,_)=>((n-1)*$e.body[E].boxRaw[_]+O)/n),S=e.body[E].keypoints.map((O,_)=>({score:O.score,part:O.part,position:[$e.body[E].keypoints[_]?((n-1)*$e.body[E].keypoints[_].position[0]+O.position[0])/n:O.position[0],$e.body[E].keypoints[_]?((n-1)*$e.body[E].keypoints[_].position[1]+O.position[1])/n:O.position[1]],positionRaw:[$e.body[E].keypoints[_]?((n-1)*$e.body[E].keypoints[_].positionRaw[0]+O.positionRaw[0])/n:O.position[0],$e.body[E].keypoints[_]?((n-1)*$e.body[E].keypoints[_].positionRaw[1]+O.positionRaw[1])/n:O.position[1]]}));$e.body[E]={...e.body[E],box:z,boxRaw:$,keypoints:S}}if(!$e.hand||e.hand.length!==$e.hand.length)$e.hand=JSON.parse(JSON.stringify(e.hand));else for(let E=0;E<e.hand.length;E++){let z=e.hand[E].box.map((W,G)=>((n-1)*$e.hand[E].box[G]+W)/n),$=e.hand[E].boxRaw.map((W,G)=>((n-1)*$e.hand[E].boxRaw[G]+W)/n),S=e.hand[E].keypoints.map((W,G)=>W.map((H,J)=>((n-1)*$e.hand[E].keypoints[G][J]+H)/n)),O=Object.keys(e.hand[E].annotations),_={};for(let W of O)_[W]=e.hand[E].annotations[W].map((G,H)=>G.map((J,K)=>((n-1)*$e.hand[E].annotations[W][H][K]+J)/n));$e.hand[E]={...e.hand[E],box:z,boxRaw:$,keypoints:S,annotations:_}}if(!$e.face||e.face.length!==$e.face.length)$e.face=JSON.parse(JSON.stringify(e.face));else for(let E=0;E<e.face.length;E++){let z=e.face[E].box.map((O,_)=>((n-1)*$e.face[E].box[_]+O)/n),$=e.face[E].boxRaw.map((O,_)=>((n-1)*$e.face[E].boxRaw[_]+O)/n),S={matrix:[0,0,0,0,0,0,0,0,0],angle:{roll:0,yaw:0,pitch:0},gaze:{bearing:0,strength:0}};S.matrix=(r=e.face[E].rotation)==null?void 0:r.matrix,S.angle={roll:((n-1)*(((i=(s=$e.face[E].rotation)==null?void 0:s.angle)==null?void 0:i.roll)||0)+(((u=(o=e.face[E].rotation)==null?void 0:o.angle)==null?void 0:u.roll)||0))/n,yaw:((n-1)*(((d=(l=$e.face[E].rotation)==null?void 0:l.angle)==null?void 0:d.yaw)||0)+(((c=(p=e.face[E].rotation)==null?void 0:p.angle)==null?void 0:c.yaw)||0))/n,pitch:((n-1)*(((m=(h=$e.face[E].rotation)==null?void 0:h.angle)==null?void 0:m.pitch)||0)+(((A=(f=e.face[E].rotation)==null?void 0:f.angle)==null?void 0:A.pitch)||0))/n},S.gaze={bearing:((n-1)*(((g=(y=$e.face[E].rotation)==null?void 0:y.gaze)==null?void 0:g.bearing)||0)+(((v=(x=e.face[E].rotation)==null?void 0:x.gaze)==null?void 0:v.bearing)||0))/n,strength:((n-1)*(((w=(b=$e.face[E].rotation)==null?void 0:b.gaze)==null?void 0:w.strength)||0)+(((C=(N=e.face[E].rotation)==null?void 0:N.gaze)==null?void 0:C.strength)||0))/n},$e.face[E]={...e.face[E],rotation:S,box:z,boxRaw:$}}if(!$e.object||e.object.length!==$e.object.length)$e.object=JSON.parse(JSON.stringify(e.object));else for(let E=0;E<e.object.length;E++){let z=e.object[E].box.map((S,O)=>((n-1)*$e.object[E].box[O]+S)/n),$=e.object[E].boxRaw.map((S,O)=>((n-1)*$e.object[E].boxRaw[O]+S)/n);$e.object[E]={...e.object[E],box:z,boxRaw:$}}let a=e.persons;if(!$e.persons||a.length!==$e.persons.length)$e.persons=JSON.parse(JSON.stringify(a));else for(let E=0;E<a.length;E++)$e.persons[E].box=a[E].box.map((z,$)=>((n-1)*$e.persons[E].box[$]+z)/n);return $e.gesture=e.gesture,$e.performance=e.performance,$e}var rf=`
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2Q==`;var nI="2.0.0";var fu,yp,gp,Yi,Ji,mu,of,xp,lf,uf,df,pf,tue=class{constructor(t){na(this,fu,void 0);na(this,yp,void 0);na(this,gp,void 0);na(this,Yi,void 0);na(this,Ji,void 0);na(this,mu,void 0);this.analyze=(...t)=>{if(!dn(this,yp))return;let n=this.tf.engine().state.numTensors,a=dn(this,fu);wa(this,fu,n);let r=n-a;r!==0&&pe(...t,r)};na(this,of,t=>{if(!dn(this,gp))return null;if(!t)return"input is not defined";if(this.tf.ENV.flags.IS_NODE&&!(t instanceof We))return"input must be a tensor";try{this.tf.getBackend()}catch(n){return"backend not loaded"}return null});na(this,xp,async(t=!1)=>{var n;if(this.config.backend&&this.config.backend.length>0&&t||this.tf.getBackend()!==this.config.backend){let a=Je();if(this.state="backend",this.config.backend&&this.config.backend.length>0){if(typeof window=="undefined"&&typeof WorkerGlobalScope!="undefined"&&this.config.debug&&pe("running inside web worker"),this.tf.ENV.flags.IS_BROWSER&&this.config.backend==="tensorflow"&&(this.config.backend="webgl"),this.tf.ENV.flags.IS_NODE&&(this.config.backend==="webgl"||this.config.backend==="humangl")&&(this.config.backend="tensorflow"),this.config.debug&&pe("setting backend:",this.config.backend),this.config.backend==="wasm"){if(this.config.debug&&pe("wasm path:",this.config.wasmPath),typeof((n=this.tf)==null?void 0:n.setWasmPaths)!="undefined")this.tf.setWasmPaths(this.config.wasmPath);else throw new Error("Human: WASM backend is not loaded");let r=await this.tf.env().getAsync("WASM_HAS_SIMD_SUPPORT"),s=await this.tf.env().getAsync("WASM_HAS_MULTITHREAD_SUPPORT");this.config.debug&&pe(`wasm execution: ${r?"SIMD":"no SIMD"} ${s?"multithreaded":"singlethreaded"}`),this.config.debug&&!r&&pe("warning: wasm simd support is not enabled")}this.config.backend==="humangl"&&s9();try{await this.tf.setBackend(this.config.backend)}catch(r){pe("error: cannot set backend:",this.config.backend,r)}}if(this.tf.enableProdMode(),this.tf.getBackend()==="webgl"||this.tf.getBackend()==="humangl"){this.tf.ENV.set("CHECK_COMPUTATION_FOR_ERRORS",!1),this.tf.ENV.set("WEBGL_CPU_FORWARD",!0),this.tf.ENV.set("WEBGL_PACK_DEPTHWISECONV",!0),this.config.object.enabled||this.tf.ENV.set("WEBGL_FORCE_F16_TEXTURES",!0),typeof this.config.deallocate!="undefined"&&this.config.deallocate&&(pe("changing webgl: WEBGL_DELETE_TEXTURE_THRESHOLD:",!0),this.tf.ENV.set("WEBGL_DELETE_TEXTURE_THRESHOLD",0));let r=await this.tf.backend().getGPGPUContext().gl;this.config.debug&&pe(`gl version:${r.getParameter(r.VERSION)} renderer:${r.getParameter(r.RENDERER)}`)}await this.tf.ready(),this.performance.backend=Math.trunc(Je()-a)}});this.next=t=>tI(t||this.result);na(this,lf,async t=>{if(this.config.cacheSensitivity===0)return!1;let n=32,a=t.resizeBilinear([Math.trunc(t.shape[1]/n),Math.trunc(t.shape[2]/n)]),r=a.dataSync(),s=0;for(let u=0;u<r.length/3;u++)s+=r[3*u+2];a.dispose();let i=100*(Math.max(s,dn(this,Ji))/Math.min(s,dn(this,Ji))-1);wa(this,Ji,s);let o=i<Math.max(this.config.cacheSensitivity,dn(this,mu));return wa(this,mu,i>10*this.config.cacheSensitivity?0:i),o});na(this,uf,async()=>{let t=(r,s="application/octet-stream")=>fetch(`data:${s};base64,${r}`).then(i=>i.blob()),n,a;switch(this.config.warmup){case"face":n=await t(rf);break;case"full":n=await t(sf);break;default:n=null}if(n){let r=await createImageBitmap(n);a=await this.detect(r,this.config),r.close()}return a});na(this,df,async()=>new Promise(t=>{let n,a=0;switch(this.config.warmup){case"face":a=256,n="data:image/jpeg;base64,"+rf;break;case"full":case"body":a=1200,n="data:image/jpeg;base64,"+sf;break;default:n=null}let r=new Image;r.onload=async()=>{let s=typeof OffscreenCanvas!="undefined"?new OffscreenCanvas(a,a):document.createElement("canvas");s.width=r.naturalWidth,s.height=r.naturalHeight;let i=s.getContext("2d");i==null||i.drawImage(r,0,0);let o=await this.detect(s,this.config);t(o)},n?r.src=n:t(null)}));na(this,pf,async()=>{let t=r=>Buffer.from(r,"base64"),n;if(this.config.warmup==="face"&&(n=t(rf)),(this.config.warmup==="body"||this.config.warmup==="full")&&(n=t(sf)),!n)return null;let a;if(typeof void 0!="undefined"){let r=(void 0).decodeJpeg(n),s=r.expandDims(0);this.tf.dispose(r),a=await this.detect(s,this.config),this.tf.dispose(s)}else this.config.debug&&pe("Warmup tfjs-node not loaded");return a});this.config=Pn(C5,t||{}),this.tf=up,this.draw=f5,this.version=nI,this.state="idle",wa(this,fu,0),wa(this,yp,!1),wa(this,gp,!1),wa(this,Yi,!0),wa(this,mu,0),this.performance={backend:0,load:0,image:0,frames:0,cached:0,changed:0,total:0,draw:0},this.models={face:null,posenet:null,blazepose:null,efficientpose:null,movenet:null,handpose:null,age:null,gender:null,emotion:null,embedding:null,nanodet:null,centernet:null,faceres:null},this.image=n=>p5(n,this.config),this.classes={facemesh:g2,emotion:w2,faceres:T2,body:this.config.body.modelPath.includes("posenet")?O2:H2,hand:j2,nanodet:s5,centernet:d5},this.faceTriangulation=y9,this.faceUVMap=g9,this.sysinfo=E5(),wa(this,Ji,1)}similarity(t,n){return S2(t,n)}enhance(t){return N2(t)}match(t,n,a=0){return v9(t,n,a)}async load(t){this.state="load";let n=Je();t&&(this.config=Pn(this.config,t)),dn(this,Yi)&&(this.config.debug&&pe(`version: ${this.version}`),this.config.debug&&pe(`tfjs version: ${this.tf.version_core}`),this.config.debug&&pe("platform:",this.sysinfo.platform),this.config.debug&&pe("agent:",this.sysinfo.agent),await dn(this,xp).call(this,!0),this.tf.ENV.flags.IS_BROWSER&&(this.config.debug&&pe("configuration:",this.config),this.config.debug&&pe("tf flags:",this.tf.ENV.flags))),this.config.async?[this.models.face,this.models.emotion,this.models.handpose,this.models.posenet,this.models.blazepose,this.models.efficientpose,this.models.movenet,this.models.nanodet,this.models.centernet,this.models.faceres]=await Promise.all([this.models.face||(this.config.face.enabled?y2(this.config):null),this.models.emotion||(this.config.face.enabled&&this.config.face.emotion.enabled?v2(this.config):null),this.models.handpose||(this.config.hand.enabled?V2(this.config):null),this.models.posenet||(this.config.body.enabled&&this.config.body.modelPath.includes("posenet")?_2(this.config):null),this.models.blazepose||(this.config.body.enabled&&this.config.body.modelPath.includes("blazepose")?Q0(this.config):null),this.models.efficientpose||(this.config.body.enabled&&this.config.body.modelPath.includes("efficientpose")?j9(this.config):null),this.models.movenet||(this.config.body.enabled&&this.config.body.modelPath.includes("movenet")?Q2(this.config):null),this.models.nanodet||(this.config.object.enabled&&this.config.object.modelPath.includes("nanodet")?a5(this.config):null),this.models.centernet||(this.config.object.enabled&&this.config.object.modelPath.includes("centernet")?l5(this.config):null),this.models.faceres||(this.config.face.enabled&&this.config.face.description.enabled?I2(this.config):null)]):(this.config.face.enabled&&!this.models.face&&(this.models.face=await y2(this.config)),this.config.face.enabled&&this.config.face.emotion.enabled&&!this.models.emotion&&(this.models.emotion=await v2(this.config)),this.config.hand.enabled&&!this.models.handpose&&(this.models.handpose=await V2(this.config)),this.config.body.enabled&&!this.models.posenet&&this.config.body.modelPath.includes("posenet")&&(this.models.posenet=await _2(this.config)),this.config.body.enabled&&!this.models.blazepose&&this.config.body.modelPath.includes("blazepose")&&(this.models.blazepose=await Q0(this.config)),this.config.body.enabled&&!this.models.efficientpose&&this.config.body.modelPath.includes("efficientpose")&&(this.models.efficientpose=await Q0(this.config)),this.config.body.enabled&&!this.models.movenet&&this.config.body.modelPath.includes("movenet")&&(this.models.movenet=await Q2(this.config)),this.config.object.enabled&&!this.models.nanodet&&this.config.object.modelPath.includes("nanodet")&&(this.models.nanodet=await a5(this.config)),this.config.object.enabled&&!this.models.centernet&&this.config.object.modelPath.includes("centernet")&&(this.models.centernet=await l5(this.config)),this.config.face.enabled&&this.config.face.description.enabled&&!this.models.faceres&&(this.models.faceres=await I2(this.config))),dn(this,Yi)&&(this.config.debug&&pe("tf engine state:",this.tf.engine().state.numBytes,"bytes",this.tf.engine().state.numTensors,"tensors"),wa(this,Yi,!1));let a=Math.trunc(Je()-n);a>(this.performance.load||0)&&(this.performance.load=a)}async detect(t,n){return new Promise(async a=>{this.state="config";let r;this.config=Pn(this.config,n),this.state="check";let s=dn(this,of).call(this,t);s&&(pe(s,t),a({error:s}));let i=Je();await dn(this,xp).call(this),await this.load(),r=Je();let o=p5(t,this.config);if(!o||!o.tensor){pe("could not convert input to tensor"),a({error:"could not convert input to tensor"});return}this.performance.image=Math.trunc(Je()-r),this.analyze("Get Image:"),r=Je(),this.config.skipFrame=await dn(this,lf).call(this,o.tensor),this.performance.frames||(this.performance.frames=0),this.performance.cached||(this.performance.cached=0),this.performance.frames++,this.config.skipFrame&&this.performance.cached++,this.performance.changed=Math.trunc(Je()-r),this.analyze("Check Changed:");let u,l,d,p,c;this.config.async?(u=this.config.face.enabled?C2(this,o.tensor):[],this.performance.face&&delete this.performance.face):(this.state="run:face",r=Je(),u=this.config.face.enabled?await C2(this,o.tensor):[],c=Math.trunc(Je()-r),c>0&&(this.performance.face=c)),this.analyze("Start Body:"),this.config.async?(this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?D2(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?U2(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?l=this.config.body.enabled?K2(o.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(l=this.config.body.enabled?e5(o.tensor,this.config):[]),this.performance.body&&delete this.performance.body):(this.state="run:body",r=Je(),this.config.body.modelPath.includes("posenet")?l=this.config.body.enabled?await D2(o.tensor,this.config):[]:this.config.body.modelPath.includes("blazepose")?l=this.config.body.enabled?await U2(o.tensor,this.config):[]:this.config.body.modelPath.includes("efficientpose")?l=this.config.body.enabled?await K2(o.tensor,this.config):[]:this.config.body.modelPath.includes("movenet")&&(l=this.config.body.enabled?await e5(o.tensor,this.config):[]),c=Math.trunc(Je()-r),c>0&&(this.performance.body=c)),this.analyze("End Body:"),this.analyze("Start Hand:"),this.config.async?(d=this.config.hand.enabled?B2(o.tensor,this.config):[],this.performance.hand&&delete this.performance.hand):(this.state="run:hand",r=Je(),d=this.config.hand.enabled?await B2(o.tensor,this.config):[],c=Math.trunc(Je()-r),c>0&&(this.performance.hand=c)),this.analyze("End Hand:"),this.analyze("Start Object:"),this.config.async?(this.config.object.modelPath.includes("nanodet")?p=this.config.object.enabled?r5(o.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(p=this.config.object.enabled?u5(o.tensor,this.config):[]),this.performance.object&&delete this.performance.object):(this.state="run:object",r=Je(),this.config.object.modelPath.includes("nanodet")?p=this.config.object.enabled?await r5(o.tensor,this.config):[]:this.config.object.modelPath.includes("centernet")&&(p=this.config.object.enabled?await u5(o.tensor,this.config):[]),c=Math.trunc(Je()-r),c>0&&(this.performance.object=c)),this.analyze("End Object:"),this.config.async&&([u,l,d,p]=await Promise.all([u,l,d,p]));let h=[];this.config.gesture.enabled&&(r=Je(),h=[...H9(u),...U9(l),...q9(d),...G9(u)],this.config.async?this.performance.gesture&&delete this.performance.gesture:this.performance.gesture=Math.trunc(Je()-r)),this.performance.total=Math.trunc(Je()-i),this.state="idle",this.result={face:u,body:l,hand:d,gesture:h,object:p,performance:this.performance,canvas:o.canvas,timestamp:Date.now(),get persons(){var m;return eI(u,l,d,h,(m=o==null?void 0:o.tensor)==null?void 0:m.shape)}},Ie(o.tensor),a(this.result)})}async warmup(t){let n=Je();if(t&&(this.config=Pn(this.config,t)),!this.config.warmup||this.config.warmup==="none")return{error:"null"};let a;typeof createImageBitmap=="function"?a=await dn(this,uf).call(this):typeof Image!="undefined"?a=await dn(this,df).call(this):a=await dn(this,pf).call(this);let r=Je();return this.config.debug&&pe("Warmup",this.config.warmup,Math.round(r-n),"ms",a),a}};fu=new WeakMap,yp=new WeakMap,gp=new WeakMap,Yi=new WeakMap,Ji=new WeakMap,mu=new WeakMap,of=new WeakMap,xp=new WeakMap,lf=new WeakMap,uf=new WeakMap,df=new WeakMap,pf=new WeakMap;export{tue as Human,tue as default};
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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.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2018 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
*
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2019 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google Inc. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC
|
|
*
|
|
* Use of this source code is governed by an MIT-style
|
|
* license that can be found in the LICENSE file or at
|
|
* https://opensource.org/licenses/MIT.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2020 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the License);
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an AS IS BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/**
|
|
* @license
|
|
* Copyright 2021 Google LLC. All Rights Reserved.
|
|
* Licensed under the Apache License, Version 2.0 (the "License");
|
|
* you may not use this file except in compliance with the License.
|
|
* You may obtain a copy of the License at
|
|
*
|
|
* https://www.apache.org/licenses/LICENSE-2.0
|
|
*
|
|
* Unless required by applicable law or agreed to in writing, software
|
|
* distributed under the License is distributed on an "AS IS" BASIS,
|
|
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
* See the License for the specific language governing permissions and
|
|
* limitations under the License.
|
|
* =============================================================================
|
|
*/
|
|
/** @license See the LICENSE file. */
|
|
//# sourceMappingURL=human.esm.js.map
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